Enabling Low-Radiation-Dose and Low-Contrast-Volume Renal Artery CT Angiography with Dual-Layer Spectral Detector CT-Derived Virtual Monoenergetic Imaging and Model-Based Iterative Reconstruction

Enabling Low-Radiation-Dose and Low-Contrast-Volume Renal Artery CT Angiography with Dual-Layer Spectral Detector CT-Derived Virtual Monoenergetic Imaging and Model-Based Iterative Reconstruction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enabling Low-Radiation-Dose and Low-Contrast-Volume Renal Artery CT Angiography with Dual-Layer Spectral Detector CT-Derived Virtual Monoenergetic Imaging and Model-Based Iterative Reconstruction Chengle MA, Ruiquan Chen, YinChen Wu, Fan Zhang, Yuyang Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8485820/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives: To evaluate the efficacy of Virtual Monochromatic Imaging (VMI) and Model-based Iterative Reconstruction (IMR) derived from Dual-Layer Spectral Detector CT (DLCT) in reducing radiation dose and contrast agent volume during renal CT angiography (CTA). Methods: Ninety-four patients with suspected renal artery disease were prospectively enrolled and randomized into a full-dose group and a dual-low-dose group (n=47 each). Scans were performed on a DLCT system with Automatic Tube Current Modulation. The full-dose group received 1.5 ml/kg iohexol (300 mgi/ml) with a Dose Right Index (DRI) of 22 and hybrid iterative reconstruction(Group A).The dual-low-dose group received 0.75 ml/kg iohexol with a DRI of 10; images were reconstructed using VMI at 70, 60, 50, and 40 keV (Groups B–E) and IMR (Group F). Quantitative metrics (CT value, noise, SNR, CNR) and Qualitative image quality were compared among groups. Results: The dual-low-dose protocol achieved 65% and 50% reductions in effective dose and iodine load, respectively. Groups D (50 keV) and F (IMR) provided optimal reconstructions,followed by Group E. SNR and CNR in Groups D–F were significantly higher than in Group A ( P <0.05). While Group F exhibited the lowest noise ( P 0.05). Subjectively, vessel contrast, artifacts, and diagnostic confidence in Groups D and E matched or exceeded Group A. Diagnostic confidence in Group F was superior to Group E ( P 0.05). Conclusions: DLCT-based renal CTA utilizing 40–50 keV VMI or IMR reconstruction maintains or improves image quality while significantly reducing radiation and contrast burden compared to standard protocols with hybrid iterative reconstruction. Dual-Layer Spectral Detector CT Virtual Monochromatic Imaging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Computed Tomography Angiography (CTA) provides precise visualization of renal artery course, objective quantification of luminal stenosis severity [ 1 ] , and detailed assessment of pathological vascular supply patterns [ 2 ] . It has become an essential noninvasive imaging modality for diagnosing renal vascular hypertension, systemic vasculitis, renal tumors, and vascular malformations.Recent epidemiological data reveal a concerning upward trend in renovascular hypertension diagnoses among adults under 40 years of age, a demographic now representing nearly 30% of secondary hypertension cases [ 3 ] .Additionally, the global incidence of renal carcinoma is rising consistently; in 2022 alone, approximately 430,000 new cases were reported, accounting for roughly 2.2% of the worldwide cancer burden [ 4 ] . These epidemiological trends are expected to increase the clinical utilization of renal artery CTA, consequently increasing population exposure to ionizing radiation [ 5 ] . Furthermore, while administering sufficient iodinated contrast agent is crucial for optimizing lumen-to-soft tissue contrast and image quality, high contrast volumes elevate the risk of Contrast-Induced Nephropathy (CIN) and Post-Contrast Acute Kidney Injury (PC-AKI) [ 6 – 8 ] .Therefore, optimization of both radiation and iodinated contrast agent volume during renal artery CTA examinations will mitigate these associated risks, benefiting patients. In dual-layer spectral detector computed tomography (DLCT), virtual monoenergetic images (VMIs) at lower energy levels (< 70 keV) demonstrate progressively increased iodine attenuation as the energy level approaches the k-edge of iodine (33.2 keV) [ 9 , 10 ] . This significantly enhances contrast in iodine-containing tissues, thereby enabling reduction of iodinated contrast agent volume during contrast-enhanced CT scans [ 11 , 12 ] . Moreover, the usage of energy CT VMIs facilitates a decrease in patient radiation exposure. By acquiring perfectly aligned projection data from both the lower and upper detector layers and implementing a noise-reduction algorithm that leverages anti-correlated noise, DLCT maintains low noise levels across all VMI energy levels [ 13 ] . Conventional CT exams primarily reduce radiation exposure by utilizing techniques such as lowering tube voltage (kVp), modulating tube current (mA), and employing hybrid iterative reconstruction (HIR) [ 14 ] . However, reducing tube voltage significantly increases quantum noise and beam-hardening artifacts, thereby compromising diagnostic image quality. This is particularly pronounced during image reconstruction using conventional filtered back projection (FBP) for CTA in patients with higher body mass index [ 15 ] .While HIR allows for dose reduction while preserving diagnostic confidence [ 16 ] , the recently developed model-based iterative reconstruction (IMR) represents a more advanced paradigm. IMR integrates a physics-based system noise model to accurately simulate quantum noise statistics and scanner characteristics [ 17 , 18 ] . Clinical studies have validated that IMR offers superior noise reduction and diagnostic efficacy in angiographic applications compared to FBP and HIR [ 19 , 20 ] . While both VMI-reconstructed and IMR-reconstructed CTA examinations have individually demonstrated improved image quality, their combined application in low-radiation dose and low-contrast agent-volume ("dual-low") renal artery CTA has not been evaluated.This study aims to assess the diagnostic value of a dual-low protocol using DLCT-derived VMI and IMR reconstructions, using conventional full-dose renal artery CTA with HIR as the reference standard. MATERIALS AND METHODS Study population This single-center, randomized, prospective study was approved and supervised by the Ethics Committee of MRCTA,ECFAH of ***. Written informed consent was obtained from all participants. Between March 2022 and June 2024,106 consecutive patients with suspected renal disorders or renal artery stenosis were enrolled. Figure 1 shows a flowchart of enrolment, exclusion, and randomisation.Twelve patients were excluded based on the following criteria: contraindications to iodinated contrast media (n = 2), history of renal surgery/endovascular intervention (n = 6), and nondiagnostic image quality resulting from severe respiratory motion Artifact that prevented renal vascular assessment (n = 4).The remaining 94 patients were randomly assigned, using a random number table, to either the full-dose renal CTA group (n = 47) or the dual-low renal CTA group (n = 47). (Table 1 ) Table 1 Patient demographics,iodine load and radiation dose Item Group A (n = 47) Group B-F (n = 47) t value/x 2 value p value Gender(Male/Female) 28/19 27/20 0.044 1 b Age(year) 56.20 ± 13.08 46.77 ± 15.15 1.888 0.062 a Weight(kg) 65.70 ± 10.47 66.43 ± 12.68 0.302 0.764 a Height(cm) 163.50 ± 7.83 166.10 ± 7.44 1.621 0.109 a BMI(kg/m 2 ) 24.47 ± 2.85 24.00 ± 3.87 0.679 0.499 a Contrast agent volume(ml) 98.55 ± 15.71 49.85 ± 9.51 18.18 < 0.0001 a Iodine load(g) 29.59 ± 4.71 14.96 ± 2.86 18.22 < 0.0001 a CTDIvol(mGy) 10.68 ± 2.34 3.36 ± 0.99 18.99 < 0.0001 a DLP(mGy*cm) 265.40 ± 71.20 92.94 ± 31.13 15.21 < 0.0001 a ED(mSv) 3.98 ± 1.07 1.39 ± 0.47 15.22 < 0.0001 a Data are presented as the mean ± standard deviation or number. BMI, body mass index;CTDIvol, volume CT dose index; DLP, dose length product; ED, effective dose. a:t value.b:x 2 value. Protocols for Renal CT Angiography All renal artery CTA exams were conducted using a dual-layer spectral detector CT system (IQon, Philips Healthcare, Best, The Netherlands). Patients were positioned supine in a feet-first orientation with arms elevated above the head. Standard radiation protection measures were used, including lead shielding for the thyroid and pelvic areas. The protocol consisted of an initial non-contrast acquisition subsequently followed by contrast-enhanced imaging. Iodinated contrast agent (Iohexol 300 mgI/mL, GE Healthcare, Shanghai, China) was administered through an 18-gauge catheter placed in the median cubital vein using a dual-syringe power injector (EmpowerCTA+, Bracco Injenerring S.A., Switzerland) at a constant injection rate of 5 ml/s. The volume of the contrast agent was adjusted according to weight: the full-dose group received 450 mgI/kg (1.5 ml/kg), whereas the dual-low-dose group received 225 mgI/kg (0.75 ml/kg). The total contrast volume was divided by 5 ml/s to get the injection time. Then, a 30 ml saline chaser was given at the same rate. For arterial phase acquisition, automated bolus monitoring is performed using a circular region of interest (ROI; 100–150 mm²) placed in the abdominal aorta at the level of the renal hila. The scan began and was automatically triggered when the predefined enhancement threshold of 110 Hounsfield units (HU) was reached, with an additional 5-second delay to ensure optimal arterial phase visualisation of the renal vasculature. (Table 2 ) Table 2 Scanning parameters,Injection parameters and Reconstruction parameters for all groups Item Group A (n = 47) Group B-F (n = 47) Scanning parameters Tube voltage (kVp) 120 120 Tube current control Automatic exposure control Automatic exposure control Rotation time (s) 0.5 0.5 Pitch 1.2 1.2 Dose Right Index 22 10 Collimation width(mm) 64*0.625 64*0.625 Trigger technology Bolus-tracking Bolus-tracking Injection parameters Contrast agent volume(ml) 1.5ml/kg 0.75ml/kg Contrast agent concentration(mg/ml) 300mgI/ml 300mgI/ml Injection rate (ml/s) 5ml/s 5ml/s Iodine loading(g) 450mgI/kg*weight(kg)/1000 225mgI/kg*weight(kg)/1000 Reconstruction parameters Reconstruction algorithm iDose 4 (standard B) VMI 40 − 70Kev , an interval of 10 keV(standard B)/IMR༈Routine༉ Denoising level 3/6(iDose 4 ) 3/6(VMI)and 2/3༈IMR༉ Reconstruction thickness 1.0/0.8 1.0/0.8 Reconstruction matrix 512*512 512*512 Scanning parameters Standardized imaging parameters were applied across both groups, including a fixed tube voltage of 120 kVp, pitch of 1.2, gantry rotation time of 0.5 seconds, and detector collimation of 64*0.625 mm. The field of view was adjusted to individual patient anatomy (350–400 mm). Automatic Tube Current Modulation (ATCM; DoseRight system, Philips Healthcare) was utilized to manage radiation exposure based on the DoseRight Index (DRI). The DRI was set to 22 for the full-dose group and reduced to 10 for the dual-low-dose group, with tube current dynamically adjusted to maintain these prespecified dose levels. Parameters for Image Reconstruction: Full-dose group (Group A): Hybrid iterative reconstruction (iDose4, Standard B, Level 3/6, Philips Healthcare).Dual-low-dose groups (Groups B-E): Spectral-based holographic Imaging reconstruction (SBI, Spectral B, Level 3/6, Philips Healthcare) and virtual monoenergetic imaging (VMI) generated based on SBI data packets: Group B: VMI at 70 keV Group C: VMI at 60 keV Group D: VMI at 50 keV Group E: VMI at 40 keV. Dual-low-dose group (Group F): Model-based iterative reconstruction (IMR, Routine, Level 2/3, Philips Healthcare).All reconstructed images possessed a slice thickness of 1 mm, a slice spacing of 0.8 mm, a window width of 400 HU, and a window level of 60 HU. Quantitative analysis All CT images were transferred to a dedicated post-processing workstation (IntelliSpace Portal Version 10.1, Philips Healthcare) for quantitative analysis. Two board-certified abdominal radiologists (with 8 and 13 years of experience in abdominal CT interpretation, respectively) independently performed region-of-interest (ROI) measurements under standardised viewing conditions (width: 400 HU, level: 60 HU). For each subject (Groups A–F), circular or elliptical ROIs were systematically placed at the following anatomical locations: Abdominal aorta: transverse section at renal hilum level (ROI area: 20–30 mm²) Erector spinae muscles: Bilateral measurements at the maximal transverse section (ROI area: 30–50 mm²) Renal arteries: Main trunk, 1 cm distal to the origin (ROI encompassing full luminal diameter while carefully avoiding vessel walls) Measurements were carefully performed to exclude regions affected by calcifications, thrombuses, focal lesions, or beam-hardening Artifact. Each ROI was measured by two radiologists, and the average attenuation value (in Hounsfield units [HU]) and standard deviation (SD) were recorded. Noise was quantified as the SD of the measured CT attenuation values. To ensure geometric consistency across all study groups (A-F), the ROI copy-paste method was used, ensuring identical size, shape, and spatial positioning for comparative analyses. SNR abdominal aorta = CT abdominal aorta /SD abdominal aorta SNR renal artery = CT renal artery /SD renal artery CNR abdominal aorta = (CT abdominal aorta - CT erector spinae )/SD erector spinae CNR renal artery =(CT renal artery - CT erector spinae )/SD erector spinae Qualitative analysis Image evaluation was predominantly performed on axial reconstructions, supplemented by multiplanar maximum intensity projection (MIP) and volume rendering (VR) reformats. Two fellowship-trained abdominal radiologists (each with over 8 years of experience in abdominal imaging) independently reviewed the images on a dedicated workstation (IntelliSpace Portal, Version 10.1; Philips Healthcare).To ensure blinding, all patient demographic data and acquisition parameters were anonymized. Cases were presented in a randomized sequence with standardized anatomical alignment. To maintain consistency, window-level adjustments were not permitted during the review. Qualitative image quality was assessed using a validated 5-point Likert scale (Table 3). Images with a score of ≥ 3 were considered diagnostic, while those scoring ≤ 2 were classified as non-diagnostic. Table 3: The Qualitative Score Criteria of Image Quality Scores Vessel Contrast Vessel Artifact Diagnostic Confidence VR MIP 5 Excellent contrast No artifacts Fully diagnostic Many peripheral branches visualized 4 Good contrast Minor artifacts Good diagnostic Several peripheral branches visualized 3 Suboptimal contrast Moderate artifacts Diagnostic Proximal part of several peripheral branches visualized 2 Poor contrast Obvious artifacts Affecting diagnosis Peripheral branches not visualized 1 Cannot be displayed Severe artifacts Non-diagnostic Second-order branches not completely visualized Radiation dose management Radiation dose was quantified using three standardized metrics: volume CT dose index ( \(\:\text{CTD}{\text{I}}_{\text{vol}}\) , mGy), dose-length product (DLP, mGy·cm), and effective dose (ED, mSv). The effective dose was calculated using the formula \(\:\text{ED=DLP×k}\) , where \(\:\text{k}\) represents the organ-specific conversion factor for the abdomen (0.015 mSv/[mGy·cm]). Statistical analysis Statistical analyses were performed using SPSS Statistics (version 23.0, IBM Corp.) and GraphPad Prism (version 6.0, GraphPad Software Inc.). Continuous variables with normal distribution are expressed as mean ± standard deviation (SD), while non-normally distributed data are reported as median and interquartile range (Q1, Q3). Categorical variables were analysed using Fisher's exact test. Continuous demographic parameters (age, weight, height, BMI), contrast administration details (volume, iodine load), and radiation dose metrics ( \(\:\text{CTD}{\text{I}}_{\text{vol}}\) , DLP, ED) were compared using independent t-tests. Quantitative image quality metrics (CT value, SD, SNR, and CNR) were compared using one-way ANOVA; Dunnett’s T3 test was employed for post-hoc pairwise comparisons between groups. Qualitative image quality scores were compared using the Kruskal-Wallis H test, with post-hoc pairwise comparisons performed using the Mann-Whitney U-test with Bonferroni correction. Inter-reader agreement for qualitative evaluation was assessed using the Kappa statistic ( k ) .Agreement was categorized as poor ( k <0.40), moderate( k = 0.41—0.60), good ( k = 0.61—0.80) or excellent( k = 0.81—1.00). A P -value < 0.05 was considered statistically significant. Result Demographic and Clinical Characteristics A total of 94 patients (55 males, 39 females) successfully underwent renal CTA. Baseline demographic characteristics were well-balanced between the two groups( P >0.05; Table 1 ). Clinical indications in the full-dose group included hypertension ( \(\:\text{n=16}\) ), renal mass ( \(\:\text{n=16}\) ), adrenal mass ( \(\:\text{n=9}\) ), renal artery stenosis ( \(\:\text{n=5}\) ), and renal perforation ( \(\:\text{n=1}\) ). Indications in the dual-low-dose group comprised hypertension ( \(\:\text{n=8}\) ), renal mass ( \(\:\text{n=17}\) ), adrenal mass ( \(\:\text{n=17}\) ), and renal artery stenosis ( \(\:\text{n=5}\) ). Radiation Dose and Iodine Load Significant reductions in radiation dose metrics ( \(\:\text{CTD}{\text{I}}_{\text{vol}}\) , DLP, ED) and iodine load were achieved in the dual-low-dose group compared to the full-dose group( P <0.05). Specifically, the dual-low-dose protocol resulted in a 65% reduction in effective dose (ED) and a 50% decrease in total iodine burden (Table 1 ). Quantitative Image Quality Analysis Comparison between Full-Dose (Group A) and Dual-Low-Dose Groups (Groups B–F) Quantitative image quality metrics are summarized in Table 4 and Fig. 6 – 7 Significant inter-group differences in CT attenuation were observed in the abdominal aorta and bilateral renal arteries (all P <0.0001). Groups C–E (40–60 keV) exhibited significantly higher attenuation compared to Group A (all P 0.05). Group F (IMR) demonstrated superior overall performance compared to Group A, characterized by significantly higher SNR and CNR values and reduced image noise (SD) (all P <0.05). While Groups D–E (40–50 keV) achieved higher SNR and CNR than Group A, they also exhibited increased image noise. Group B (70 keV) yielded the poorest performance, with significantly lower SNR and CNR compared to Group A across all assessed vessels (all P <0.05). Table 4 Results of Quantitative Image Quality Analysis among all groups Item Group A iDose 4 Group B 70Kev Group C 60Kev Group D 50Kev Group E 40Kev Group F IMR F value p value CT value Abdominal aorta 369.89 ± 49.47 375.30 ± 52.55 515.37 ± 75.08 748.77 ± 112.43 1147.12 ± 176.68 379.47 ± 54.92 470.3 < 0.000 1 Right renal artery 348.01 ± 50.24 354.93 ± 52.44 481.63 ± 72.76 692.80 ± 107.80 1052.43 ± 168.04 369.08 ± 59.16 406.9 < 0.000 1 Left renal artery 343.58 ± 50.61 352.83 ± 57.43 477.26 ± 81.01 685.41 ± 120.04 1039.33 ± 187.33 362.97 ± 57.80 327.8 < 0.000 1 SD value Abdominal aorta 24.24 ± 3.01 28.16 ± 3.72 28.51 ± 3.81 29.37 ± 4.00 31.54 ± 4.47 12.07 ± 1.14 189.2 < 0.000 1 Right renal artery 19.11 ± 2.22 22.28 ± 3.74 22.41 ± 3.71 22.94 ± 3.56 24.50 ± 3.48 11.04 ± 1.60 112.9 < 0.000 1 Left renal artery 19.01 ± 2.12 21.67 ± 3.80 21.91 ± 3.79 22.50 ± 3.87 24.04 ± 4.31 11.11 ± 1.49 89.23 < 0.000 1 SNR Abdominal aorta 15.53 ± 3.02 13.63 ± 2.93 18.48 ± 4.08 26.07 ± 5.76 37.20 ± 8.25 31.52 ± 5.83 147.8 < 0.000 1 Right renal artery 18.47 ± 3.52 16.39 ± 3.72 22.11 ± 5.10 30.99 ± 7.17 43.96 ± 10.25 33.74 ± 5.24 132.7 < 0.000 1 Left renal artery 18.38 ± 3.93 16.81 ± 4.08 22.47 ± 5.50 31.37 ± 7.61 44.61 ± 11.02 33.04 ± 5.53 116.2 < 0.000 1 CNR Abdominal aorta 17.13 ± 3.70 14.44 ± 3.34 20.42 ± 4.70 29.79 ± 6.82 43.94 ± 10.17 32.13 ± 6.50 145.4 < 0.000 1 Right renal artery 15.98 ± 3.75 13.56 ± 3.38 18.96 ± 4.67 27.45 ± 6.72 40.22 ± 9.91 31.37 ± 6.93 124.3 < 0.000 1 Left renal artery 15.74 ± 3.77 13.47 ± 3.52 18.78 ± 4.95 27.16 ± 7.13 39.73 ± 10.51 30.75 ± 6.68 111.6 < 0.000 1 Data are presented as the mean ± standard deviation or number. CT,computed tomography; SD, standard deviation; SNR, signal-to-noise ratio; CNR, contrast-to-noise ratio. Comparison within Dual-Low-Dose Subgroups (Groups D, E, and F) Within the dual-low-dose groups, CT attenuation values increased as VMI energy levels decreased (Groups D-E), differing significantly from Group F (IMR) (all P <0.05). Group E (40 keV) achieved the highest SNR and CNR, significantly surpassing Group F( P <0.05). However, Group F (IMR) exhibited the lowest image noise, which was significantly lower than that of Groups D–E (all P 0.05). Qualitative Image Quality Analysis Comparison between Full-Dose (Group A) and Dual-Low-Dose Groups (Groups B–F) Inter-reader agreement for qualitative assessments ranged from good to excellent ( k values: 0.863 for vessel contrast, 0.831 for artifacts, 0.844 for diagnostic confidence, 0.757 for VR, and 0.730 for MIP). In comparative analysis, Group E (40 keV) showed no significant differences in diagnostic confidence or vessel artifact scores compared to Group A( P >0.05). Group D (50 keV) demonstrated superior diagnostic confidence, vessel contrast, VR, and MIP quality compared to Group A( P <0.05). Conversely, Groups B–C (60–70 keV) scored significantly lower in vessel artifact reduction and diagnostic confidence( P 0.05), it achieved significantly higher ratings for diagnostic confidence, VR, and MIP quality (all P <0.05) (Table 5 and Fig. 8 ). Comparison within Dual-Low-Dose Subgroups (Groups D, E, and F) Within the dual-low-dose groups, Group F (IMR) and Group D (50 keV) achieved the highest scores for diagnostic confidence and artifact reduction, with no significant difference between them (all P >0.05). Both groups scored significantly higher in these categories than Group E (40 keV) ( P <0.05). For vessel contrast, VR, and MIP assessment, Group E (40 keV) scored significantly higher than Group F ( P 0.05). Table 5 :Result of qualitative observation indices of image quality among all groups Item Group A iDose 4 Group B 70Kev Group C 60Kev Group D 50Kev Group E 40Kev Group F IMR H value P value Vessel Contrast 3(3,4) 3(2,3) 3(3,4) 4(4,4) 5(4.75,5) 3(3,4) 357.1 < 0.000 1 Vessel Artifact 4(4,4) 3(2,3) 3(3,4) 4(4,4) 3(4,4) 4(4,4) 327.2 < 0.000 1 Diagnostic Confidence 4(4,4) 3(2,3) 3(3,4) 4(4,4) 4(4,4) 4(4,5) 358.3 < 0.000 1 VR 3.5(3,4) 2(2,3) 3(3,4) 4(4,4) 5(5,5) 4(4,4) 379.9 < 0.000 1 MIP 3.5(3,4) 2(2,3) 3(3,4) 4(4,4) 5(5,5) 4(4,5) 380.9 < 0.000 1 Qualitative image quality score for Conventional-dose group (Group A) and Dual-low-dose groups B-F(40/50/60/70 keV and IMR) Discussion This study systematically evaluated the image quality of renal CTA using DLCT-derived VMI and IMR with decreased contrast and radiation doses. Our primary findings indicate that both low-energy VMI (40–50 keV) and IMR significantly improve diagnostic interpretability while achieving superior SNR and CNR. Notably, these improvements were maintained despite a substantial reduction in contrast media volume (approximately 67%) and radiation exposure (approximately 33%) compared to conventional HIR at standard doses. The efficacy of the dual-low-dose protocol is largely attributable to the energy-dependent attenuation properties of iodine. As photon energy approaches the iodine K-edge absorption threshold (33.2 keV), the photoelectric absorption cross-section increases substantially. Consequently, VMI at lower energy levels (e.g., 40–70 keV) significantly enhances attenuation differences between vessels, parenchymal organs, and pathological lesions [ 21 – 23 ] . Our analysis confirmed that utilizing a 50% reduced iodine load within the 40–50 keV range yielded higher arterial attenuation values ( P < 0.05), as well as enhanced CNR and SNR, compared to full-dose groups. Noise management remains critical when evaluating delicate anatomical structures such as the renal arteries. In this study, we employed the DRI system for real-time dose modulation [ 24 ] , whereby lower DRI values reduce tube current and consequently radiation dose, albeit at the expense of increased image noise.Our quantitative analysis revealed that although the dual-low-dose protocol significantly increased image noise in VMI reconstructions compared to conventional acquisitions, this elevation was largely offset by a corresponding enhancement in vessel-to-background contrast ratios [ 25 ] .These findings were corroborated by qualitative image quality assessments, with 40–50 keV VMI datasets achieving comparable diagnostic confidence scores.Furthermore,advanced three-dimensional post-processing techniques, including VR and MIP enhanced the delineation of third- and fourth-order arterial branches in the VMI groups(Fig. 3 – 4 ). The technical architecture of DLCT confers distinct advantages in noise management.Unlike conventional DECT, DLCT employs true simultaneous acquisition, thereby eliminating temporal mismatches and enabling projection-based decomposition that minimizes beam-hardening artifacts.More importantly, its capacity to identify and suppress anticorrelated noise in raw material basis datasets provides superior noise reduction while preserving signal integrity [ 26 , 27 ] . D'Angelo et al. reported comparable findings in coronary artery imaging, demonstrating that DLCT-derived VMI at 40 keV with standard contrast dose achieved optimal SNR and CNR without an additional noise penalty [ 25 ] . In contrast, fast kVp-switching scanners operate at reduced photon output when generating low-energy data, and the absence of automated tube current modulation in this technical configuration may result in elevated image noise [ 28 ] .Similarly, dual-source dual-energy CT (dsDECT), which performs material decomposition in the image domain rather than the projection domain, is also susceptible to increased noise levels [ 28 ] . In addition to spectral reconstructions, the advancement of iterative reconstruction techniques has been pivotal for low-dose protocols. Compared to HIR, IMR utilizes advanced modeling to reduce image noise and improve spatial resolution, a capability previously shown to enhance peripheral renal artery visualization while reducing noise by approximately 52% compared to filtered back projection [ 29 ] . In the present study, the dual-low-dose groups achieved radiation exposure levels only one-third that of the conventional-dose group, with significantly reduced CTDIvol (3.36 ± 0.99 mGy) and effective dose (1.39 ± 0.47 mSv).These effective dose values are lower than those reported in comparable studies [ 12 , 29 ] . However, IMR is not without limitations; the technique can produce characteristic "waxy" or "plastic" textural artifacts, which may affect diagnostic confidence [ 30 ] . To mitigate this, we utilized a "routine" kernel at level 2/3. This setting offered an optimal compromise, yielding the lowest noise values among all reconstructions while minimizing artificial textures and improving the CNR and SNR of the abdominal aorta and renal arteries. Consequently, IMR demonstrated significantly higher CNR when delineating smaller vessels, such as branches of the mesenteric artery, compared to VMI and HIR. This superior visualization is likely due to IMR’s ability to reduce noise spectral density while preserving the modulation transfer function. In this study, the conventional-dose group involved an iodine burden of 29.59 ± 4.71 g, which was methodically reduced by approximately 50% to 14.96 ± 2.86 g in the dual-low-dose groups. Although Group E (40 keV) demonstrated the highest arterial attenuation, SNR, and CNR, these quantitative improvements did not translate to increased diagnostic confidence. Conversely, Group E showed significantly lower diagnostic confidence scores compared to Group F (IMR) ( P < 0.05). This discrepancy arises from the proximity of the 40 keV level to the iodine K-edge, which causes an abrupt rise in CT values. This excessive attenuation can result in "blooming" artifacts that obscure vascular details and hinder the precise evaluation of stenotic lesions or vessel lumens [ 21 ] . In contrast, both Group D (50 keV) and Group F (IMR) achieved the highest diagnostic confidence scores ( P > 0.05), coupled with favorable SNR and CNR. Therefore, our data suggests that 50 keV represents the optimal trade-off between contrast enhancement and image noise for vascular imaging.Some investigators have proposed windowing adjustments to mitigate pseudo-stenosis at lower energies [ 31 ] ,presenting a promising direction for our future research.meanwhile,the application of photon-counting CT and deep learning reconstruction algorithms holds significant potential for further improving image quality in future research [ 32 ] . This study has several limitations. First, the sample size was constrained by the single-center design, which may introduce selection bias and limit the generalizability of the findings. Second, the study cohort exclusively included patients with a body mass index (BMI) below 25 kg/m². As obese patients often present distinct imaging challenges regarding noise and attenuation, the applicability of these low-dose protocols to populations with higher BMI requires further validation. Conclusions In conclusion, renal artery CTA with low radiation dose and Contrast agent can provide equivalent or improved renal artery image quality using 40–50 keV VMI or IMR reconstructions on SDCT. These protocol optimizations mitigate population exposure risks of ionizing radiation and contrast-induced complications. Declarations Author Contribution literature research, experimental design and manuscript editing: Chengle Ma.statistical analysis,graph plotting and specimen collection: Ruiquan Chen and Yinchen Wu.designed the study and supervised the research implementation:Fan Zhang and Yuyang Zhang. References Fraioli F, Catalano C, Bertoletti L, et al. Multidetector-row CT angiography of renal artery stenosis in 50 consecutive patients: prospective interobserver comparison with DSA[J]. Radiol Med, 2006,111(3):459-468. Luo S, Lin W, Wu J, et al. Quantitative Measurement on Contrast-Enhanced CT Distinguishes Small Clear Cell Renal Cell Carcinoma From Benign Renal Tumors: A Multicenter Study[J]. Acad Radiol, 2024,31(4):1460-1471. de Freminville J, Gardini M, Cremer A, et al. Prevalence and Risk Factors for Secondary Hypertension in Young Adults[J]. Hypertension, 2024,81(11):2340-2349. Sung H, Ferlay J, Siegel R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries[J]. CA Cancer J Clin, 2021,71(3):209-249. Smith-Bindman R, Chu P W, Azman Firdaus H, et al. Projected Lifetime Cancer Risks From Current Computed Tomography Imaging[J]. JAMA Intern Med, 2025. Davenport M S, Cohan R H, Khalatbari S, et al. The challenges in assessing contrast-induced nephropathy: where are we now?[J]. AJR Am J Roentgenol, 2014,202(4):784-789. McDonald J S, McDonald R J, Carter R E, et al. Risk of intravenous contrast material-mediated acute kidney injury: a propensity score-matched study stratified by baseline-estimated glomerular filtration rate[J]. Radiology, 2014,271(1):65-73. McDonald J S, McDonald R J, Comin J, et al. Frequency of acute kidney injury following intravenous contrast medium administration: a systematic review and meta-analysis[J]. Radiology, 2013,267(1):119-128. McCollough C H, Leng S, Yu L, et al. Dual- and Multi-Energy CT: Principles, Technical Approaches, and Clinical Applications[J]. Radiology, 2015,276(3):637-653. Rajiah P, Parakh A, Kay F, et al. Update on Multienergy CT: Physics, Principles, and Applications[J]. Radiographics, 2020,40(5):1284-1308. Zhou X, Cui M, Liu Y, et al. Low Dose Iodinated Contrast Material and Radiation for Virtual Monochromatic Imaging in Craniocervical Dual-Layer Spectral Detector Computed Tomography Angiography: A Prospective and Randomized Study[J]. Acad Radiol, 2024,31(6):2501-2510. Morita S, Ogawa Y, Yamamoto T, et al. Image quality of early postoperative CT angiography with reduced contrast material and radiation dose using model-based iterative reconstruction for screening of renal pseudoaneurysms after partial nephrectomy[J]. Eur J Radiol, 2020,124:108853. Kalisz K, Rassouli N, Dhanantwari A, et al. Noise characteristics of virtual monoenergetic images from a novel detector-based spectral CT scanner[J]. Eur J Radiol, 2018,98:118-125. Chen Y, Liu Z, Li M, et al. Reducing both radiation and contrast doses in coronary CT angiography in lean patients on a 16-cm wide-detector CT using 70 kVp and ASiR-V algorithm, in comparison with the conventional 100-kVp protocol[J]. Eur Radiol, 2019,29(6):3036-3043. Singh S, Kalra M K, Hsieh J, et al. Abdominal CT: comparison of adaptive statistical iterative and filtered back projection reconstruction techniques[J]. Radiology, 2010,257(2):373-383. Ren Z, Zhang X, Hu Z, et al. Application of Adaptive Statistical Iterative Reconstruction-V With Combination of 80 kV for Reducing Radiation Dose and Improving Image Quality in Renal Computed Tomography Angiography for Slim Patients[J]. Acad Radiol, 2019,26(11):e324-e332. Stiller W. Basics of iterative reconstruction methods in computed tomography: A vendor-independent overview[J]. Eur J Radiol, 2018,109:147-154. Deak Z, Grimm J M, Treitl M, et al. Filtered back projection, adaptive statistical iterative reconstruction, and a model-based iterative reconstruction in abdominal CT: an experimental clinical study[J]. Radiology, 2013,266(1):197-206. Qian W, Zhou D, Jiang Y, et al. Ultra-low radiation dose CT angiography of the lower extremity using the iterative model reconstruction (IMR) algorithm[J]. Clin Radiol, 2018,73(11):913-985. Hajdu S D, Daniel R T, Meuli R A, et al. Impact of model-based iterative reconstruction (MBIR) on image quality in cerebral CT angiography before and after intracranial aneurysm treatment[J]. Eur J Radiol, 2018,102:109-114. Huang X, Gao S, Ma Y, et al. The optimal monoenergetic spectral image level of coronary computed tomography (CT) angiography on a dual-layer spectral detector CT with half-dose contrast media[J]. Quant Imaging Med Surg, 2020,10(3):592-603. Al-Baldawi Y, Grosse Hokamp N, Haneder S, et al. Virtual mono-energetic images and iterative image reconstruction: abdominal vessel imaging in the era of spectral detector CT[J]. Clin Radiol, 2020,75(8):641-649. Ren H, Zhen Y, Gong Z, et al. Feasibility of low-dose contrast media in run-off CT angiography on dual-layer spectral detector CT[J]. Quant Imaging Med Surg, 2021,11(5):1796-1804. Fillon M, Si-Mohamed S, Coulon P, et al. Reduction of patient radiation dose with a new organ based dose modulation technique for thoraco-abdominopelvic computed tomography (CT) (Liver dose right index)[J]. Diagn Interv Imaging, 2018,99(7-8):483-492. D'Angelo T, Lanzafame L R M, Micari A, et al. Improved Coronary Artery Visualization Using Virtual Monoenergetic Imaging from Dual-Layer Spectral Detector CT Angiography[J]. Diagnostics (Basel), 2023,13(16). Rassouli N, Etesami M, Dhanantwari A, et al. Detector-based spectral CT with a novel dual-layer technology: principles and applications[J]. Insights Imaging, 2017,8(6):589-598. Ozguner O, Dhanantwari A, Halliburton S, et al. Objective image characterization of a spectral CT scanner with dual-layer detector[J]. Phys Med Biol, 2018,63(2):25027. Agostini A, Borgheresi A, Mari A, et al. Dual-energy CT: theoretical principles and clinical applications[J]. Radiol Med, 2019,124(12):1281-1295. Wu R, Hori M, Onishi H, et al. Effects of reconstruction technique on the quality of abdominal CT angiography: A comparison between forward projected model-based iterative reconstruction solution (FIRST) and conventional reconstruction methods[J]. Eur J Radiol, 2018,106:100-105. Laurent G, Villani N, Hossu G, et al. Full model-based iterative reconstruction (MBIR) in abdominal CT increases objective image quality, but decreases subjective acceptance[J]. Eur Radiol, 2019,29(8):4016-4025. Iuga A, Doerner J, Siedek F, et al. Computed tomography pulmonary angiograms using a novel dual-layer spectral detector: Adjusted window settings are essential for diagnostic image quality[J]. Medicine (Baltimore), 2019,98(33):e16606. Wrazidlo R, Walder L, Estler A, et al. Radiation Dose Reduction in Contrast-Enhanced Abdominal CT: Comparison of Photon-Counting Detector CT with 2nd Generation Dual-Source Dual-Energy CT in an oncologic cohort[J]. Acad Radiol, 2023,30(5):855-862. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8485820","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581828499,"identity":"27f42462-e296-4562-8507-4f108de4dfa4","order_by":0,"name":"Chengle MA","email":"","orcid":"","institution":"Department of Radiology, the First Affiliated Hospital, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chengle","middleName":"","lastName":"MA","suffix":""},{"id":581828501,"identity":"cbffc3f3-f15d-4800-b0bd-0b0bd8015b88","order_by":1,"name":"Ruiquan Chen","email":"","orcid":"","institution":"Department of Radiology, the First Affiliated Hospital, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruiquan","middleName":"","lastName":"Chen","suffix":""},{"id":581828502,"identity":"a5278f8c-77d7-42d4-b392-ad995520a748","order_by":2,"name":"YinChen Wu","email":"","orcid":"","institution":"Department of Radiology, the First Affiliated Hospital, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"YinChen","middleName":"","lastName":"Wu","suffix":""},{"id":581828503,"identity":"a96f2354-d149-4c46-aa8d-41428a8e82a5","order_by":3,"name":"Fan Zhang","email":"","orcid":"","institution":"Department of Radiology, the First Affiliated Hospital, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Zhang","suffix":""},{"id":581828515,"identity":"484c6d8e-a936-4c42-94b3-fb978334ce72","order_by":4,"name":"Yuyang Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYNCCCjlmMM1DvJYzxqRqYWwzZiBei8HxHrOHX+cZsOu2NzA+eNvGIG9OUMuZM+bGstsMmM3OHGA2nNvGYLizgZCWGzlm0pLb/jCb3Uhgk+ZtY0gwOECUljlAW+4/YP9NtBbJjw1ALTcY2JiJ0iJ55liZNMMxkF8SmyXnnJMw3EBIC9/x5m2SP2oMks2OHz744U2ZjTxBWxSACpiB0ZEMjJ0GIF+CgHogkAeqY/zBwGBHWOkoGAWjYBSMWAAAw089yLKCljMAAAAASUVORK5CYII=","orcid":"","institution":"Department of Radiology, the First Affiliated Hospital, Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yuyang","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-12-31 04:38:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8485820/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8485820/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101507741,"identity":"65233d2f-9812-4c27-98bf-01cafe4e5a65","added_by":"auto","created_at":"2026-01-30 14:42:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":128030,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8485820/v1/3c1c1d8324e019b75858a03a.png"},{"id":101507689,"identity":"e1809a83-e78b-4c81-922d-ad9a62040f5d","added_by":"auto","created_at":"2026-01-30 14:42:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":740136,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8485820/v1/0daf7606328f8a61b74e97ce.png"},{"id":101507679,"identity":"78aeff52-639b-4097-a512-e276ddf0626c","added_by":"auto","created_at":"2026-01-30 14:42:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":610577,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8485820/v1/2638cb6eaf75c32060dd8ffb.png"},{"id":101507852,"identity":"c66a162c-6c9b-4616-90cc-9d0ea28fb259","added_by":"auto","created_at":"2026-01-30 14:43:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":471752,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8485820/v1/3ec9e1ca30aa5c71f04f33ff.png"},{"id":101507745,"identity":"28b6b394-bf7c-4199-be81-8b40fda741b4","added_by":"auto","created_at":"2026-01-30 14:42:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":466681,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8485820/v1/a6f41d1e41d4f4546efb9065.png"},{"id":101507755,"identity":"326f4764-d508-4dce-8ea3-bec5235601c0","added_by":"auto","created_at":"2026-01-30 14:42:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":149604,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8485820/v1/24ae4b7ec1a8b1047060e141.png"},{"id":101507873,"identity":"8a56ff5e-23f0-4dec-9823-a8534d501331","added_by":"auto","created_at":"2026-01-30 14:43:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":158088,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8485820/v1/84500cc347c0946fea534eaa.png"},{"id":101507736,"identity":"5d6f4c4f-9fe0-4472-9833-5d1b3ebf2512","added_by":"auto","created_at":"2026-01-30 14:42:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":135754,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8485820/v1/1e6e2de186f18b5ed67b5dc1.png"},{"id":103232383,"identity":"712fba81-c135-4e35-b823-2ebe74d8f97e","added_by":"auto","created_at":"2026-02-23 12:27:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4109147,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8485820/v1/4fed4fe1-3c14-4100-bbac-101c33811a94.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enabling Low-Radiation-Dose and Low-Contrast-Volume Renal Artery CT Angiography with Dual-Layer Spectral Detector CT-Derived Virtual Monoenergetic Imaging and Model-Based Iterative Reconstruction","fulltext":[{"header":"Introduction","content":"\u003cp\u003eComputed Tomography Angiography (CTA) provides precise visualization of renal artery course, objective quantification of luminal stenosis severity\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, and detailed assessment of pathological vascular supply patterns\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. It has become an essential noninvasive imaging modality for diagnosing renal vascular hypertension, systemic vasculitis, renal tumors, and vascular malformations.Recent epidemiological data reveal a concerning upward trend in renovascular hypertension diagnoses among adults under 40 years of age, a demographic now representing nearly 30% of secondary hypertension cases\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.Additionally, the global incidence of renal carcinoma is rising consistently; in 2022 alone, approximately 430,000 new cases were reported, accounting for roughly 2.2% of the worldwide cancer burden\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThese epidemiological trends are expected to increase the clinical utilization of renal artery CTA, consequently increasing population exposure to ionizing radiation\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Furthermore, while administering sufficient iodinated contrast agent is crucial for optimizing lumen-to-soft tissue contrast and image quality, high contrast volumes elevate the risk of Contrast-Induced Nephropathy (CIN) and Post-Contrast Acute Kidney Injury (PC-AKI)\u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.Therefore, optimization of both radiation and iodinated contrast agent volume during renal artery CTA examinations will mitigate these associated risks, benefiting patients.\u003c/p\u003e \u003cp\u003eIn dual-layer spectral detector computed tomography (DLCT), virtual monoenergetic images (VMIs) at lower energy levels (\u0026lt;\u0026thinsp;70 keV) demonstrate progressively increased iodine attenuation as the energy level approaches the k-edge of iodine (33.2 keV)\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. This significantly enhances contrast in iodine-containing tissues, thereby enabling reduction of iodinated contrast agent volume during contrast-enhanced CT scans\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Moreover, the usage of energy CT VMIs facilitates a decrease in patient radiation exposure. By acquiring perfectly aligned projection data from both the lower and upper detector layers and implementing a noise-reduction algorithm that leverages anti-correlated noise, DLCT maintains low noise levels across all VMI energy levels\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConventional CT exams primarily reduce radiation exposure by utilizing techniques such as lowering tube voltage (kVp), modulating tube current (mA), and employing hybrid iterative reconstruction (HIR)\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. However, reducing tube voltage significantly increases quantum noise and beam-hardening artifacts, thereby compromising diagnostic image quality. This is particularly pronounced during image reconstruction using conventional filtered back projection (FBP) for CTA in patients with higher body mass index\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.While HIR allows for dose reduction while preserving diagnostic confidence\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, the recently developed model-based iterative reconstruction (IMR) represents a more advanced paradigm. IMR integrates a physics-based system noise model to accurately simulate quantum noise statistics and scanner characteristics\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Clinical studies have validated that IMR offers superior noise reduction and diagnostic efficacy in angiographic applications compared to FBP and HIR\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile both VMI-reconstructed and IMR-reconstructed CTA examinations have individually demonstrated improved image quality, their combined application in low-radiation dose and low-contrast agent-volume (\"dual-low\") renal artery CTA has not been evaluated.This study aims to assess the diagnostic value of a dual-low protocol using DLCT-derived VMI and IMR reconstructions, using conventional full-dose renal artery CTA with HIR as the reference standard.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy population\u003c/h2\u003e\n \u003cp\u003eThis single-center, randomized, prospective study was approved and supervised by the Ethics Committee of MRCTA,ECFAH of ***. Written informed consent was obtained from all participants. Between March 2022 and June 2024,106 consecutive patients with suspected renal disorders or renal artery stenosis were enrolled. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows a flowchart of enrolment, exclusion, and randomisation.Twelve patients were excluded based on the following criteria: contraindications to iodinated contrast media (n\u0026thinsp;=\u0026thinsp;2), history of renal surgery/endovascular intervention (n\u0026thinsp;=\u0026thinsp;6), and nondiagnostic image quality resulting from severe respiratory motion Artifact that prevented renal vascular assessment (n\u0026thinsp;=\u0026thinsp;4).The remaining 94 patients were randomly assigned, using a random number table, to either the full-dose renal CTA group (n\u0026thinsp;=\u0026thinsp;47) or the dual-low renal CTA group (n\u0026thinsp;=\u0026thinsp;47). (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePatient demographics,iodine load and radiation dose\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup A\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;47)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup B-F\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;47)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e value/x\u003csup\u003e2\u003c/sup\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender(Male/Female)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28/19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27/20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge(year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.20\u0026thinsp;\u0026plusmn;\u0026thinsp;13.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.77\u0026thinsp;\u0026plusmn;\u0026thinsp;15.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight(kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.70\u0026thinsp;\u0026plusmn;\u0026thinsp;10.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.43\u0026thinsp;\u0026plusmn;\u0026thinsp;12.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.764\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeight(cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163.50\u0026thinsp;\u0026plusmn;\u0026thinsp;7.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166.10\u0026thinsp;\u0026plusmn;\u0026thinsp;7.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.109\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI(kg/m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.47\u0026thinsp;\u0026plusmn;\u0026thinsp;2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.00\u0026thinsp;\u0026plusmn;\u0026thinsp;3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.499\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eContrast agent volume(ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.55\u0026thinsp;\u0026plusmn;\u0026thinsp;15.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.85\u0026thinsp;\u0026plusmn;\u0026thinsp;9.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIodine load(g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.59\u0026thinsp;\u0026plusmn;\u0026thinsp;4.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.96\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCTDIvol(mGy)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.68\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDLP(mGy*cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e265.40\u0026thinsp;\u0026plusmn;\u0026thinsp;71.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.94\u0026thinsp;\u0026plusmn;\u0026thinsp;31.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eED(mSv)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eData are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or number.\u003c/p\u003e\n \u003cp\u003eBMI, body mass index;CTDIvol, volume CT dose index; DLP, dose length product; ED, effective dose.\u003c/p\u003e\n \u003cp\u003ea:t value.b:x\u003csup\u003e2\u003c/sup\u003e value.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eProtocols for Renal CT Angiography\u003c/h3\u003e\n\u003cp\u003eAll renal artery CTA exams were conducted using a dual-layer spectral detector CT system (IQon, Philips Healthcare, Best, The Netherlands). Patients were positioned supine in a feet-first orientation with arms elevated above the head. Standard radiation protection measures were used, including lead shielding for the thyroid and pelvic areas. The protocol consisted of an initial non-contrast acquisition subsequently followed by contrast-enhanced imaging. Iodinated contrast agent (Iohexol 300 mgI/mL, GE Healthcare, Shanghai, China) was administered through an 18-gauge catheter placed in the median cubital vein using a dual-syringe power injector (EmpowerCTA+, Bracco Injenerring S.A., Switzerland) at a constant injection rate of 5 ml/s. The volume of the contrast agent was adjusted according to weight: the full-dose group received 450 mgI/kg (1.5 ml/kg), whereas the dual-low-dose group received 225 mgI/kg (0.75 ml/kg). The total contrast volume was divided by 5 ml/s to get the injection time. Then, a 30 ml saline chaser was given at the same rate. For arterial phase acquisition, automated bolus monitoring is performed using a circular region of interest (ROI; 100\u0026ndash;150 mm\u0026sup2;) placed in the abdominal aorta at the level of the renal hila. The scan began and was automatically triggered when the predefined enhancement threshold of 110 Hounsfield units (HU) was reached, with an additional 5-second delay to ensure optimal arterial phase visualisation of the renal vasculature. (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eScanning parameters,Injection parameters and Reconstruction parameters for all groups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup A\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;47)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup B-F\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;47)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eScanning parameters\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTube voltage (kVp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTube current control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutomatic exposure\u003c/p\u003e\n \u003cp\u003econtrol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutomatic exposure\u003c/p\u003e\n \u003cp\u003econtrol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRotation time (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePitch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDose Right Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollimation width(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64*0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64*0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrigger technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBolus-tracking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBolus-tracking\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eInjection parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContrast agent volume(ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5ml/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75ml/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContrast agent concentration(mg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300mgI/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300mgI/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInjection rate (ml/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5ml/s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5ml/s\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIodine loading(g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e450mgI/kg*weight(kg)/1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e225mgI/kg*weight(kg)/1000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eReconstruction parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReconstruction algorithm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eiDose\u003csup\u003e4\u003c/sup\u003e(standard B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVMI\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;70Kev\u003c/sub\u003e, an interval of 10 keV(standard B)/IMR༈Routine༉\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDenoising level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3/6(iDose\u003csup\u003e4\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3/6(VMI)and 2/3༈IMR༉\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReconstruction thickness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0/0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0/0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReconstruction matrix\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e512*512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e512*512\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eScanning parameters\u003c/h3\u003e\n\u003cp\u003eStandardized imaging parameters were applied across both groups, including a fixed tube voltage of 120 kVp, pitch of 1.2, gantry rotation time of 0.5 seconds, and detector collimation of 64*0.625 mm. The field of view was adjusted to individual patient anatomy (350\u0026ndash;400 mm). Automatic Tube Current Modulation (ATCM; DoseRight system, Philips Healthcare) was utilized to manage radiation exposure based on the DoseRight Index (DRI). The DRI was set to 22 for the full-dose group and reduced to 10 for the dual-low-dose group, with tube current dynamically adjusted to maintain these prespecified dose levels.\u003c/p\u003e\n\u003ch3\u003eParameters for Image Reconstruction:\u003c/h3\u003e\n\u003cp\u003eFull-dose group (Group A): Hybrid iterative reconstruction (iDose4, Standard B, Level 3/6, Philips Healthcare).Dual-low-dose groups (Groups B-E): Spectral-based holographic Imaging reconstruction (SBI, Spectral B, Level 3/6, Philips Healthcare) and virtual monoenergetic imaging (VMI) generated based on SBI data packets: Group B: VMI at 70 keV Group C: VMI at 60 keV Group D: VMI at 50 keV Group E: VMI at 40 keV. Dual-low-dose group (Group F): Model-based iterative reconstruction (IMR, Routine, Level 2/3, Philips Healthcare).All reconstructed images possessed a slice thickness of 1 mm, a slice spacing of 0.8 mm, a window width of 400 HU, and a window level of 60 HU.\u003c/p\u003e\n\u003ch3\u003eQuantitative analysis\u003c/h3\u003e\n\u003cp\u003eAll CT images were transferred to a dedicated post-processing workstation (IntelliSpace Portal Version 10.1, Philips Healthcare) for quantitative analysis. Two board-certified abdominal radiologists (with 8 and 13 years of experience in abdominal CT interpretation, respectively) independently performed region-of-interest (ROI) measurements under standardised viewing conditions (width: 400 HU, level: 60 HU). For each subject (Groups A\u0026ndash;F), circular or elliptical ROIs were systematically placed at the following anatomical locations:\u003c/p\u003e\n\u003cp\u003eAbdominal aorta: transverse section at renal hilum level (ROI area: 20\u0026ndash;30 mm\u0026sup2;)\u003c/p\u003e\n\u003cp\u003eErector spinae muscles: Bilateral measurements at the maximal transverse section (ROI area: 30\u0026ndash;50 mm\u0026sup2;)\u003c/p\u003e\n\u003cp\u003eRenal arteries: Main trunk, 1 cm distal to the origin (ROI encompassing full luminal diameter while carefully avoiding vessel walls)\u003c/p\u003e\n\u003cp\u003eMeasurements were carefully performed to exclude regions affected by calcifications, thrombuses, focal lesions, or beam-hardening Artifact. Each ROI was measured by two radiologists, and the average attenuation value (in Hounsfield units [HU]) and standard deviation (SD) were recorded. Noise was quantified as the SD of the measured CT attenuation values. To ensure geometric consistency across all study groups (A-F), the ROI copy-paste method was used, ensuring identical size, shape, and spatial positioning for comparative analyses.\u003c/p\u003e\n\u003cp\u003eSNR \u003csub\u003eabdominal aorta\u003c/sub\u003e = CT \u003csub\u003eabdominal aorta\u003c/sub\u003e/SD \u003csub\u003eabdominal aorta\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eSNR \u003csub\u003erenal artery\u003c/sub\u003e = CT \u003csub\u003erenal artery\u003c/sub\u003e/SD \u003csub\u003erenal artery\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eCNR \u003csub\u003eabdominal aorta\u003c/sub\u003e = (CT \u003csub\u003eabdominal aorta\u003c/sub\u003e - CT \u003csub\u003eerector spinae\u003c/sub\u003e)/SD \u003csub\u003eerector spinae\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eCNR \u003csub\u003erenal artery\u003c/sub\u003e =(CT \u003csub\u003erenal artery\u003c/sub\u003e - CT \u003csub\u003eerector spinae\u003c/sub\u003e)/SD \u003csub\u003eerector spinae\u003c/sub\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eQualitative analysis\u003c/h2\u003e\n \u003cp\u003eImage evaluation was predominantly performed on axial reconstructions, supplemented by multiplanar maximum intensity projection (MIP) and volume rendering (VR) reformats. Two fellowship-trained abdominal radiologists (each with over 8 years of experience in abdominal imaging) independently reviewed the images on a dedicated workstation (IntelliSpace Portal, Version 10.1; Philips Healthcare).To ensure blinding, all patient demographic data and acquisition parameters were anonymized. Cases were presented in a randomized sequence with standardized anatomical alignment. To maintain consistency, window-level adjustments were not permitted during the review. Qualitative image quality was assessed using a validated 5-point Likert scale (Table 3). Images with a score of \u0026ge;\u0026thinsp;3 were considered diagnostic, while those scoring\u0026thinsp;\u0026le;\u0026thinsp;2 were classified as non-diagnostic.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eTable\u0026nbsp;3: The Qualitative Score Criteria of Image Quality\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScores\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVessel Contrast\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVessel Artifact\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDiagnostic Confidence\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMIP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExcellent contrast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo artifacts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFully diagnostic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMany peripheral branches visualized\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood contrast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinor artifacts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood diagnostic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSeveral peripheral branches visualized\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuboptimal contrast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate artifacts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnostic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eProximal part of several peripheral branches visualized\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoor contrast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObvious artifacts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAffecting diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePeripheral branches not visualized\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCannot be displayed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSevere artifacts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-diagnostic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSecond-order branches not completely\u003c/p\u003e\n \u003cp\u003evisualized\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eRadiation dose management\u003c/h3\u003e\n\u003cp\u003eRadiation dose was quantified using three standardized metrics: volume CT dose index (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{CTD}{\\text{I}}_{\\text{vol}}\\)\u003c/span\u003e\u003c/span\u003e, mGy), dose-length product (DLP, mGy\u0026middot;cm), and effective dose (ED, mSv). The effective dose was calculated using the formula \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{ED=DLP\u0026times;k}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{k}\\)\u003c/span\u003e\u003c/span\u003e represents the organ-specific conversion factor for the abdomen (0.015 mSv/[mGy\u0026middot;cm]).\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eStatistical analyses were performed using SPSS Statistics (version 23.0, IBM Corp.) and GraphPad Prism (version 6.0, GraphPad Software Inc.). Continuous variables with normal distribution are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), while non-normally distributed data are reported as median and interquartile range (Q1, Q3). Categorical variables were analysed using Fisher\u0026apos;s exact test. Continuous demographic parameters (age, weight, height, BMI), contrast administration details (volume, iodine load), and radiation dose metrics (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{CTD}{\\text{I}}_{\\text{vol}}\\)\u003c/span\u003e\u003c/span\u003e, DLP, ED) were compared using independent t-tests. Quantitative image quality metrics (CT value, SD, SNR, and CNR) were compared using one-way ANOVA; Dunnett\u0026rsquo;s T3 test was employed for post-hoc pairwise comparisons between groups. Qualitative image quality scores were compared using the Kruskal-Wallis H test, with post-hoc pairwise comparisons performed using the Mann-Whitney U-test with Bonferroni correction. Inter-reader agreement for qualitative evaluation was assessed using the Kappa statistic ( \u003cem\u003ek\u003c/em\u003e ) .Agreement was categorized as poor (\u003cem\u003ek\u003c/em\u003e\u0026lt;0.40), moderate(\u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.41\u0026mdash;0.60), good (\u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.61\u0026mdash;0.80) or excellent(\u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.81\u0026mdash;1.00). A \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Result","content":"\u003ch2\u003eDemographic and Clinical Characteristics\u003c/h2\u003e\u003cp\u003eA total of 94 patients (55 males, 39 females) successfully underwent renal CTA. Baseline demographic characteristics were well-balanced between the two groups(\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Clinical indications in the full-dose group included hypertension (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{n=16}\\)\u003c/span\u003e\u003c/span\u003e), renal mass (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{n=16}\\)\u003c/span\u003e\u003c/span\u003e), adrenal mass (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{n=9}\\)\u003c/span\u003e\u003c/span\u003e), renal artery stenosis (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{n=5}\\)\u003c/span\u003e\u003c/span\u003e), and renal perforation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{n=1}\\)\u003c/span\u003e\u003c/span\u003e). Indications in the dual-low-dose group comprised hypertension (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{n=8}\\)\u003c/span\u003e\u003c/span\u003e), renal mass (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{n=17}\\)\u003c/span\u003e\u003c/span\u003e), adrenal mass (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{n=17}\\)\u003c/span\u003e\u003c/span\u003e), and renal artery stenosis (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{n=5}\\)\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eRadiation Dose and Iodine Load\u003c/h2\u003e\u003cp\u003eSignificant reductions in radiation dose metrics (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{CTD}{\\text{I}}_{\\text{vol}}\\)\u003c/span\u003e\u003c/span\u003e, DLP, ED) and iodine load were achieved in the dual-low-dose group compared to the full-dose group(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Specifically, the dual-low-dose protocol resulted in a 65% reduction in effective dose (ED) and a 50% decrease in total iodine burden (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eQuantitative Image Quality Analysis\u003c/h2\u003e\u003ch2\u003eComparison between Full-Dose (Group A) and Dual-Low-Dose Groups (Groups B–F)\u003c/h2\u003e\u003cp\u003eQuantitative image quality metrics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003e–\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003e Significant inter-group differences in CT attenuation were observed in the abdominal aorta and bilateral renal arteries (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001). Groups C–E (40–60 keV) exhibited significantly higher attenuation compared to Group A (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Conversely, attenuation levels in Group B (70 keV) and Group F (IMR) were comparable to Group A (375.30 ± 52.55 and 379.47 ± 54.92 HU vs. 369.89 ± 49.47 HU, respectively;\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05).\u003c/p\u003e\u003cp\u003eGroup F (IMR) demonstrated superior overall performance compared to Group A, characterized by significantly higher SNR and CNR values and reduced image noise (SD) (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). While Groups D–E (40–50 keV) achieved higher SNR and CNR than Group A, they also exhibited increased image noise. Group B (70 keV) yielded the poorest performance, with significantly lower SNR and CNR compared to Group A across all assessed vessels (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Quantitative Image Quality Analysis among all groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup A iDose\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup B 70Kev\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup C 60Kev\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup D 50Kev\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup E 40Kev\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGroup F IMR\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eF value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eCT value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal aorta\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e369.89 ± 49.47\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e375.30 ± 52.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e515.37 ± 75.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e748.77 ± 112.43\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1147.12 ± 176.68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e379.47 ± 54.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e470.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.000 1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight renal artery\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e348.01 ± 50.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e354.93 ± 52.44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e481.63 ± 72.76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e692.80 ± 107.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1052.43 ± 168.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e369.08 ± 59.16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e406.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.000 1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft renal artery\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e343.58 ± 50.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e352.83 ± 57.43\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e477.26 ± 81.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e685.41 ± 120.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1039.33 ± 187.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e362.97 ± 57.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e327.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.000 1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSD value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal aorta\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.24 ± 3.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.16 ± 3.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.51 ± 3.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.37 ± 4.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.54 ± 4.47\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.07 ± 1.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e189.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.000 1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight renal artery\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.11 ± 2.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.28 ± 3.74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.41 ± 3.71\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.94 ± 3.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.50 ± 3.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.04 ± 1.60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e112.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.000 1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft renal artery\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.01 ± 2.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.67 ± 3.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.91 ± 3.79\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.50 ± 3.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.04 ± 4.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.11 ± 1.49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e89.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.000 1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSNR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal aorta\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.53 ± 3.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.63 ± 2.93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.48 ± 4.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.07 ± 5.76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.20 ± 8.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.52 ± 5.83\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e147.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.000 1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight renal artery\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.47 ± 3.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.39 ± 3.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.11 ± 5.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.99 ± 7.17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.96 ± 10.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.74 ± 5.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e132.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.000 1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft renal artery\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.38 ± 3.93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.81 ± 4.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.47 ± 5.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.37 ± 7.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.61 ± 11.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.04 ± 5.53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e116.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.000 1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCNR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal aorta\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.13 ± 3.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.44 ± 3.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.42 ± 4.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.79 ± 6.82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.94 ± 10.17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.13 ± 6.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e145.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.000 1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight renal artery\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.98 ± 3.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.56 ± 3.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.96 ± 4.67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.45 ± 6.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.22 ± 9.91\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.37 ± 6.93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e124.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.000 1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft renal artery\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.74 ± 3.77\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.47 ± 3.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.78 ± 4.95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.16 ± 7.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.73 ± 10.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.75 ± 6.68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e111.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.000 1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eData are presented as the mean ± standard deviation or number.\u003c/p\u003e\u003cp\u003eCT,computed tomography; SD, standard deviation; SNR, signal-to-noise ratio; CNR, contrast-to-noise ratio.\u003c/p\u003e\u003ch2\u003eComparison within Dual-Low-Dose Subgroups (Groups D, E, and F)\u003c/h2\u003e\u003cp\u003eWithin the dual-low-dose groups, CT attenuation values increased as VMI energy levels decreased (Groups D-E), differing significantly from Group F (IMR) (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Group E (40 keV) achieved the highest SNR and CNR, significantly surpassing Group F(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). However, Group F (IMR) exhibited the lowest image noise, which was significantly lower than that of Groups D–E (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Notably, no statistically significant differences in SNR and CNR were observed between Group F (IMR) and Group D (50 keV)(\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05).\u003c/p\u003e\u003ch2\u003eQualitative Image Quality Analysis\u003c/h2\u003e\u003ch2\u003eComparison between Full-Dose (Group A) and Dual-Low-Dose Groups (Groups B–F)\u003c/h2\u003e\u003cp\u003eInter-reader agreement for qualitative assessments ranged from good to excellent (\u003cem\u003ek\u003c/em\u003e values: 0.863 for vessel contrast, 0.831 for artifacts, 0.844 for diagnostic confidence, 0.757 for VR, and 0.730 for MIP).\u003c/p\u003e\u003cp\u003eIn comparative analysis, Group E (40 keV) showed no significant differences in diagnostic confidence or vessel artifact scores compared to Group A(\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05). Group D (50 keV) demonstrated superior diagnostic confidence, vessel contrast, VR, and MIP quality compared to Group A(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Conversely, Groups B–C (60–70 keV) scored significantly lower in vessel artifact reduction and diagnostic confidence(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). While Group F (IMR) showed comparable scores to Group A for vessel artifacts and contrast(\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05), it achieved significantly higher ratings for diagnostic confidence, VR, and MIP quality (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eComparison within Dual-Low-Dose Subgroups (Groups D, E, and F)\u003c/h2\u003e\u003cp\u003eWithin the dual-low-dose groups, Group F (IMR) and Group D (50 keV) achieved the highest scores for diagnostic confidence and artifact reduction, with no significant difference between them (all \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05). Both groups scored significantly higher in these categories than Group E (40 keV) (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). For vessel contrast, VR, and MIP assessment, Group E (40 keV) scored significantly higher than Group F (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). However, no significant differences were found in VR and MIP scores between Group D (50 keV) and Group F(\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e:Result of qualitative observation indices of image quality among all groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup A\u003c/p\u003e \u003cp\u003eiDose\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup B\u003c/p\u003e \u003cp\u003e70Kev\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup C\u003c/p\u003e \u003cp\u003e60Kev\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup D\u003c/p\u003e \u003cp\u003e50Kev\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup E\u003c/p\u003e \u003cp\u003e40Kev\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGroup F\u003c/p\u003e \u003cp\u003eIMR\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eH value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVessel Contrast\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(3,4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(2,3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(3,4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(4,4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5(4.75,5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3(3,4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e357.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.000 1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVessel Artifact\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(4,4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(2,3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(3,4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(4,4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(4,4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4(4,4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e327.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.000 1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiagnostic Confidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(4,4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(2,3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(3,4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(4,4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(4,4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4(4,5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e358.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.000 1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5(3,4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(2,3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(3,4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(4,4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5(5,5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4(4,4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e379.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.000 1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMIP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5(3,4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(2,3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(3,4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(4,4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5(5,5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4(4,5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e380.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt; 0.000 1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eQualitative image quality score for Conventional-dose group (Group A) and Dual-low-dose groups B-F(40/50/60/70 keV and IMR)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study systematically evaluated the image quality of renal CTA using DLCT-derived VMI and IMR with decreased contrast and radiation doses. Our primary findings indicate that both low-energy VMI (40\u0026ndash;50 keV) and IMR significantly improve diagnostic interpretability while achieving superior SNR and CNR. Notably, these improvements were maintained despite a substantial reduction in contrast media volume (approximately 67%) and radiation exposure (approximately 33%) compared to conventional HIR at standard doses.\u003c/p\u003e \u003cp\u003eThe efficacy of the dual-low-dose protocol is largely attributable to the energy-dependent attenuation properties of iodine. As photon energy approaches the iodine K-edge absorption threshold (33.2 keV), the photoelectric absorption cross-section increases substantially. Consequently, VMI at lower energy levels (e.g., 40\u0026ndash;70 keV) significantly enhances attenuation differences between vessels, parenchymal organs, and pathological lesions\u003csup\u003e[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Our analysis confirmed that utilizing a 50% reduced iodine load within the 40\u0026ndash;50 keV range yielded higher arterial attenuation values (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as well as enhanced CNR and SNR, compared to full-dose groups.\u003c/p\u003e \u003cp\u003eNoise management remains critical when evaluating delicate anatomical structures such as the renal arteries. In this study, we employed the DRI system for real-time dose modulation\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, whereby lower DRI values reduce tube current and consequently radiation dose, albeit at the expense of increased image noise.Our quantitative analysis revealed that although the dual-low-dose protocol significantly increased image noise in VMI reconstructions compared to conventional acquisitions, this elevation was largely offset by a corresponding enhancement in vessel-to-background contrast ratios\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.These findings were corroborated by qualitative image quality assessments, with 40\u0026ndash;50 keV VMI datasets achieving comparable diagnostic confidence scores.Furthermore,advanced three-dimensional post-processing techniques, including VR and MIP enhanced the delineation of third- and fourth-order arterial branches in the VMI groups(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The technical architecture of DLCT confers distinct advantages in noise management.Unlike conventional DECT, DLCT employs true simultaneous acquisition, thereby eliminating temporal mismatches and enabling projection-based decomposition that minimizes beam-hardening artifacts.More importantly, its capacity to identify and suppress anticorrelated noise in raw material basis datasets provides superior noise reduction while preserving signal integrity\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. D'Angelo et al. reported comparable findings in coronary artery imaging, demonstrating that DLCT-derived VMI at 40 keV with standard contrast dose achieved optimal SNR and CNR without an additional noise penalty\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. In contrast, fast kVp-switching scanners operate at reduced photon output when generating low-energy data, and the absence of automated tube current modulation in this technical configuration may result in elevated image noise\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.Similarly, dual-source dual-energy CT (dsDECT), which performs material decomposition in the image domain rather than the projection domain, is also susceptible to increased noise levels\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition to spectral reconstructions, the advancement of iterative reconstruction techniques has been pivotal for low-dose protocols. Compared to HIR, IMR utilizes advanced modeling to reduce image noise and improve spatial resolution, a capability previously shown to enhance peripheral renal artery visualization while reducing noise by approximately 52% compared to filtered back projection\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. In the present study, the dual-low-dose groups achieved radiation exposure levels only one-third that of the conventional-dose group, with significantly reduced CTDIvol (3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99 mGy) and effective dose (1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47 mSv).These effective dose values are lower than those reported in comparable studies\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. However, IMR is not without limitations; the technique can produce characteristic \"waxy\" or \"plastic\" textural artifacts, which may affect diagnostic confidence\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. To mitigate this, we utilized a \"routine\" kernel at level 2/3. This setting offered an optimal compromise, yielding the lowest noise values among all reconstructions while minimizing artificial textures and improving the CNR and SNR of the abdominal aorta and renal arteries. Consequently, IMR demonstrated significantly higher CNR when delineating smaller vessels, such as branches of the mesenteric artery, compared to VMI and HIR. This superior visualization is likely due to IMR\u0026rsquo;s ability to reduce noise spectral density while preserving the modulation transfer function.\u003c/p\u003e \u003cp\u003eIn this study, the conventional-dose group involved an iodine burden of 29.59\u0026thinsp;\u0026plusmn;\u0026thinsp;4.71 g, which was methodically reduced by approximately 50% to 14.96\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86 g in the dual-low-dose groups. Although Group E (40 keV) demonstrated the highest arterial attenuation, SNR, and CNR, these quantitative improvements did not translate to increased diagnostic confidence. Conversely, Group E showed significantly lower diagnostic confidence scores compared to Group F (IMR) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This discrepancy arises from the proximity of the 40 keV level to the iodine K-edge, which causes an abrupt rise in CT values. This excessive attenuation can result in \"blooming\" artifacts that obscure vascular details and hinder the precise evaluation of stenotic lesions or vessel lumens\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In contrast, both Group D (50 keV) and Group F (IMR) achieved the highest diagnostic confidence scores (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), coupled with favorable SNR and CNR. Therefore, our data suggests that 50 keV represents the optimal trade-off between contrast enhancement and image noise for vascular imaging.Some investigators have proposed windowing adjustments to mitigate pseudo-stenosis at lower energies\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e,presenting a promising direction for our future research.meanwhile,the application of photon-counting CT and deep learning reconstruction algorithms holds significant potential for further improving image quality in future research\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the sample size was constrained by the single-center design, which may introduce selection bias and limit the generalizability of the findings. Second, the study cohort exclusively included patients with a body mass index (BMI) below 25 kg/m\u0026sup2;. As obese patients often present distinct imaging challenges regarding noise and attenuation, the applicability of these low-dose protocols to populations with higher BMI requires further validation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, renal artery CTA with low radiation dose and Contrast agent can provide equivalent or improved renal artery image quality using 40\u0026ndash;50 keV VMI or IMR reconstructions on SDCT. These protocol optimizations mitigate population exposure risks of ionizing radiation and contrast-induced complications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eliterature research, experimental design and manuscript editing: Chengle Ma.statistical analysis,graph plotting and specimen collection: Ruiquan Chen and Yinchen Wu.designed the study and supervised the research implementation:Fan Zhang and Yuyang Zhang.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFraioli F, Catalano C, Bertoletti L, et al. Multidetector-row CT angiography of renal artery stenosis in 50 consecutive patients: prospective interobserver comparison with DSA[J]. Radiol Med, 2006,111(3):459-468.\u003c/li\u003e\n\u003cli\u003eLuo S, Lin W, Wu J, et al. Quantitative Measurement on Contrast-Enhanced CT Distinguishes Small Clear Cell Renal Cell Carcinoma From Benign Renal Tumors: A Multicenter Study[J]. Acad Radiol, 2024,31(4):1460-1471.\u003c/li\u003e\n\u003cli\u003ede Freminville J, Gardini M, Cremer A, et al. Prevalence and Risk Factors for Secondary Hypertension in Young Adults[J]. Hypertension, 2024,81(11):2340-2349.\u003c/li\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries[J]. CA Cancer J Clin, 2021,71(3):209-249.\u003c/li\u003e\n\u003cli\u003eSmith-Bindman R, Chu P W, Azman Firdaus H, et al. Projected Lifetime Cancer Risks From Current Computed Tomography Imaging[J]. JAMA Intern Med, 2025.\u003c/li\u003e\n\u003cli\u003eDavenport M S, Cohan R H, Khalatbari S, et al. The challenges in assessing contrast-induced nephropathy: where are we now?[J]. AJR Am J Roentgenol, 2014,202(4):784-789.\u003c/li\u003e\n\u003cli\u003eMcDonald J S, McDonald R J, Carter R E, et al. Risk of intravenous contrast material-mediated acute kidney injury: a propensity score-matched study stratified by baseline-estimated glomerular filtration rate[J]. Radiology, 2014,271(1):65-73.\u003c/li\u003e\n\u003cli\u003eMcDonald J S, McDonald R J, Comin J, et al. Frequency of acute kidney injury following intravenous contrast medium administration: a systematic review and meta-analysis[J]. Radiology, 2013,267(1):119-128.\u003c/li\u003e\n\u003cli\u003eMcCollough C H, Leng S, Yu L, et al. Dual- and Multi-Energy CT: Principles, Technical Approaches, and Clinical Applications[J]. Radiology, 2015,276(3):637-653.\u003c/li\u003e\n\u003cli\u003eRajiah P, Parakh A, Kay F, et al. Update on Multienergy CT: Physics, Principles, and Applications[J]. Radiographics, 2020,40(5):1284-1308.\u003c/li\u003e\n\u003cli\u003eZhou X, Cui M, Liu Y, et al. Low Dose Iodinated Contrast Material and Radiation for Virtual Monochromatic Imaging in Craniocervical Dual-Layer Spectral Detector Computed Tomography Angiography: A Prospective and Randomized Study[J]. Acad Radiol, 2024,31(6):2501-2510.\u003c/li\u003e\n\u003cli\u003eMorita S, Ogawa Y, Yamamoto T, et al. Image quality of early postoperative CT angiography with reduced contrast material and radiation dose using model-based iterative reconstruction for screening of renal pseudoaneurysms after partial nephrectomy[J]. Eur J Radiol, 2020,124:108853.\u003c/li\u003e\n\u003cli\u003eKalisz K, Rassouli N, Dhanantwari A, et al. Noise characteristics of virtual monoenergetic images from a novel detector-based spectral CT scanner[J]. Eur J Radiol, 2018,98:118-125.\u003c/li\u003e\n\u003cli\u003eChen Y, Liu Z, Li M, et al. Reducing both radiation and contrast doses in coronary CT angiography in lean patients on a 16-cm wide-detector CT using 70 kVp and ASiR-V algorithm, in comparison with the conventional 100-kVp protocol[J]. Eur Radiol, 2019,29(6):3036-3043.\u003c/li\u003e\n\u003cli\u003eSingh S, Kalra M K, Hsieh J, et al. Abdominal CT: comparison of adaptive statistical iterative and filtered back projection reconstruction techniques[J]. Radiology, 2010,257(2):373-383.\u003c/li\u003e\n\u003cli\u003eRen Z, Zhang X, Hu Z, et al. Application of Adaptive Statistical Iterative Reconstruction-V With Combination of 80 kV for Reducing Radiation Dose and Improving Image Quality in Renal Computed Tomography Angiography for Slim Patients[J]. Acad Radiol, 2019,26(11):e324-e332.\u003c/li\u003e\n\u003cli\u003eStiller W. Basics of iterative reconstruction methods in computed tomography: A vendor-independent overview[J]. Eur J Radiol, 2018,109:147-154.\u003c/li\u003e\n\u003cli\u003eDeak Z, Grimm J M, Treitl M, et al. Filtered back projection, adaptive statistical iterative reconstruction, and a model-based iterative reconstruction in abdominal CT: an experimental clinical study[J]. Radiology, 2013,266(1):197-206.\u003c/li\u003e\n\u003cli\u003eQian W, Zhou D, Jiang Y, et al. Ultra-low radiation dose CT angiography of the lower extremity using the iterative model reconstruction (IMR) algorithm[J]. Clin Radiol, 2018,73(11):913-985.\u003c/li\u003e\n\u003cli\u003eHajdu S D, Daniel R T, Meuli R A, et al. Impact of model-based iterative reconstruction (MBIR) on image quality in cerebral CT angiography before and after intracranial aneurysm treatment[J]. Eur J Radiol, 2018,102:109-114.\u003c/li\u003e\n\u003cli\u003eHuang X, Gao S, Ma Y, et al. The optimal monoenergetic spectral image level of coronary computed tomography (CT) angiography on a dual-layer spectral detector CT with half-dose contrast media[J]. Quant Imaging Med Surg, 2020,10(3):592-603.\u003c/li\u003e\n\u003cli\u003eAl-Baldawi Y, Grosse Hokamp N, Haneder S, et al. Virtual mono-energetic images and iterative image reconstruction: abdominal vessel imaging in the era of spectral detector CT[J]. Clin Radiol, 2020,75(8):641-649.\u003c/li\u003e\n\u003cli\u003eRen H, Zhen Y, Gong Z, et al. Feasibility of low-dose contrast media in run-off CT angiography on dual-layer spectral detector CT[J]. Quant Imaging Med Surg, 2021,11(5):1796-1804.\u003c/li\u003e\n\u003cli\u003eFillon M, Si-Mohamed S, Coulon P, et al. Reduction of patient radiation dose with a new organ based dose modulation technique for thoraco-abdominopelvic computed tomography (CT) (Liver dose right index)[J]. Diagn Interv Imaging, 2018,99(7-8):483-492.\u003c/li\u003e\n\u003cli\u003eD\u0026apos;Angelo T, Lanzafame L R M, Micari A, et al. Improved Coronary Artery Visualization Using Virtual Monoenergetic Imaging from Dual-Layer Spectral Detector CT Angiography[J]. Diagnostics (Basel), 2023,13(16).\u003c/li\u003e\n\u003cli\u003eRassouli N, Etesami M, Dhanantwari A, et al. Detector-based spectral CT with a novel dual-layer technology: principles and applications[J]. Insights Imaging, 2017,8(6):589-598.\u003c/li\u003e\n\u003cli\u003eOzguner O, Dhanantwari A, Halliburton S, et al. Objective image characterization of a spectral CT scanner with dual-layer detector[J]. Phys Med Biol, 2018,63(2):25027.\u003c/li\u003e\n\u003cli\u003eAgostini A, Borgheresi A, Mari A, et al. Dual-energy CT: theoretical principles and clinical applications[J]. Radiol Med, 2019,124(12):1281-1295.\u003c/li\u003e\n\u003cli\u003eWu R, Hori M, Onishi H, et al. Effects of reconstruction technique on the quality of abdominal CT angiography: A comparison between forward projected model-based iterative reconstruction solution (FIRST) and conventional reconstruction methods[J]. Eur J Radiol, 2018,106:100-105.\u003c/li\u003e\n\u003cli\u003eLaurent G, Villani N, Hossu G, et al. Full model-based iterative reconstruction (MBIR) in abdominal CT increases objective image quality, but decreases subjective acceptance[J]. Eur Radiol, 2019,29(8):4016-4025.\u003c/li\u003e\n\u003cli\u003eIuga A, Doerner J, Siedek F, et al. Computed tomography pulmonary angiograms using a novel dual-layer spectral detector: Adjusted window settings are essential for diagnostic image quality[J]. Medicine (Baltimore), 2019,98(33):e16606.\u003c/li\u003e\n\u003cli\u003eWrazidlo R, Walder L, Estler A, et al. Radiation Dose Reduction in Contrast-Enhanced Abdominal CT: Comparison of Photon-Counting Detector CT with 2nd Generation Dual-Source Dual-Energy CT in an oncologic cohort[J]. Acad Radiol, 2023,30(5):855-862.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dual-Layer Spectral Detector CT, Virtual Monochromatic Imaging","lastPublishedDoi":"10.21203/rs.3.rs-8485820/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8485820/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003e To evaluate the efficacy of Virtual Monochromatic Imaging (VMI) and Model-based Iterative Reconstruction (IMR) derived from Dual-Layer Spectral Detector CT (DLCT) in reducing radiation dose and contrast agent volume during renal CT angiography (CTA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Ninety-four patients with suspected renal artery disease were prospectively enrolled and randomized into a full-dose group and a dual-low-dose group (n=47 each). Scans were performed on a DLCT system with Automatic Tube Current Modulation. The full-dose group received 1.5 ml/kg iohexol (300 mgi/ml) with a Dose Right Index (DRI) of 22 and hybrid iterative reconstruction(Group A).The dual-low-dose group received 0.75 ml/kg iohexol with a DRI of 10; images were reconstructed using VMI at 70, 60, 50, and 40 keV (Groups B–E) and IMR (Group F). Quantitative metrics (CT value, noise, SNR, CNR) and Qualitative image quality were compared among groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The dual-low-dose protocol achieved 65% and 50% reductions in effective dose and iodine load, respectively. Groups D (50 keV) and F (IMR) provided optimal reconstructions,followed by Group E. SNR and CNR in Groups D–F were significantly higher than in Group A (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). While Group F exhibited the lowest noise (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), its SNR and CNR were comparable to Group D (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05). Subjectively, vessel contrast, artifacts, and diagnostic confidence in Groups D and E matched or exceeded Group A. Diagnostic confidence in Group F was superior to Group E (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) and equivalent to Group D (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eDLCT-based renal CTA utilizing 40–50 keV VMI or IMR reconstruction maintains or improves image quality while significantly reducing radiation and contrast burden compared to standard protocols with hybrid iterative reconstruction.\u003c/p\u003e","manuscriptTitle":"Enabling Low-Radiation-Dose and Low-Contrast-Volume Renal Artery CT Angiography with Dual-Layer Spectral Detector CT-Derived Virtual Monoenergetic Imaging and Model-Based Iterative Reconstruction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-30 14:41:26","doi":"10.21203/rs.3.rs-8485820/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"16e17a04-8277-49d2-8769-09495b6a28b4","owner":[],"postedDate":"January 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T12:26:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-30 14:41:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8485820","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8485820","identity":"rs-8485820","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}