Comparative Diagnostic Performance of Dual-Layer Detector Spectral CT–Derived Multiparametric Fusion Imaging for Detecting Common Bile Duct Stones

Comparative Diagnostic Performance of Dual-Layer Detector Spectral CT–Derived Multiparametric Fusion Imaging for Detecting Common Bile Duct Stones | 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 Comparative Diagnostic Performance of Dual-Layer Detector Spectral CT–Derived Multiparametric Fusion Imaging for Detecting Common Bile Duct Stones Shan Yang, Jun Qin, XiaoKun Zhang, Yujie Wang, Jing Wen, Bin Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9118932/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Common bile duct (CBD) stones are a common cause of biliary obstruction and may lead to serious complications if not diagnosed accurately. Although magnetic resonance cholangiopancreatography (MRCP) and conventional computed tomography (CT) are widely used in clinical practice, conventional CT has limited sensitivity for small, non-calcified, or isoattenuating stones. Dual-layer detector spectral CT (DLCT) provides multiparametric information that may improve stone detection. This study aimed to compare the diagnostic performance of DLCT-derived 40-keV/Z eff fusion images with that of MRCP, conventional CT, and single-parameter DLCT images for detecting CBD stones. Methods This retrospective study was conducted at Civil Aviation General Hospital, Beijing, China. A total of 98 patients who underwent non-contrast DLCT and MRCP between May 2024 and November 2025 for suspected gallbladder stones or CBD dilatation were included. Laparoscopic common bile duct exploration (LCBDE) or endoscopic retrograde cholangiopancreatography (ERCP) served as the reference standard. Four image datasets were reconstructed from the DLCT data: conventional CT images, 40-keV virtual monoenergetic images, effective atomic number (Z eff ) images, and 40-keV/ Zeff fusion images. Two radiologists blinded to the MRCP reports independently reviewed all CT datasets for the presence of CBD stones. MRCP findings were obtained from the final clinical reports archived in the hospital information system and were analyzed as a comparator imaging modality. Diagnostic performance was assessed on a per-patient basis. Receiver operating characteristic (ROC) curve analysis was performed to calculate the area under the curve (AUC), and AUCs were compared using the DeLong test. Results Among the 98 included patients, 53 were confirmed to have CBD stones, including 45 (84.9%) with secondary stones and 8 (15.1%) with primary stones. Conventional CT achieved an AUC of 0.742, with a sensitivity of 52.8% (28/53) and a specificity of 95.6% (43/45). The 40-keV/Z eff fusion images improved diagnostic performance, achieving an AUC of 0.872 and a sensitivity of 81.1% (43/53), while maintaining a specificity of 93.3% (42/45). MRCP achieved an AUC of 0.912, with a sensitivity of 86.8% (46/53) and a specificity of 95.6% (43/45). The diagnostic performance of the fusion images was comparable to that of MRCP (AUC, 0.872 vs 0.912; P = 0.233). Conclusions DLCT-derived 40-keV/Z eff fusion imaging improves the detection of CBD stones and demonstrates diagnostic performance comparable to that of MRCP. This technique may serve as a valuable adjunct imaging modality in clinical practice. common bile duct stones diagnostic performance dual-layer detector spectral CT fusion imaging magnetic resonance cholangiopancreatography Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Choledocholithiasis is a important manifestation within the spectrum of gallstone disease, particularly common in patients with gallbladder stones. It is estimated that approximately 10%-20% of patients with gallstones develop common bile duct stones, and the incidence increases with age [ 1 ] . Previous studies have demonstrated that approximately 70%-90% of patients with choledocholithiasis have secondary stones, resulting from gallstones migrating through the cystic duct from the gallbladder. The remaining have primary stones often associated with biliary tract infections, cholestasis, and anatomical abnormalities of the bile ducts [ 2 , 3 ] . Common bile duct (CBD) stones can lead to biliary obstruction and secondary infection, potentially progressing to acute cholangitis or biliary pancreatitis, which may be life-threatening in severe cases. Therefore, timely and accurate imaging evaluation is essential for formulating appropriate treatment strategies and minimizing the risk of severe complications [ 2 , 4 ] . In clinical practice, confirming the presence of CBD stones directly influences decisions regarding invasive treatments, such as endoscopic retrograde cholangiopancreatography (ERCP) or laparoscopic common bile duct exploration (LCBDE) [ 5 , 6 ] . Although clinical prediction models based on clinical manifestations and laboratory indicators (e.g., American Society for Gastrointestinal Endoscopy [ASGE] guidelines) are commonly used for preliminary risk assessment, their diagnostic accuracy can vary considerably across various patient populations with acute biliary disease. Relying solely on clinical and laboratory criteria may therefore result in unnecessary invasive procedures or missed diagnoses of stones [ 7 – 9 ] . Consequently, incorporating reliable and readily available imaging confirmation prior to invasive treatment decisions remains a crucial component of current clinical pathways [ 7 , 9 ] . In radiology, CT offers advantages in emergency settings due to its rapid acquisition and wide accessibility; however, its ability to detect isodense, noncalcified, or small CBD stones is limited [ 10 – 12 ] . These stones often demonstrate attenuation values similar to the surrounding bile on routine CT images and have poorly defined margins, increasing the likelihood of missed diagnoses. In contrast, magnetic resonance cholangiopancreatography (MRCP) uses heavy T2-weighted sequences to clearly display biliary filling defects against a high-signal bile background, resulting in high sensitivity for the noninvasive assessment of CBD stones [ 13 , 14 ] . However, the clinical application of MRCP is constrained by factors such as longer examination times, dependence on patient cooperation, susceptibility to motion artifacts, and restricted availability of resources. These limitations are particularly pronounced in emergency settings or resource-constrained settings, where diagnostic timeliness and reliability may be compromised [ 15 ] . Consequently, enhancing the diagnostic performance of CBD stones within the CT platform remains essential for optimizing clinical decision-making pathways. In recent years, dual-layer detector spectral CT (DLCT) has enabled the simultaneous acquisition of multi-energy information in a single scan, thereby providing a technical foundation for combined morphologic and compositional assessment of biliary tract lesions. Previous studies have demonstrated that virtual non-contrast images, 40-keV virtual single-energy imaging, and effective atomic number ( Z eff ) images can enhance visualization of CBD stones to a certain extent [ 16 , 17 , 18 ] . However, the overall diagnostic performance of multiparametric fusion imaging in diagnosing CBD stones has not been systematically evaluated. Therefore, this study aimed to assess the diagnostic performance of DLCT-derived 40-keV/ Z eff fusion imaging for detecting CBD stones, and to compare its performance with that of MRCP and conventional CT. 2. Materials and Methods 2.1 Patients Using our hospital information system (HIS), we identified patients who underwent noncontrast spectral CT and MRCP of the upper abdomen for “gallbladder stones” and “CBD dilatation” between May 2024 and November 2025. All included patients subsequently underwent CBD exploration or ERCP within 30 days of imaging to confirm the presence of CBD stones. The inclusion criteria were as follows: (1) patients who underwent both noncontrast DLCT and MRCP for suspected gallbladder stones or CBD dilatation; (2) patients who received surgical CBD exploration or ERCP within 30 days after imaging; and (3) patients with complete clinical and imaging data. The exclusion criteria were as follows: (1) poor image quality; (2) concurrent biliary tract neoplastic disease; (3) prior biliary tract interventional procedures, including percutaneous transhepatic biliary drainage, endoscopic nasobiliary drainage, or biliary stent placement. 2.2 DLCT Scanning Protocol Noncontrast upper abdominal scans were performed using a DLCT scanner (Spectral CT 7500; Philips Healthcare, The Netherlands). The scan parameters were as follows: tube voltage = 120 kV; tube current (controlled using DoseRight automated modulation technology) = 100-300 mAs; ReduceDose parameter = 18; collimator width = 128 × 0.625 mm; slice thickness = 1 mm; slice spacing = 1 mm; pitch = 1.190; and tube rotation time = 0.4 seconds. 2.3 DLCT Postprocessing Analysis The acquired spectral CT raw data were imported into a postprocessing workstation (IntelliSpace Portal V12; Philips Healthcare) for image reconstruction. Reconstruction slice thickness and slice spacing were both set to 1 mm, generating 40-keV virtual monoenergetic images (VMI) and Z eff maps. The 40-keV images were then fused with the Z eff maps to produce 40-keV/Z eff fusion images for subsequent analysis. The fusion images were generated using the vendor-provided automatic fusion function on IntelliSpace Portal, with standardized display parameters applied consistently across all patients and readers. 2.4 MRCP Acquisition and Clinical Assessment All patients underwent MRCP at Civil Aviation General Hospital, Beijing, China, as part of routine clinical evaluation for suspected CBD stones. MRCP findings were obtained from the final clinical reports archived in the hospital information system and were analyzed as a comparator imaging modality rather than as the reference standard. Because MRCP served as a real-world clinical comparator in this study, its diagnostic performance was based on archived final clinical reports rather than on a separate blinded rereading under the study framework. The final diagnosis of CBD stones was established using ERCP or LCBDE findings as the reference standard. All MRCP examinations were performed according to the institutional routine protocol. Because MRCP results were derived from routine clinical reports generated on different scanners, no cross-device quantitative comparison was performed. The major acquisition parameters of the MRCP examinations are summarized in Table 1. Table 1. MRCP scanning parameters for various machines Parameter SSFSE–T2WI FSE–T2WI 3D–MRCP–T1WI 2D–MRCP–T2WI 3D–MRCP–T2WI Equipment model Pioneer Vida Pioneer Vida Pioneer Vida Pioneer Vida Pioneer Vida Direction Cor Cor Axi Axi Axi Axi Radial Radial Cor Cor TR (ms) 1000 700 >2000 2300 Min 3.9 >4000 4500 >2000 3000 TE (ms) 80-100 85 70-85 95 Min 1.26 >600 700 500 560 FOV (cm) 42 × 42 41 × 35.8 40 × 40 40 × 40 40 × 38 40 × 32.5 40 × 38 30 × 30 40 × 38 32 × 30 ST/ISG (mm) 5.0/5 6.1/2 6/1 6.1/2 4/–2 6/–3 4/0 5/0 1.8/–0.9 2.0/–1.2 Matrix 320 × 288 314 × 235 320 × 320 288 × 256 320 × 208 288 × 256 288 × 256 384 × 269 255 × 256 320 × 219 Note: Pioneer refers to the GE Signa Pioneer system, and Vida refers to the Siemens Vida system. For the 3D–MRCP–T1WI sequence, the Pioneer system primarily used the LAVA sequence, whereas the Vida system primarily used the VIBE sequence. Axi, axial; Cor, coronal; 3D, three-dimensional; FOV, field of view; FSE, fast spin-echo; ISG, interslice gap; LAVA, liver acquisition with volume acceleration; MRCP, magnetic resonance cholangiopancreatography; SSFSE, single-shot fast spin-echo; ST, slice thickness; TE, echo time; TR, repetition time; T1WI, T1-weighted imaging; T2WI, T2-weighted imaging; VIBE, volumetric interpolated breath-hold examination. 2.5 Image Analysis This study used an independent blinded subjective image evaluation protocol to assess the diagnostic performance of different CT image sets for CBD stones. All CT image sets were anonymized and independently reviewed by two radiologists (one attending radiologist with 7 years of diagnostic experience and one associate chief radiologist with 10 years of diagnostic experience), both of whom were blinded to the MRCP results and reference standard findings. Each radiologist independently assessed four CT image sets: conventional CT images, 40-keV virtual monoenergetic images, Zeff images, and fused 40-keV/Zeff images. For each image set, the readers determined, on a per-patient basis, whether CBD stones were present or absent. Equivocal findings, such as “possible stone” or “high-density shadow,” were classified as negative for dichotomous diagnostic analysis. Window width and level could be adjusted freely during interpretation, and multiplanar reconstructions were allowed when necessary. Interobserver agreement was assessed for these four CT image sets and is presented in Section 3.2. 2.6 Reference Standard In this study, the presence of CBD stones was confirmed using LCBDE or ERCP as the diagnostic reference standard. The results of DLCT and MRCP imaging were compared against this standard to evaluate diagnostic accuracy. Diagnostic classifications were defined as follows: - A true positive was recorded when imaging suggested the presence of stones, which was confirmed by the reference standard. - A false positive was recorded when imaging suggested the presence of stones, but the reference standard confirmed their absence. - A true negative was recorded when imaging indicated no evidence of stones, and the reference standard confirmed their absence. - A false negative was recorded when imaging indicated no stones, despite positive confirmation by the reference standard. 2.7 Statistical Analysis This study used a retrospective paired diagnostic design. Continuous variables are presented as mean ± standard deviation, and categorical variables as counts and percentages. Using ERCP or LCBDE findings as the reference standard, the sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of the different imaging modalities were calculated, with 95% confidence intervals. Diagnostic performance was further assessed using receiver operating characteristic (ROC) curve analysis, and the area under the curve (AUC) values were compared using the DeLong test. Because all imaging modalities were applied to the same patients, paired proportions, including sensitivity, specificity, and accuracy, were compared using the McNemar test. Interobserver consistency across the four CT image sets was additionally assessed using the intraclass correlation coefficient (ICC), with higher values indicating better consistency between the two readers. This analysis was used to provide a complementary assessment of inter-reader consistency across the four CT image sets. Decision curve analysis (DCA) was performed to assess the clinical net benefit of the different imaging modalities across a range of threshold probabilities. Statistical analyses were performed using SPSS version 27.0 (IBM Corp., Armonk, NY, USA), and DCA was performed using R software (R Foundation for Statistical Computing, Vienna, Austria). A two-sided P value < 0.05 was considered statistically significant. 3. Results 3.1 Clinical Data A total of 121 patients with gallbladder stones and CBD dilatation underwent abdominal noncontrast DLCT. Among these, 98 patients met the inclusion criteria. The study cohort comprised 62 male and 36 female patients, with a mean age of 56.8 ± 17.7 years (Fig. 1). Among the included patients, 30 underwent ERCP for stone extraction, 63 underwent laparoscopic cholecystectomy and laparoscopic exploration, and 5 underwent open cholecystectomy. The choice of treatment modality was determined based on clinical presentation, stone characteristics, and multidisciplinary team assessment according to standard clinical practice. According to the reference standard findings from ERCP or LCBDE, CBD stones were confirmed in 53 patients. The clinical characteristics of the patients are summarized in Table 2. Table 2. Clinical data of participants ( n = 98) Characteristic General information ( n = 98) Sex Male Female 62 (63.3%) 36 (36.7%) Age (year) 56.8 ± 17.7 Clinical manifestations ( n = 98) Abdominal pain 91 (92.9%) Fever >38 °C 38 (38.8%) Nausea/Vomiting 43 (43.9%) Admission diagnosis ( n = 98) Cholecystitis 77 (78.6%) Cholangitis 35 (35.7%) Biliary pancreatitis 13 (13.3%) Diagnosis and treatment method ( n = 98) Laparoscopic-related surgery 63 (64.3%) ERCP 30 (30.6%) Open surgery 5 (5.1%) Stone-related characteristics in the overall cohort ( n = 98) Secondary stones 45 (45.9%) 1 Primary stones 8 (8.2%) 1 Maximum stone diameter (mm) 10.2 ± 4.7 Stone diameter ≥9 mm 24 (24.5%) 1 Stone diameter 60 U/L 61 (62.2%) ALP > 125 U/L 52 (53.1%) DBil > 6.8 μmol/L 48 (49.0%) ALT > 50 U/L 35 (35.7%) WBC > 9.5 × 10⁹/L 22 (22.4%) NEUT% > 75% 31 (31.6%) Note: Quantitative variables are expressed as mean ± standard deviation. Qualitative variables are presented as case numbers, with percentages in parentheses. ¹ These percentages represent the proportion of the total study population (n = 98). Among patients with CBD stones (n = 53), 45 (84.9%) had secondary stones and 8 (15.1%) had primary stones; 24 (45.3%) had a maximum stone diameter ≥ 9 mm and 29 (54.7%) had a diameter < 9 mm. ALP, Alkaline phosphatase; ALT , alanine transaminase; DBiL, direct bilirubin; ERCP, endoscopic retrograde cholangiopancreatography; GGT, gamma-glutamyl transpeptidase; NEUT%, neutrophil percentage; WBC, white blood cell. 3.2 Interobserver Consistency Analysis Interobserver consistency between the two readers was highest for the 40-keV/Zeff fusion images (ICC = 0.883; 95% CI: 0.842–0.916), indicating excellent consistency. The 40-keV images also showed high interobserver consistency (ICC = 0.844; 95% CI: 0.789–0.888). In contrast, conventional CT and Zeff images showed relatively lower consistency, with ICC values of 0.756 (95% CI: 0.668–0.823) and 0.749 (95% CI: 0.661–0.822), respectively. Overall, the fusion images showed the highest inter-reader consistency among the four CT image sets. 3.3 Diagnostic Performance Analysis MRCP demonstrated the highest overall diagnostic performance, with an AUC of 0.912 (95% CI: 0.848-0.976), whereas the fusion images showed comparable performance, with an AUC of 0.872 (95% CI: 0.797-0.948). In terms of sensitivity, 40-keV virtual single-energy imaging (69.81%) outperformed conventional CT (52.83%). All modalities demonstrated satisfactory specificity, with MRCP achieving the highest value (95.56%), followed by fusion imaging (93.33%). In terms of overall accuracy, both MRCP and fusion images outperformed conventional CT, 40-keV, and Z eff images. Similarly, MRCP and fusion images demonstrated superior PPV and NPV values. Statistical comparisons showed that conventional CT and Z eff images had significantly lower AUC, sensitivity, and accuracy than MRCP (all P < 0.05). The 40-keV images showed a significantly lower AUC than MRCP (P = 0.014), whereas the differences in sensitivity (P = 0.064) and accuracy (P = 0.052) were not statistically significant. The fusion images showed no significant differences from MRCP in AUC, sensitivity, specificity, or accuracy (all P > 0.05) (Table 3 and Fig. 2). Among the 53 reference-positive patients, MRCP correctly identified 46 cases, corresponding to a sensitivity of 86.79%. Fusion imaging correctly identified 43 of these 53 patients, whereas 40-keV imaging detected 37 cases, and conventional CT and Z eff imaging detected 28 and 22 cases, respectively (Fig. 3). Figures 4-7 illustrate the differences in stone visualization quality across the imaging modalities. Table 3. Comparison of diagnostic efficacy for common bile duct stones among various imaging techniques and their statistical differences compared with MRCP Conventional CT 40 keV Z eff (40 keV/ Z eff ) fusion MRCP P 1 P 2 P 3 P 4 AUC 0.742 0.816 0.696 0.872 0.912 ＜.001 .014 ＜.001 .233 (0.643-0.841) (0.728-0.903) (0.593-0.8) (0.797-0.948) (0.848-0.976) Sensitivity (%) 52.83 69.81 41.51 81.13 86.79 ＜.001 .064 ＜.001 .581 (39.66-65.62) (56.46-80.47) (29.26-54.90) (68.58-89.48) (75.16-93.43) Specificity (%) 95.56 93.33 97.78 93.33 95.56 1.000 1.000 1.000 1.000 (85.16-98.77) (82.14-97.71) (88.44-99.61) (82.14-97.71) (85.16-98.77) Accuracy (%) 72.45 80.61 67.35 86.73 90.82 ＜.001 .052 ＜.001 .481 (62.89-80.34) (71.68-87.23) (57.55-75.82) (78.50-92.17) (83.45-95.09) PPV (%) 93.33 92.50 95.65 93.48 95.83 – – – – (78.67-98.14) (80.12-97.40) (79.01-99.24) (82.47-97.68) (85.75-98.87) NPV (%) 63.24 72.41 58.67 80.77 86.00 – – – – (51.35-73.71) (60.07-82.09) (47.91-68.70) (68.73-88.94) (74.18-92.86) Note: AUC denotes the area under the receiver operating characteristic curve. AUC comparisons employed the DeLong test. Sensitivity, specificity, and accuracy comparisons used the McNemar test (paired categorical data). PPV and NPV were reported only as estimates with 95% confidence intervals, without hypothesis testing. P 1 indicates comparison of conventional CT and MRCP; P 2 indicates comparison of 40 keV and MRCP; P 3 indicates comparison of Z eff and MRCP; and P 4 indicates comparison of fusion images and MRCP. AUC, Area under the curve; MRCP, magnetic resonance cholangiopancreatography; NPV, negative predictive value; PPV, positive predictive value; Z eff , effective atomic number. 3.4 Clinical Utility Decision curve analysis demonstrated that, across the clinically relevant threshold probability range, the fusion images provided greater net benefit for detecting CBD stones than the other CT-based imaging modalities and were second only to MRCP (Fig. 8). 4. Discussion The results of this study indicated that 40-keV virtual single-energy images derived from DLCT combined with Z eff fusion imaging provided superior overall diagnostic efficacy compared with conventional CT and single-spectral-parameter imaging for detecting CBD stones. Notably, key diagnostic metrics approached those achieved with MRCP. These findings suggest that the integration of low-energy contrast enhancement and material composition discrimination on a CT platform may compensate for the inherent limitations of conventional CT in detecting CBD stones without substantially increasing examination complexity. From an imaging mechanism perspective, low-energy virtual single-energy images (e.g., 40 keV) enhance the photoelectric effect, thereby substantially increasing attenuation contrast between high-atomic-number materials and adjacent soft tissues or fluids. This results in improved contrast between stones and the surrounding bile [ 19 , 20 ] . In the evaluation of CBD stones, this contrast enhancement facilitates clearer delineation of stone margins, particularly for noncalcified or low-density stones. However, low-keV imaging is also associated with increased image noise, which may compromise overall image readability and diagnostic reliability when used alone [ 18 ] . Therefore, low-energy virtual single-energy imaging alone is insufficient to comprehensively address the complex diagnostic challenges associated with CBD stones. Z eff images provide compositional information on CBD stones by reflecting the attenuation characteristics of various substances across multiple energy conditions. Previous studies have demonstrated that Z eff and related material decomposition parameters offer potential advantages in distinguishing isodense bile from stones, particularly in cases that are challenging to identify using conventional CT [ 17 , 21 ] . However, Z eff images are limited by relatively low spatial resolution and subjective interpretability. When used in isolation, their diagnostic performance remains relatively constrained, preventing them from fully replacing conventional morphologic imaging. In this study, 40-keV virtual single-energy images were fused with Z eff images, allowing the simultaneous presentation of morphologic contrast enhancement and material composition discrimination on the same image plane. This fusion strategy preserves the advantage of low-keV imaging in delineating stone margins while incorporating compositional data provided by Z eff , thereby partially overcoming the limitations inherent to single-parameter imaging. This multiparametric synergistic imaging strategy aligns with the prevailing research trend in DLCT applications for biliary tract and related diseases, which aims to improve the overall detectability of complex lesions by integrating diverse spectral information [ 22 , 23 ] . Our findings further confirmed that, in the specific context of CBD stone detection, multiparametric fusion imaging surpasses single-spectral-parameter techniques, delivering more robust and stable diagnostic performance. From a clinical practice perspective, postprocessing of the 40-keV/ Z eff fusion images is performed using conventional DLCT data and does not require additional scans or specialized acquisition parameters. These images can be generated using standard postprocessing workstations. Compared with the need to sequentially switch between multiparameter images for comprehensive interpretation, fusion imaging streamlines the reading workflow to a certain extent, potentially reducing the cognitive burden of image interpretation rather than increasing the radiologist's workload. Furthermore, this method possesses superior reproducibility and scalability in routine clinical practice due to its short generation time and relatively standardized workflow, demonstrating potential utility in emergency settings or scenarios requiring rapid decision-making. However, its practical effectiveness across diverse clinical environments requires further validation through larger-scale, multicenter studies. From a clinical standpoint, in scenarios where MRCP is unavailable or rapid decision-making is required—such as during the initial assessment of patients with acute biliary symptoms, or in individuals with claustrophobia, metallic implants, or limited examination conditions—40-keV/ Z eff fusion imaging holds promise as a vital complementary modality bridging conventional CT and MRCP. By providing more reliable stone information beyond conventional CT, this fusion strategy offers stronger imaging evidence for determining whether to proceed with ERCP or LCBDE. Consequently, it may help reduce unnecessary invasive procedures and avoid treatment delays due to stone misdiagnosis [ 5 , 6 , 7 ] , thereby offering potential value in optimizing clinical resource allocation and minimizing patient risks. This study had certain limitations. First, this was a single-center retrospective study with a relatively limited sample size, leading to the possibility of selection bias. Second, the generation of fusion images depends on a specific vendor’s software platform, and their cross-device consistency and generalizability require further validation. Additionally, this study primarily focused on subjective image interpretation and diagnostic performance analysis, without in-depth exploration of quantitative parameters such as Z eff or low-keV images. Future studies should aim to confirm the robustness of these findings in a multicenter, large-sample setting and explore the potential incremental value of spectral parameters in diagnosing CBD stones by incorporating quantitative analysis methods. In summary, DLCT-based 40-keV/Z eff fusion imaging improves the detection of CBD stones, with diagnostic performance comparable to that of MRCP. This technique demonstrates substantial application potential in clinical decision-making and may serve as a vital adjunct between conventional CT and MRCP, thereby providing a more efficient and accessible solution for the radiologic evaluation of CBD stones. Declarations Acknowledgements The authors thank the staff of the Department of Radiology at Civil Aviation General Hospital for their assistance with data collection and technical support during this study. Funding This study received no external funding. Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Contributions YS and LB conceived and designed the study. YS and QJ collected the clinical data. ZXK and WYJ performed the image analysis. WJ provided technical support and guidance on spectral CT imaging. YS and WJ performed the statistical analysis. YS drafted the manuscript. LB supervised the study and critically revised the manuscript. All authors contributed to the manuscript and approved the final version. Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and was approved by the Ethics Committee of Civil Aviation General Hospital (Ethics No. KY2025-001-02). The requirement for written informed consent was waived due to the retrospective nature of the study. Consent for publication Not applicable. 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Dual-layer spectral detector computed tomography versus magnetic resonance cholangiopancreatography for biliary stones. Eur J Gastroenterol Hepatol . 2021;33(1):32-39. doi:10.1097/MEG.0000000000001832 Jagtap N, Kumar JK, Chavan R, et al. EUS versus MRCP to perform ERCP in patients with intermediate likelihood of choledocholithiasis: a randomised controlled trial. Gut . Published online February 10, 2022. doi:10.1136/gutjnl-2021-325080 Brendel JM, Dehdab R, Herrmann J, et al. Deep learning reconstruction for accelerated 3-D magnetic resonance cholangiopancreatography. Radiol Med . 2025;130(5):714-722. doi:10.1007/s11547-025-01987-z Xiao CH, Liu P, Zhang HH, et al. Incremental diagnostic value of virtual non-contrast dual-energy CT for the diagnosis of choledocholithiasis over conventional unenhanced CT. Diagn Interv Imaging . 2024;105(7-8):292-298. doi:10.1016/j.diii.2024.02.004 Zhang HH, Xiao CH, Yang F, Chen X, Liu P, Tan XZ. Diagnostic accuracy of dual-energy CT Rho/Z maps for detecting secondary choledocholithiasis. Int J Surg . 2025;111(10):6638-6646. doi:10.1097/JS9.0000000000002886 Zhong H, Huang Q, Zheng X, et al. Generation of virtual monoenergetic images at 40 keV of the upper abdomen and image quality evaluation based on generative adversarial networks. BMC Med Imaging . 2024;24(1):151. Published 2024 Jun 18. doi:10.1186/s12880-024-01331-3 Hamid S, Nasir MU, So A, Andrews G, Nicolaou S, Qamar SR. Clinical Applications of Dual-Energy CT. Korean J Radiol . 2021;22(6):970-982. doi:10.3348/kjr.2020.0996 Sodickson AD, Keraliya A, Czakowski B, Primak A, Wortman J, Uyeda JW. Dual energy CT in clinical routine: how it works and how it adds value. Emerg Radiol . 2021;28(1):103-117. doi:10.1007/s10140-020-01785-2 Parakh A, Lennartz S, An C, et al. Dual-Energy CT Images: Pearls and Pitfalls. Radiographics . 2021;41(1):98-119. doi:10.1148/rg.2021200102 Stański M, Michałowska I, Lemanowicz A, et al. Dual-Energy and Photon-Counting Computed Tomography in Vascular Applications-Technical Background and Post-Processing Techniques. Diagnostics (Basel) . 2024;14(12):1223. Published 2024 Jun 11. doi:10.3390/diagnostics14121223 Wang F, Wang S, Gong W. Negative gallbladder stones: Diagnostic advantages of dual-energy CT and analysis of a unique case. Asian J Surg . 2024;47(5):2347-2348. doi:10.1016/j.asjsur.2024.01.181 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 21 Apr, 2026 Editor invited by journal 25 Mar, 2026 Editor assigned by journal 25 Mar, 2026 Submission checks completed at journal 25 Mar, 2026 First submitted to journal 13 Mar, 2026 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. 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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-9118932","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631240039,"identity":"7640aa59-88e4-4127-8eab-e17742730f61","order_by":0,"name":"Shan Yang","email":"","orcid":"","institution":"Civil Aviation General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shan","middleName":"","lastName":"Yang","suffix":""},{"id":631240040,"identity":"b4116b8b-57f9-4397-9bac-2bb7600be241","order_by":1,"name":"Jun Qin","email":"","orcid":"","institution":"Civil Aviation General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Qin","suffix":""},{"id":631240041,"identity":"1adaedd7-039e-4379-a627-43df6d66701d","order_by":2,"name":"XiaoKun Zhang","email":"","orcid":"","institution":"Civil Aviation General Hospital","correspondingAuthor":false,"prefix":"","firstName":"XiaoKun","middleName":"","lastName":"Zhang","suffix":""},{"id":631240042,"identity":"cb3d9784-1665-4145-b156-fd8e627737d1","order_by":3,"name":"Yujie Wang","email":"","orcid":"","institution":"Civil Aviation General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yujie","middleName":"","lastName":"Wang","suffix":""},{"id":631240044,"identity":"2e0e2e58-1206-4810-964c-1259a72cc4af","order_by":4,"name":"Jing Wen","email":"","orcid":"","institution":"Philips Healthcare","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Wen","suffix":""},{"id":631240046,"identity":"9b1bab6e-ef4b-4a98-a904-8a660ec738cc","order_by":5,"name":"Bin Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACPmYGNoYEBgk5fvnHBx98MLCRI6iFDaLFwliyIS3ZcEZBmjFhLWDEUJG44UCOmTTPh8OJhLWw85g9eLhDInFmwwEzaRsD5gQG9sNHN+B3GI+5QeIZCeN+xoZk6xwDtjwGnrS0GwS0mEkktknIzmxmOHg7x4CnmEGCx4woLYwbjjE2SFsYSCQ2EKtFccMZZiZpBgMDYrSwlRsAtRhLzmBjNuwxSDBmI+QXfv7D2x7+bKuT45fg//jgx5//cvzsh4/h1YLFXtKUj4JRMApGwSjABgCPS0ETrD+h0QAAAABJRU5ErkJggg==","orcid":"","institution":"Civil Aviation General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-03-14 02:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9118932/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9118932/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108388782,"identity":"f119ae64-8876-42c7-a2a1-6c4b2fc873ed","added_by":"auto","created_at":"2026-05-04 06:43:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":201080,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient selection.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9118932/v1/2bbede0e60795f53feab9576.png"},{"id":108388783,"identity":"a45afe15-d268-4489-9349-5808118df926","added_by":"auto","created_at":"2026-05-04 06:43:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":90104,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for various imaging techniques in diagnosing common bile duct stones, comparing the overall diagnostic performance of MRCP, fusion, 40-keV, and \u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e imaging. Statistical comparisons of AUC values were performed using the DeLong test. AUC, Area under the curve; MRCP, magnetic resonance cholangiopancreatography; ROC, receiver operating characteristic; \u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e, effective atomic number.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9118932/v1/8cfab7d9dc95f3317ba1817d.png"},{"id":108492831,"identity":"5a6a919b-3d36-47e3-9a21-5459fe398526","added_by":"auto","created_at":"2026-05-05 09:58:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":185889,"visible":true,"origin":"","legend":"\u003cp\u003eConfusion matrix for 5 imaging techniques diagnosing common bile duct stones. True labels were determined by reference standard diagnosis. Predicted labels were derived from various imaging examinations. The bar on the right demonstrates the relative frequency in the study sample, with corresponding color coding displayed in the matrix. Absolute frequencies are demonstrated in the 4 squares. (A) Conventional CT; (B) 40 keV; (C) \u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e; (D) (40 keV/\u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e) fusion; and (E) MRCP imaging. MRCP, Magnetic resonance cholangiopancreatography; \u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e, effective atomic number.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9118932/v1/de944c4426993f41471e3d45.png"},{"id":108388784,"identity":"aa07a107-cb81-4868-8698-6b45406a6a3b","added_by":"auto","created_at":"2026-05-04 06:43:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":559713,"visible":true,"origin":"","legend":"\u003cp\u003eImages of common bile duct stones in a 70-year-old female patient. White arrows indicate the common bile duct stones. (A) Nonenhanced CT demonstrated a hypodense lesion in the lower common bile duct; (B) 40-keV image clearly demonstrated the stones; (C) The Z\u003csub\u003eeff\u003c/sub\u003e image enhanced the contrast between the stones and the surrounding bile; (D) fusion image displaying sharp stone margins with optimal visualization; and (E) MRCP confirmed the presence of stones. MRCP, Magnetic resonance cholangiopancreatography; \u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e, effective atomic number.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9118932/v1/742679a17a96105c5334b07a.png"},{"id":108493826,"identity":"184c6393-85f0-4a15-8dd8-9ccc507cbab7","added_by":"auto","created_at":"2026-05-05 10:01:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":445369,"visible":true,"origin":"","legend":"\u003cp\u003eImages of common bile duct stones in a 73-year-old male patient. White arrows indicate the common bile duct stones. (A) Nonenhanced CT failed to demonstrate definitive stones, indicating only a suspicious hyperdense area with insufficient diagnostic confidence; (B) 40-keV image clearly identified the stones; (C) \u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e image revealed enhanced contrast between stones and bile; (D) fusion image demonstrated stones clearly; and (E) MRCP confirmed the presence of common bile duct stones. MRCP, Magnetic resonance cholangiopancreatography; \u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e, effective atomic number.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9118932/v1/e30b87a329652c1d8ca41d8e.png"},{"id":108492316,"identity":"b1576162-14b2-4dc4-a841-995c6cf5af9b","added_by":"auto","created_at":"2026-05-05 09:57:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":446571,"visible":true,"origin":"","legend":"\u003cp\u003eImages of CBD stones in a 31-year-old woman. White arrows indicate the CBD stones. (A) Unenhanced conventional CT demonstrated no definite stones. (B) The 40-keV image revealed a focal hypodense lesion, but the finding was insufficient for a confident diagnosis. (C) The Z\u003csub\u003eeff \u003c/sub\u003eimage demonstrated faint contrast between the stone and bile. (D) The fusion image clearly demonstrated a distal CBD stone. (E) MRCP revealed a proximal CBD stone. MRCP had been performed 2 days before CT; interval stone migration may explain the positional discrepancy. The presence of a CBD stone was ultimately confirmed by common bile duct exploration. MRCP, magnetic resonance cholangiopancreatography; Z\u003csub\u003eeff\u003c/sub\u003e, effective atomic number.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9118932/v1/8b2169fc929f879b26f8f8cf.png"},{"id":108388788,"identity":"e8d82ee2-ae99-412a-be37-558b563d7690","added_by":"auto","created_at":"2026-05-04 06:43:45","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":440039,"visible":true,"origin":"","legend":"\u003cp\u003eImages of common bile duct stones in an 83-year-old male patient. White arrows indicate the common bile duct stones. (A) Nonenhanced CT demonstrated poorly defined stones; (B) 40-keV images clearly delineated the stones; (C) \u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e images revealed insufficient diagnostic confidence due to similar atomic numbers of bile and stones resulting in poor mass contrast; (D) fusion images clearly distinguished the stones; and (E) MRCP confirmed the presence of common bile duct stones. MRCP, Magnetic resonance cholangiopancreatography; Z\u003csub\u003eeff\u003c/sub\u003e, effective atomic number.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9118932/v1/783ced2561f676015dff85d0.png"},{"id":108388790,"identity":"9a21d7d4-4023-46ca-9001-1aee456e03ca","added_by":"auto","created_at":"2026-05-04 06:43:45","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":53162,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis for the entire study cohort. Across the threshold probability range from 0.1 to 0.9, the net benefit curves for all five imaging modalities remained above the horizontal axis, indicating potential clinical utility. Moreover, the net benefit curve of the fusion imaging model consistently remained above those of the other three CT-based imaging modalities (conventional CT, 40 keV, and Z\u003csub\u003eeff\u003c/sub\u003e), indicating superior clinical net benefit across a broad range of decision thresholds. DCA, decision curve analysis; MRCP, magnetic resonance cholangiopancreatography; Z\u003csub\u003eeff,\u003c/sub\u003e effective atomic number.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9118932/v1/018267a73ee57aeb58b5263b.png"},{"id":108804138,"identity":"012a1cd9-9df4-46c3-919e-4ec939c3ad7d","added_by":"auto","created_at":"2026-05-08 15:16:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2873201,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9118932/v1/7d4717e5-505f-4f6f-9a04-6c1615164bbe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Diagnostic Performance of Dual-Layer Detector Spectral CT–Derived Multiparametric Fusion Imaging for Detecting Common Bile Duct Stones","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCholedocholithiasis is a important manifestation within the spectrum of gallstone disease, particularly common in patients with gallbladder stones. It is estimated that approximately 10%-20% of patients with gallstones develop common bile duct stones, and the incidence increases with age \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Previous studies have demonstrated that approximately 70%-90% of patients with choledocholithiasis have secondary stones, resulting from gallstones migrating through the cystic duct from the gallbladder. The remaining have primary stones often associated with biliary tract infections, cholestasis, and anatomical abnormalities of the bile ducts \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Common bile duct (CBD) stones can lead to biliary obstruction and secondary infection, potentially progressing to acute cholangitis or biliary pancreatitis, which may be life-threatening in severe cases. Therefore, timely and accurate imaging evaluation is essential for formulating appropriate treatment strategies and minimizing the risk of severe complications \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. In clinical practice, confirming the presence of CBD stones directly influences decisions regarding invasive treatments, such as endoscopic retrograde cholangiopancreatography (ERCP) or laparoscopic common bile duct exploration (LCBDE) \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Although clinical prediction models based on clinical manifestations and laboratory indicators (e.g., American Society for Gastrointestinal Endoscopy [ASGE] guidelines) are commonly used for preliminary risk assessment, their diagnostic accuracy can vary considerably across various patient populations with acute biliary disease. Relying solely on clinical and laboratory criteria may therefore result in unnecessary invasive procedures or missed diagnoses of stones \u003csup\u003e[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Consequently, incorporating reliable and readily available imaging confirmation prior to invasive treatment decisions remains a crucial component of current clinical pathways \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. In radiology, CT offers advantages in emergency settings due to its rapid acquisition and wide accessibility; however, its ability to detect isodense, noncalcified, or small CBD stones is limited \u003csup\u003e[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. These stones often demonstrate attenuation values similar to the surrounding bile on routine CT images and have poorly defined margins, increasing the likelihood of missed diagnoses. In contrast, magnetic resonance cholangiopancreatography (MRCP) uses heavy T2-weighted sequences to clearly display biliary filling defects against a high-signal bile background, resulting in high sensitivity for the noninvasive assessment of CBD stones \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. However, the clinical application of MRCP is constrained by factors such as longer examination times, dependence on patient cooperation, susceptibility to motion artifacts, and restricted availability of resources. These limitations are particularly pronounced in emergency settings or resource-constrained settings, where diagnostic timeliness and reliability may be compromised \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Consequently, enhancing the diagnostic performance of CBD stones within the CT platform remains essential for optimizing clinical decision-making pathways. In recent years, dual-layer detector spectral CT (DLCT) has enabled the simultaneous acquisition of multi-energy information in a single scan, thereby providing a technical foundation for combined morphologic and compositional assessment of biliary tract lesions. Previous studies have demonstrated that virtual non-contrast images, 40-keV virtual single-energy imaging, and effective atomic number (\u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e) images can enhance visualization of CBD stones to a certain extent \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. However, the overall diagnostic performance of multiparametric fusion imaging in diagnosing CBD stones has not been systematically evaluated. Therefore, this study aimed to assess the diagnostic performance of DLCT-derived 40-keV/\u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e fusion imaging for detecting CBD stones, and to compare its performance with that of MRCP and conventional CT.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e2.1 Patients\u003c/p\u003e\n\u003cp\u003eUsing our hospital information system (HIS), we identified patients who underwent noncontrast spectral CT and MRCP of the upper abdomen for \u0026ldquo;gallbladder stones\u0026rdquo; and \u0026ldquo;CBD dilatation\u0026rdquo; between May 2024 and November 2025. All included patients subsequently underwent CBD exploration or ERCP within 30 days of imaging to confirm the presence of CBD stones. The inclusion criteria were as follows: (1) patients who underwent both noncontrast DLCT and MRCP for suspected gallbladder stones or CBD dilatation; (2) patients who received surgical CBD exploration or ERCP within 30 days after imaging; and (3) patients with complete clinical and imaging data. The exclusion criteria were as follows: (1) poor image quality; (2) concurrent biliary tract neoplastic disease; (3) prior biliary tract interventional procedures, including percutaneous transhepatic biliary drainage, endoscopic nasobiliary drainage, or biliary stent placement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.2 DLCT Scanning Protocol\u003c/p\u003e\n\u003cp\u003eNoncontrast upper abdominal scans were performed using a DLCT scanner (Spectral CT 7500; Philips Healthcare, The Netherlands). The scan parameters were as follows: tube voltage = 120 kV; tube current (controlled using DoseRight automated modulation technology) = 100-300 mAs; ReduceDose parameter = 18; collimator width = 128 \u0026times; 0.625 mm; slice thickness = 1 mm; slice spacing = 1 mm; pitch = 1.190; and tube rotation time = 0.4 seconds.\u003c/p\u003e\n\u003cp\u003e2.3 DLCT Postprocessing Analysis\u003c/p\u003e\n\u003cp\u003eThe acquired spectral CT raw data were imported into a postprocessing workstation (IntelliSpace Portal V12; Philips Healthcare) for image reconstruction. Reconstruction slice thickness and slice spacing were both set to 1 mm, generating 40-keV virtual monoenergetic images (VMI) and Z\u003csub\u003eeff\u003c/sub\u003e maps. The 40-keV images were then fused with the Z\u003csub\u003eeff\u003c/sub\u003e maps to produce 40-keV/Z\u003csub\u003eeff\u003c/sub\u003e fusion images for subsequent analysis. The fusion images were generated using the vendor-provided automatic fusion function on IntelliSpace Portal, with standardized display parameters applied consistently across all patients and readers.\u003c/p\u003e\n\u003cp\u003e2.4\u0026nbsp;MRCP Acquisition and Clinical Assessment\u003c/p\u003e\n\u003cp\u003eAll patients underwent MRCP at Civil Aviation General Hospital, Beijing, China, as part of routine clinical evaluation for suspected CBD stones. MRCP findings were obtained from the final clinical reports archived in the hospital information system and were analyzed as a comparator imaging modality rather than as the reference standard. Because MRCP served as a real-world clinical comparator in this study, its diagnostic performance was based on archived final clinical reports rather than on a separate blinded rereading under the study framework. The final diagnosis of CBD stones was established using ERCP or LCBDE findings as the reference standard. All MRCP examinations were performed according to the institutional routine protocol. Because MRCP results were derived from routine clinical reports generated on different scanners, no cross-device quantitative comparison was performed. The major acquisition parameters of the MRCP examinations are summarized in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1. MRCP scanning parameters for various machines\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"104%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eSSFSE\u0026ndash;T2WI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eFSE\u0026ndash;T2WI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e3D\u0026ndash;MRCP\u0026ndash;T1WI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e2D\u0026ndash;MRCP\u0026ndash;T2WI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3D\u0026ndash;MRCP\u0026ndash;T2WI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eEquipment model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003ePioneer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eVida\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;Pioneer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eVida\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003ePioneer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eVida\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003ePioneer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eVida\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003ePioneer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eVida\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eDirection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eCor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eCor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eAxi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eAxi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eAxi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eAxi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eRadial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eRadial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eCor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eCor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eTR (ms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026gt;2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026gt;4000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e4500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026gt;2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eTE (ms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e80-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e70-85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026gt;600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e560\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eFOV (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e42 \u0026times; 42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e41 \u0026times; 35.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e40 \u0026times; 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e40 \u0026times; 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e40 \u0026times; 38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e40 \u0026times; 32.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e40 \u0026times; 38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e30 \u0026times; 30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e40 \u0026times; 38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e32 \u0026times; 30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eST/ISG (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e5.0/5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e6.1/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e6/1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e6.1/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e4/\u0026ndash;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e6/\u0026ndash;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e5/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.8/\u0026ndash;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e2.0/\u0026ndash;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMatrix\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e320 \u0026times; 288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e314 \u0026times; 235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e320 \u0026times; 320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e288 \u0026times; 256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e320 \u0026times; 208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e288 \u0026times; 256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e288 \u0026times; 256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e384 \u0026times; 269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e255 \u0026times; 256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e320 \u0026times; 219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Pioneer refers to the GE Signa Pioneer system, and Vida refers to the Siemens Vida system. For the 3D\u0026ndash;MRCP\u0026ndash;T1WI sequence, the Pioneer system primarily used the LAVA sequence, whereas the Vida system primarily used the VIBE sequence.\u003c/p\u003e\n\u003cp\u003eAxi, axial; Cor, coronal; 3D, three-dimensional; FOV, field of view; FSE, fast spin-echo; ISG, interslice gap; LAVA, liver acquisition with volume acceleration; MRCP, magnetic resonance cholangiopancreatography; SSFSE, single-shot fast spin-echo; ST, slice thickness; TE, echo time; TR, repetition time; T1WI, T1-weighted imaging; T2WI, T2-weighted imaging; VIBE, volumetric interpolated breath-hold examination.\u003c/p\u003e\n\u003cp\u003e2.5 Image Analysis\u003c/p\u003e\n\u003cp\u003eThis study used an independent blinded subjective image evaluation protocol to assess the diagnostic performance of different CT image sets for CBD stones. All CT image sets were anonymized and independently reviewed by two radiologists (one attending radiologist with 7 years of diagnostic experience and one associate chief radiologist with 10 years of diagnostic experience), both of whom were blinded to the MRCP results and reference standard findings. Each radiologist independently assessed four CT image sets: conventional CT images, 40-keV virtual monoenergetic images, Zeff images, and fused 40-keV/Zeff images. For each image set, the readers determined, on a per-patient basis, whether CBD stones were present or absent. Equivocal findings, such as \u0026ldquo;possible stone\u0026rdquo; or \u0026ldquo;high-density shadow,\u0026rdquo; were classified as negative for dichotomous diagnostic analysis. Window width and level could be adjusted freely during interpretation, and multiplanar reconstructions were allowed when necessary. Interobserver agreement was assessed for these four CT image sets and is presented in Section 3.2.\u003c/p\u003e\n\u003cp\u003e2.6 Reference Standard\u003c/p\u003e\n\u003cp\u003eIn this study, the presence of CBD stones was confirmed using LCBDE or ERCP as the diagnostic reference standard. The results of DLCT and MRCP imaging were compared against this standard to evaluate diagnostic accuracy. Diagnostic classifications were defined as follows:\u003c/p\u003e\n\u003cp\u003e- A true positive was recorded when imaging suggested the presence of stones, which was confirmed by the reference standard.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- A false positive was recorded when imaging suggested the presence of stones, but the reference standard confirmed their absence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- A true negative was recorded when imaging indicated no evidence of stones, and the reference standard confirmed their absence.\u003c/p\u003e\n\u003cp\u003e- A false negative was recorded when imaging indicated no stones, despite positive confirmation by the reference standard.\u003c/p\u003e\n\u003cp\u003e2.7 Statistical Analysis\u003c/p\u003e\n\u003cp\u003eThis study used a retrospective paired diagnostic design. Continuous variables are presented as mean \u0026plusmn; standard deviation, and categorical variables as counts and percentages. Using ERCP or LCBDE findings as the reference standard, the sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of the different imaging modalities were calculated, with 95% confidence intervals. Diagnostic performance was further assessed using receiver operating characteristic (ROC) curve analysis, and the area under the curve (AUC) values were compared using the DeLong test. Because all imaging modalities were applied to the same patients, paired proportions, including sensitivity, specificity, and accuracy, were compared using the McNemar test. Interobserver consistency across the four CT image sets was additionally assessed using the intraclass correlation coefficient (ICC), with higher values indicating better consistency between the two readers. This analysis was used to provide a complementary assessment of inter-reader consistency across the four CT image sets. Decision curve analysis (DCA) was performed to assess the clinical net benefit of the different imaging modalities across a range of threshold probabilities. Statistical analyses were performed using SPSS version 27.0 (IBM Corp., Armonk, NY, USA), and DCA was performed using R software (R Foundation for Statistical Computing, Vienna, Austria). A two-sided P value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1 Clinical Data\u003c/p\u003e\n\u003cp\u003eA total of 121 patients with gallbladder stones and CBD dilatation underwent abdominal noncontrast DLCT. Among these, 98 patients met the inclusion criteria. The study cohort comprised 62 male and 36 female patients, with a mean age of 56.8 \u0026plusmn; 17.7 years (Fig. 1). Among the included patients, 30 underwent ERCP for stone extraction, 63 underwent laparoscopic cholecystectomy and laparoscopic exploration, and 5 underwent open cholecystectomy. The choice of treatment modality was determined based on clinical presentation, stone characteristics, and multidisciplinary team assessment according to standard clinical practice. According to the reference standard findings from ERCP or LCBDE, CBD stones were confirmed in 53 patients. The clinical characteristics of the patients are summarized in Table 2.\u003c/p\u003e\n\u003cp\u003eTable 2. Clinical data of participants (\u003cem\u003en\u003c/em\u003e = 98)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"501\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneral information (\u003cem\u003en\u003c/em\u003e = 98)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e62 (63.3%)\u003c/p\u003e\n \u003cp\u003e36 (36.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eAge (year)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e56.8 \u0026plusmn; 17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical manifestations (\u003cem\u003en\u003c/em\u003e = 98)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eAbdominal pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e91 (92.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eFever \u0026gt;38\u0026nbsp;\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e38 (38.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eNausea/Vomiting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e43 (43.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdmission diagnosis (\u003cem\u003en\u003c/em\u003e = 98)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eCholecystitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e77 (78.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eCholangitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e35 (35.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eBiliary pancreatitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e13 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnosis and treatment method\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(\u003cem\u003en\u003c/em\u003e = 98)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eLaparoscopic-related surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e63 (64.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eERCP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e30 (30.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eOpen surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e5 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eStone-related characteristics in the overall cohort (\u003cem\u003en\u003c/em\u003e = 98)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eSecondary stones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e45 (45.9%)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003ePrimary stones\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e8 (8.2%)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eMaximum stone diameter (mm)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e10.2 \u0026plusmn; 4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eStone diameter \u0026ge;9 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e24 (24.5%)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eStone diameter \u0026lt;9 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e29 (29.6%)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 501px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbnormal laboratory findings (\u003cem\u003en\u003c/em\u003e = 98)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eGGT \u0026gt; 60 U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e61 (62.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eALP \u0026gt; 125 U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e52 (53.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eDBil \u0026gt; 6.8 \u0026mu;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e48 (49.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eALT \u0026gt; 50 U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e35 (35.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eWBC \u0026gt; 9.5 \u0026times; 10⁹/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e22 (22.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eNEUT% \u0026gt; 75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e31 (31.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Quantitative variables are expressed as mean \u0026plusmn; standard deviation. Qualitative variables are presented as case numbers, with percentages in parentheses. \u0026sup1; These percentages represent the proportion of the total study population (n = 98). Among patients with CBD stones (n = 53), 45 (84.9%) had secondary stones and 8 (15.1%) had primary stones; 24 (45.3%) had a maximum stone diameter \u0026ge; 9 mm and 29 (54.7%) had a diameter \u0026lt; 9 mm.\u003c/p\u003e\n\u003cp\u003eALP, Alkaline phosphatase; ALT\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003ealanine transaminase; DBiL, direct bilirubin; ERCP, endoscopic retrograde cholangiopancreatography; GGT, gamma-glutamyl transpeptidase; NEUT%, neutrophil percentage; WBC, white blood cell.\u003c/p\u003e\n\u003cp\u003e3.2 \u0026nbsp;Interobserver Consistency Analysis\u003c/p\u003e\n\u003cp\u003eInterobserver consistency between the two readers was highest for the 40-keV/Zeff fusion images (ICC = 0.883; 95% CI: 0.842\u0026ndash;0.916), indicating excellent consistency. The 40-keV images also showed high interobserver consistency (ICC = 0.844; 95% CI: 0.789\u0026ndash;0.888). In contrast, conventional CT and Zeff images showed relatively lower consistency, with ICC values of 0.756 (95% CI: 0.668\u0026ndash;0.823) and 0.749 (95% CI: 0.661\u0026ndash;0.822), respectively. Overall, the fusion images showed the highest inter-reader consistency among the four CT image sets.\u003c/p\u003e\n\u003cp\u003e3.3 \u0026nbsp;Diagnostic Performance Analysis\u003c/p\u003e\n\u003cp\u003eMRCP demonstrated the highest overall diagnostic performance, with an AUC of 0.912 (95% CI: 0.848-0.976), whereas the fusion images showed comparable performance, with an AUC of 0.872 (95% CI: 0.797-0.948). In terms of sensitivity, 40-keV virtual single-energy imaging (69.81%) outperformed conventional CT (52.83%). All modalities demonstrated satisfactory specificity, with MRCP achieving the highest value (95.56%), followed by fusion imaging (93.33%). In terms of overall accuracy, both MRCP and fusion images outperformed conventional CT, 40-keV, and Z\u003csub\u003eeff\u003c/sub\u003e images. Similarly, MRCP and fusion images demonstrated superior PPV and NPV values. Statistical comparisons showed that conventional CT and Z\u003csub\u003eeff\u003c/sub\u003e images had significantly lower AUC, sensitivity, and accuracy than MRCP (all P \u0026lt; 0.05). The 40-keV images showed a significantly lower AUC than MRCP (P = 0.014), whereas the differences in sensitivity (P = 0.064) and accuracy (P = 0.052) were not statistically significant. The fusion images showed no significant differences from MRCP in AUC, sensitivity, specificity, or accuracy (all P \u0026gt; 0.05) (Table 3 and Fig. 2). Among the 53 reference-positive patients, MRCP correctly identified 46 cases, corresponding to a sensitivity of 86.79%. Fusion imaging correctly identified 43 of these 53 patients, whereas 40-keV imaging detected 37 cases, and conventional CT and Z\u003csub\u003eeff\u003c/sub\u003e imaging detected 28 and 22 cases, respectively (Fig. 3). Figures 4-7 illustrate the differences in stone visualization quality across the imaging modalities.\u003c/p\u003e\n\u003cp\u003eTable 3. Comparison of diagnostic efficacy for common bile duct stones among various imaging techniques and their statistical differences compared with MRCP\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"818\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eConventional CT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40 keV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e(40 keV/\u003c/strong\u003e\u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e\u003cstrong\u003e) fusion\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMRCP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e＜.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e＜.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e.233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.643-0.841)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.728-0.903)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.593-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.797-0.948)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.848-0.976)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e＜.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e＜.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e.581\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e(39.66-65.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(56.46-80.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(29.26-54.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(68.58-89.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(75.16-93.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e97.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e(85.16-98.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(82.14-97.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(88.44-99.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(82.14-97.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(85.16-98.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eAccuracy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e＜.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e＜.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e.481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e(62.89-80.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(71.68-87.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(57.55-75.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(78.50-92.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(83.45-95.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003ePPV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e(78.67-98.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(80.12-97.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(79.01-99.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(82.47-97.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(85.75-98.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eNPV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e63.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e(51.35-73.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(60.07-82.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(47.91-68.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(68.73-88.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(74.18-92.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: AUC denotes the area under the receiver operating characteristic curve. AUC comparisons employed the DeLong test. Sensitivity, specificity, and accuracy comparisons used the McNemar test (paired categorical data). PPV and NPV were reported only as estimates with 95% confidence intervals, without hypothesis testing. \u003cem\u003eP\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e indicates comparison of conventional CT and MRCP; \u003cem\u003eP\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e indicates comparison of 40 keV and MRCP; \u003cem\u003eP\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e indicates comparison of \u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e and MRCP; and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e indicates comparison of fusion images and MRCP.\u003c/p\u003e\n\u003cp\u003eAUC, Area under the curve; MRCP, magnetic resonance cholangiopancreatography; NPV, negative predictive value; PPV, positive predictive value; \u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e, effective atomic number.\u003c/p\u003e\n\u003cp\u003e3.4 Clinical Utility\u003c/p\u003e\n\u003cp\u003eDecision curve analysis demonstrated that, across the clinically relevant threshold probability range, the fusion images provided greater net benefit for detecting CBD stones than the other CT-based imaging modalities and were second only to MRCP (Fig. 8).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe results of this study indicated that 40-keV virtual single-energy images derived from DLCT combined with \u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e fusion imaging provided superior overall diagnostic efficacy compared with conventional CT and single-spectral-parameter imaging for detecting CBD stones. Notably, key diagnostic metrics approached those achieved with MRCP. These findings suggest that the integration of low-energy contrast enhancement and material composition discrimination on a CT platform may compensate for the inherent limitations of conventional CT in detecting CBD stones without substantially increasing examination complexity.\u003c/p\u003e \u003cp\u003eFrom an imaging mechanism perspective, low-energy virtual single-energy images (e.g., 40 keV) enhance the photoelectric effect, thereby substantially increasing attenuation contrast between high-atomic-number materials and adjacent soft tissues or fluids. This results in improved contrast between stones and the surrounding bile \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. In the evaluation of CBD stones, this contrast enhancement facilitates clearer delineation of stone margins, particularly for noncalcified or low-density stones. However, low-keV imaging is also associated with increased image noise, which may compromise overall image readability and diagnostic reliability when used alone \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Therefore, low-energy virtual single-energy imaging alone is insufficient to comprehensively address the complex diagnostic challenges associated with CBD stones.\u003c/p\u003e \u003cp\u003e \u003cem\u003eZ\u003c/em\u003e \u003csub\u003eeff\u003c/sub\u003e images provide compositional information on CBD stones by reflecting the attenuation characteristics of various substances across multiple energy conditions. Previous studies have demonstrated that \u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e and related material decomposition parameters offer potential advantages in distinguishing isodense bile from stones, particularly in cases that are challenging to identify using conventional CT \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. However, \u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e images are limited by relatively low spatial resolution and subjective interpretability. When used in isolation, their diagnostic performance remains relatively constrained, preventing them from fully replacing conventional morphologic imaging.\u003c/p\u003e \u003cp\u003eIn this study, 40-keV virtual single-energy images were fused with \u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e images, allowing the simultaneous presentation of morphologic contrast enhancement and material composition discrimination on the same image plane. This fusion strategy preserves the advantage of low-keV imaging in delineating stone margins while incorporating compositional data provided by \u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e, thereby partially overcoming the limitations inherent to single-parameter imaging. This multiparametric synergistic imaging strategy aligns with the prevailing research trend in DLCT applications for biliary tract and related diseases, which aims to improve the overall detectability of complex lesions by integrating diverse spectral information \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Our findings further confirmed that, in the specific context of CBD stone detection, multiparametric fusion imaging surpasses single-spectral-parameter techniques, delivering more robust and stable diagnostic performance.\u003c/p\u003e \u003cp\u003eFrom a clinical practice perspective, postprocessing of the 40-keV/\u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e fusion images is performed using conventional DLCT data and does not require additional scans or specialized acquisition parameters. These images can be generated using standard postprocessing workstations. Compared with the need to sequentially switch between multiparameter images for comprehensive interpretation, fusion imaging streamlines the reading workflow to a certain extent, potentially reducing the cognitive burden of image interpretation rather than increasing the radiologist's workload. Furthermore, this method possesses superior reproducibility and scalability in routine clinical practice due to its short generation time and relatively standardized workflow, demonstrating potential utility in emergency settings or scenarios requiring rapid decision-making. However, its practical effectiveness across diverse clinical environments requires further validation through larger-scale, multicenter studies.\u003c/p\u003e \u003cp\u003eFrom a clinical standpoint, in scenarios where MRCP is unavailable or rapid decision-making is required\u0026mdash;such as during the initial assessment of patients with acute biliary symptoms, or in individuals with claustrophobia, metallic implants, or limited examination conditions\u0026mdash;40-keV/\u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e fusion imaging holds promise as a vital complementary modality bridging conventional CT and MRCP. By providing more reliable stone information beyond conventional CT, this fusion strategy offers stronger imaging evidence for determining whether to proceed with ERCP or LCBDE. Consequently, it may help reduce unnecessary invasive procedures and avoid treatment delays due to stone misdiagnosis \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, thereby offering potential value in optimizing clinical resource allocation and minimizing patient risks.\u003c/p\u003e \u003cp\u003eThis study had certain limitations. First, this was a single-center retrospective study with a relatively limited sample size, leading to the possibility of selection bias. Second, the generation of fusion images depends on a specific vendor\u0026rsquo;s software platform, and their cross-device consistency and generalizability require further validation. Additionally, this study primarily focused on subjective image interpretation and diagnostic performance analysis, without in-depth exploration of quantitative parameters such as \u003cem\u003eZ\u003c/em\u003e\u003csub\u003eeff\u003c/sub\u003e or low-keV images. Future studies should aim to confirm the robustness of these findings in a multicenter, large-sample setting and explore the potential incremental value of spectral parameters in diagnosing CBD stones by incorporating quantitative analysis methods.\u003c/p\u003e \u003cp\u003eIn summary, DLCT-based 40-keV/Z\u003csub\u003eeff\u003c/sub\u003e fusion imaging improves the detection of CBD stones, with diagnostic performance comparable to that of MRCP. This technique demonstrates substantial application potential in clinical decision-making and may serve as a vital adjunct between conventional CT and MRCP, thereby providing a more efficient and accessible solution for the radiologic evaluation of CBD stones.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors thank the staff of the Department of Radiology at Civil Aviation General Hospital for their assistance with data collection and technical support during this study.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study received no external funding.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eContributions\u003c/p\u003e\n\u003cp\u003eYS and LB conceived and designed the study. YS and QJ collected the clinical data. ZXK and WYJ performed the image analysis. WJ provided technical support and guidance on spectral CT imaging. YS and WJ performed the statistical analysis. YS drafted the manuscript. LB supervised the study and critically revised the manuscript. All authors contributed to the manuscript and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and was approved by the Ethics Committee of Civil Aviation General Hospital (Ethics No. KY2025-001-02). The requirement for written informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eWen Jing is affiliated with Philips Healthcare, which manufactures spectral CT-related technology. The other authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eUnalp-Arida A, Ruhl CE. Increasing gallstone disease prevalence and associations with gallbladder and biliary tract mortality in the US. Hepatology. 2023;77(6):1882-1895. doi:10.1097/HEP.0000000000000264\u003c/li\u003e\n \u003cli\u003eFujita N, Yasuda I, Endo I, et al. Evidence-based clinical practice guidelines for cholelithiasis 2021. J Gastroenterol. 2023;58(9):801-833. doi:10.1007/s00535-023-02014-6\u003c/li\u003e\n \u003cli\u003eLiu Q, Zheng L, Wang Y, et al. Primary choledocholithiasis occurrence and recurrence is synergetcally modulated by the bile microbiome and metabolome alternations. Life Sci. 2023;331:122073. doi:10.1016/j.lfs.2023.122073\u003c/li\u003e\n \u003cli\u003eLi S, Guizzetti L, Ma C, et al. Epidemiology and outcomes of choledocholithiasis and cholangitis in the United States: trends and urban-rural variations. \u003cem\u003eBMC Gastroenterol\u003c/em\u003e. 2023;23(1):254. Published 2023 Jul 27. doi:10.1186/s12876-023-02868-3\u003c/li\u003e\n \u003cli\u003eNassar AHM, Ng HJ, Katbeh T, Cannings E. Conventional Surgical Management of Bile Duct Stones: A Service Model and Outcomes of 1318 Laparoscopic Explorations. \u003cem\u003eAnn Surg\u003c/em\u003e. 2022;276(5):e493-e501. doi:10.1097/SLA.0000000000004680\u003c/li\u003e\n \u003cli\u003eHasak S, McHenry S, Busebee B, et al. Validation of choledocholithiasis predictors from the \u0026quot;2019 ASGE Guideline for the role of endoscopy in the evaluation and management of choledocholithiasis.\u0026quot;. \u003cem\u003eSurg Endosc\u003c/em\u003e. 2022;36(6):4199-4206. doi:10.1007/s00464-021-08752-z\u003c/li\u003e\n \u003cli\u003eWang L, Mirzaie S, Dunnsiri T, et al. Systematic review and meta-analysis of the 2010 ASGE non-invasive predictors of choledocholithiasis and comparison to the 2019 ASGE predictors. \u003cem\u003eClin J Gastroenterol\u003c/em\u003e. 2022;15(2):286-300. doi:10.1007/s12328-021-01575-4\u003c/li\u003e\n \u003cli\u003eTintara S, Shah I, Yakah W, et al. Evaluating the accuracy of American Society for Gastrointestinal Endoscopy guidelines in patients with acute gallstone pancreatitis with choledocholithiasis. \u003cem\u003eWorld J Gastroenterol\u003c/em\u003e. 2022;28(16):1692-1704. doi:10.3748/wjg.v28.i16.1692\u003c/li\u003e\n \u003cli\u003eKwok HCK, Falconer FRM, Vandal AC, Hill AG, Maccormick AD. Performance of Diagnostic Guidelines in the Evaluation of Choledocholithiasis in Patients With Acute Biliary Presentation: A Systematic Review and Meta-Analysis. \u003cem\u003eWorld J Surg\u003c/em\u003e. 2025;49(8):2153-2165. doi:10.1002/wjs.12684\u003c/li\u003e\n \u003cli\u003eLopes Vendrami C, Thorson DL, Borhani AA, et al. Imaging of Biliary Tree Abnormalities. \u003cem\u003eRadiographics\u003c/em\u003e. 2024;44(8):e230174. doi:10.1148/rg.230174\u003c/li\u003e\n \u003cli\u003ePatel N, Jensen KK, Shaaban AM, Korngold E, Foster BR. Multimodality Imaging of Cholecystectomy Complications. \u003cem\u003eRadiographics\u003c/em\u003e. 2022;42(5):1303-1319. doi:10.1148/rg.210106\u003c/li\u003e\n \u003cli\u003eAfzalpurkar S, Giri S, Kasturi S, Ingawale S, Sundaram S. Magnetic resonance cholangiopancreatography versus endoscopic ultrasound for diagnosis of choledocholithiasis: an updated systematic review and meta-analysis. \u003cem\u003eSurg Endosc\u003c/em\u003e. 2023;37(4):2566-2573. doi:10.1007/s00464-022-09744-3\u003c/li\u003e\n \u003cli\u003eSaito H, Iwagoi Y, Noda K, et al. Dual-layer spectral detector computed tomography versus magnetic resonance cholangiopancreatography for biliary stones. \u003cem\u003eEur J Gastroenterol Hepatol\u003c/em\u003e. 2021;33(1):32-39. doi:10.1097/MEG.0000000000001832\u003c/li\u003e\n \u003cli\u003eJagtap N, Kumar JK, Chavan R, et al. EUS versus MRCP to perform ERCP in patients with intermediate likelihood of choledocholithiasis: a randomised controlled trial. \u003cem\u003eGut\u003c/em\u003e. Published online February 10, 2022. doi:10.1136/gutjnl-2021-325080\u003c/li\u003e\n \u003cli\u003eBrendel JM, Dehdab R, Herrmann J, et al. Deep learning reconstruction for accelerated 3-D magnetic resonance cholangiopancreatography. \u003cem\u003eRadiol Med\u003c/em\u003e. 2025;130(5):714-722. doi:10.1007/s11547-025-01987-z\u003c/li\u003e\n \u003cli\u003eXiao CH, Liu P, Zhang HH, et al. Incremental diagnostic value of virtual non-contrast dual-energy CT for the diagnosis of choledocholithiasis over conventional unenhanced CT. \u003cem\u003eDiagn Interv Imaging\u003c/em\u003e. 2024;105(7-8):292-298. doi:10.1016/j.diii.2024.02.004\u003c/li\u003e\n \u003cli\u003eZhang HH, Xiao CH, Yang F, Chen X, Liu P, Tan XZ. Diagnostic accuracy of dual-energy CT Rho/Z maps for detecting secondary choledocholithiasis. \u003cem\u003eInt J Surg\u003c/em\u003e. 2025;111(10):6638-6646. doi:10.1097/JS9.0000000000002886\u003c/li\u003e\n \u003cli\u003eZhong H, Huang Q, Zheng X, et al. Generation of virtual monoenergetic images at 40 keV of the upper abdomen and image quality evaluation based on generative adversarial networks. \u003cem\u003eBMC Med Imaging\u003c/em\u003e. 2024;24(1):151. Published 2024 Jun 18. doi:10.1186/s12880-024-01331-3\u003c/li\u003e\n \u003cli\u003eHamid S, Nasir MU, So A, Andrews G, Nicolaou S, Qamar SR. Clinical Applications of Dual-Energy CT. \u003cem\u003eKorean J Radiol\u003c/em\u003e. 2021;22(6):970-982. doi:10.3348/kjr.2020.0996\u003c/li\u003e\n \u003cli\u003eSodickson AD, Keraliya A, Czakowski B, Primak A, Wortman J, Uyeda JW. Dual energy CT in clinical routine: how it works and how it adds value. \u003cem\u003eEmerg Radiol\u003c/em\u003e. 2021;28(1):103-117. doi:10.1007/s10140-020-01785-2\u003c/li\u003e\n \u003cli\u003eParakh A, Lennartz S, An C, et al. Dual-Energy CT Images: Pearls and Pitfalls. \u003cem\u003eRadiographics\u003c/em\u003e. 2021;41(1):98-119. doi:10.1148/rg.2021200102\u003c/li\u003e\n \u003cli\u003eStański M, Michałowska I, Lemanowicz A, et al. Dual-Energy and Photon-Counting Computed Tomography in Vascular Applications-Technical Background and Post-Processing Techniques. \u003cem\u003eDiagnostics (Basel)\u003c/em\u003e. 2024;14(12):1223. Published 2024 Jun 11. doi:10.3390/diagnostics14121223\u003c/li\u003e\n \u003cli\u003eWang F, Wang S, Gong W. Negative gallbladder stones: Diagnostic advantages of dual-energy CT and analysis of a unique case. \u003cem\u003eAsian J Surg\u003c/em\u003e. 2024;47(5):2347-2348. doi:10.1016/j.asjsur.2024.01.181\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"common bile duct stones, diagnostic performance, dual-layer detector spectral CT, fusion imaging, magnetic resonance cholangiopancreatography","lastPublishedDoi":"10.21203/rs.3.rs-9118932/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9118932/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003cbr\u003e\nCommon bile duct (CBD) stones are a common cause of biliary obstruction and may lead to serious complications if not diagnosed accurately. Although magnetic resonance cholangiopancreatography (MRCP) and conventional computed tomography (CT) are widely used in clinical practice, conventional CT has limited sensitivity for small, non-calcified, or isoattenuating stones. Dual-layer detector spectral CT (DLCT) provides multiparametric information that may improve stone detection. This study aimed to compare the diagnostic performance of DLCT-derived 40-keV/Z\u003csub\u003eeff\u003c/sub\u003e fusion images with that of MRCP, conventional CT, and single-parameter DLCT images for detecting CBD stones.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003cbr\u003e\nThis retrospective study was conducted at Civil Aviation General Hospital, Beijing, China. A total of 98 patients who underwent non-contrast DLCT and MRCP between May 2024 and November 2025 for suspected gallbladder stones or CBD dilatation were included. Laparoscopic common bile duct exploration (LCBDE) or endoscopic retrograde cholangiopancreatography (ERCP) served as the reference standard. Four image datasets were reconstructed from the DLCT data: conventional CT images, 40-keV virtual monoenergetic images, effective atomic number (Z\u003csub\u003eeff\u003c/sub\u003e) images, and 40-keV/\u003csub\u003eZeff\u003c/sub\u003e fusion images. Two radiologists blinded to the MRCP reports independently reviewed all CT datasets for the presence of CBD stones. MRCP findings were obtained from the final clinical reports archived in the hospital information system and were analyzed as a comparator imaging modality. Diagnostic performance was assessed on a per-patient basis. Receiver operating characteristic (ROC) curve analysis was performed to calculate the area under the curve (AUC), and AUCs were compared using the DeLong test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cbr\u003e\nAmong the 98 included patients, 53 were confirmed to have CBD stones, including 45 (84.9%) with secondary stones and 8 (15.1%) with primary stones. Conventional CT achieved an AUC of 0.742, with a sensitivity of 52.8% (28/53) and a specificity of 95.6% (43/45). The 40-keV/Z\u003csub\u003eeff\u003c/sub\u003e fusion images improved diagnostic performance, achieving an AUC of 0.872 and a sensitivity of 81.1% (43/53), while maintaining a specificity of 93.3% (42/45). MRCP achieved an AUC of 0.912, with a sensitivity of 86.8% (46/53) and a specificity of 95.6% (43/45). The diagnostic performance of the fusion images was comparable to that of MRCP (AUC, 0.872 vs 0.912; P = 0.233).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003cbr\u003e\nDLCT-derived 40-keV/Z\u003csub\u003eeff\u003c/sub\u003e fusion imaging improves the detection of CBD stones and demonstrates diagnostic performance comparable to that of MRCP. This technique may serve as a valuable adjunct imaging modality in clinical practice.\u003c/p\u003e","manuscriptTitle":"Comparative Diagnostic Performance of Dual-Layer Detector Spectral CT–Derived Multiparametric Fusion Imaging for Detecting Common Bile Duct Stones","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 06:43:40","doi":"10.21203/rs.3.rs-9118932/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-21T11:36:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-25T12:29:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-25T10:49:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-25T10:49:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2026-03-14T01:56:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"616df7c8-6d39-407a-985e-590776a5f303","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T06:43:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 06:43:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9118932","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9118932","identity":"rs-9118932","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}