{"paper_id":"0e44e085-934a-4df7-bdc6-997d93ee8f87","body_text":"Comparison of Reduced FOV Diffusion-weighted Imaging of Rectal Cancer at 5.0T ultra- high field versus 3.0T MRI: Image Quality and Histopathological T Staging | 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 Comparison of Reduced FOV Diffusion-weighted Imaging of Rectal Cancer at 5.0T ultra- high field versus 3.0T MRI: Image Quality and Histopathological T Staging Xue Dong, Dongmei Shi, Zhiwei Qin, Huifang Yong, Qiufeng Yin BSc, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7533445/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Dec, 2025 Read the published version in BMC Medical Imaging → Version 1 posted 10 You are reading this latest preprint version Abstract Purpose To compare image quality (IQ) of reduced field-of-view (rFOV) DWI for rectal cancer at 5.0 T compared with 3.0T and determine whether tumor ADC values are correlated with histopathological T staging. Materials and Methods In a prospective cohort, 36 patients diagnosed with rectal cancer underwent MRI scans on both 3.0T and 5.0T systems. Two experienced radiologists separately evaluated the subjective and objective IQ parameters. Objective IQ metrics were statistically analyzed utilizing paired t-tests. Subjective assessments were compared utilizing the Wilcoxon signed-rank test. Tumor ADC values obtained at the two magnetic field strengths were further compared, and their association with histopathological T stage was examined through Spearman’s rank correlation. Results Objective measures demonstrated evidently improved IQ on 5.0T rFOV DWI relative to 3.0T (all P < 0.001). Subjective evaluations confirmed superior image clarity, lesion delineation, and overall diagnostic confidence on the 5.0T platform ( P < 0.001). The two systems demonstrated comparable performance with respect to image artifacts and geometric distortions, showing no meaningful statistical divergence. However, the mean tumor ADC values differed significantly between 3.0T and 5.0T imaging ( P < 0.001). A notable inverse correlation was identified between ADC values and histopathological T stage at both field strengths ( P < 0.001). Conclusion rFOV DWI at 5.0T offers enhanced IQ and improved tumor visualization relative to 3.0T. The mean tumor ADC values were significantly different at 3.0T and 5.0T, which could be utilized for assessing histopathological T staging of rectal cancer. Rectal Cancer reduced field-of-view DWI 5.0T MRI T staging Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Colorectal cancer ranks as the third most frequently diagnosed malignancy globally, with approximately 30–35% of cases originating in the rectum [ 1 ] . Owing to its exceptional soft tissue resolution and capacity to capture both anatomical and functional details, magnetic resonance imaging (MRI) has emerged as the preferred modality for preoperative assessment of rectal cancer. It plays a vital role in assessing tumor extent, evaluating lymphovascular involvement, tracking response to neoadjuvant treatment, and recognizing postoperative local recurrence [ 2 ] . Diffusion-weighted imaging (DWI), a functional MRI technique, captures differences in the diffusion behavior of water molecules within tissues, thereby providing insight into microstructural and cellular characteristics [ 3 , 4 ] . A key quantitative parameter derived from DWI is the apparent diffusion coefficient (ADC), which reflects the degree of water molecule mobility in tissue and serves as a valuable imaging biomarker. Previous studies have established the clinical utility of ADC measurements for identifying and characterizing pathological lesions, evaluating organ function, and monitoring therapeutic outcomes, particularly at commonly used field strengths such as 1.5T and 3.0T [ 5 – 7 ] . In rectal cancer specifically, ADC values have shown potential as a non-invasive tool for stratifying tumors by histological T stage and tumor grade [ 8 – 11 ] . Traditional pelvic DWI techniques are often challenged by inherent limitations such as suboptimal spatial resolution, chemical shift artifacts, geometric distortion, and Nyquist ghosting, all of which can compromise diagnostic confidence and restrict broader clinical application. Enhancing spatial resolution in DWI not only improves lesion delineation but also facilitates advanced image analyses, including extraction of minimum ADC values, radiomic feature analysis, histogram evaluation, and detection of peripheral “rim” signs [ 12 – 14 ] . The reduced field-of-view (rFOV) strategy refers to MicroView technology of United Imaging Healthcare, which uniquely employs tilted intersecting excitation and refocusing pulses to separately excite two slices of signals and refocus the excited spin proton signals in the intersecting region of the two slices. This enables micro FOV imaging of a small region of interest, achieving higher spatial resolution in the same time and reducing image blurring and distortion caused by magnetic susceptibility effects [ 15 ] . In addition, since no imaging signal is generated outside the region of interest, MicroView can mitigate both aliasing artifacts and motion artifacts. Furthermore, due to the reduced imaging FOV in the phase encoding direction, the number of phase encoding steps can be effectively reduced, thereby realizing free choice of phase encoding direction, breaking the directional limitation of conventional FOV imaging [ 16 ] . In parallel with technical advancements in DWI, the field of MRI has seen a marked progression in ultra-high field imaging, with systems developed at 4.7T, 7.0T, and even up to 14.0T over the past decade [ 17 – 21 ] . Nonetheless, in vivo human imaging has largely remained within the scope of 3.0T or below, with higher field strengths primarily reserved for preclinical or small-animal investigations [ 22 , 23 ] . Recently, the introduction of a whole-body 5.0T MRI system presents new possibilities for pelvic imaging applications. One of the primary limitations of high-resolution DWI is the associated drop in signal-to-noise ratio (SNR), which can introduce bias in ADC estimation due to the noise floor effect. However, 5.0T MRI offers an SNR advantage over 3.0T, potentially counterbalancing this limitation. Furthermore, the high-performance gradients available on 5.0T platforms allow for reduced echo time (TE), enhancing both resolution and SNR of DWI sequences. This advancement may contribute to more accurate and reproducible ADC measurements, thereby improving diagnostic precision and staging capabilities. To date, no study has systematically evaluated the application of high-resolution rFOV DWI at 5.0T in the context of rectal cancer. Thus, the current study aimed to: (1) compare subjective and objective image quality (IQ) of rFOV DWI acquired at 5.0T versus 3.0T; and (2) investigate the association between tumor ADC values and T stage of rectal cancer. Materials and Methods Patients This prospective study was granted by the institutional ethics committee (Approval No. XHEC-C2024-072-1), and written informed consent was gained from all participants prior to inclusion. Between January and June 2024, a total of 110 individuals with clinically and pathologically confirmed rectal cancer were initially recruited. Patients meeting the criteria outlined below were enrolled: 1) diagnosis of rectal cancer confirmed via biopsy; 2) availability of pathological T staging results; 3) underwent preoperative MRI scans incorporating reduced rFOV DWI on both 3.0T and 5.0T MRI platforms. Exclusion criteria were as follows: 1) receipt of neoadjuvant chemoradiotherapy prior to MRI examination; 2) histopathological diagnosis of predominantly mucinous adenocarcinoma; and 3) inadequate IQ attributable to bowel movement or motion artifacts. Out of the 110 initially screened patients, 5 were excluded: One case had received chemoradiotherapy prior to imaging; One patient presented with mucinous histology on final pathology; Two individuals did not undergo surgical treatment at our institution, preventing histological correlation; One dataset was excluded due to severe image degradation from motion artifacts. The patient enrollment procedures and tumor characteristics are detailed in Fig. 1 . MRI examinations All participants underwent MRI scans on both a 3.0T system (uMR 790, United Imaging Healthcare, Shanghai, China) and a 5.0T whole-body system (uMR Jupiter, United Imaging Healthcare). To minimize physiological variability and technical bias, the time interval between the two imaging sessions was kept within 12 hours. For the 3.0T MRI protocol, patients were scanned in the supine position utilizing a 32-channel phased-array body coil. The imaging protocol included: sagittal fast spin echo (FSE) T2-weighted imaging (T2WI), oblique high-resolution axial FSE T2WI (aligned perpendicular to tumor axis), coronal high-resolution FSE T2WI, and multi-shot echo-planar imaging-based reduced rFOV DWI. Two b-values (0 and 1000 s/mm 2 ) were used for DWI acquisition. The plane for axial DWI was aligned perpendicular to the tumor’s longitudinal axis, as determined from sagittal T2WI images. No bowel preparation was administered prior to imaging. The 5.0T MRI protocol incorporated the following sequences: standard sagittal FSE T2WI, oblique axial FSE T1WI, oblique high-resolution axial FSE T2WI, coronal FSE T2WI, rFOV DWI, and dynamic contrast-enhanced (DCE) imaging. Notably, DCE sequences were only acquired at 5.0T to limit repeated contrast agent exposure. As previous studies have emphasized the role of temporal resolution over spatial resolution in DCE imaging, omitting this sequence at 3.0T was deemed appropriate [ 9 ] . Aside from the inherent difference in field strength, MRI sequences on both scanners were harmonized to use nearly identical imaging parameters, especially for FSE T2WI and DWI protocols. Specific technical settings for each scanner are detailed in Table 1 . Table 1 Acquisition settings for rFOV DWI at 3.0T and 5.0T Field strengths 3.0T 5.0T TR (ms) 3171 200 TE (ms) 85.5 65.8 FOV (mm) 130×200 130×200 Matrix 208×320 208×320 Voxel size (mm) 0.625×0.625×4 0.625×0.625×4 Slices 25 25 Thickness/Gap (mm) 4/0 4/0 b-value (s/mm2) 0,1000 0,1000 Acquisition time 4min30sec 2min48sec TE echo time, TR repetition time, FOV field of view, ADC maps were automatically generated on the vendor’s workstation (United Imaging Healthcare) utilizing a mono-exponential model applied along three orthogonal directions. Both 3.0T and 5.0T DWI datasets were acquired using the same rFOV method, namely the proprietary MicroView technique. Image analysis Objective assessment of IQ Two board- certified abdominal radiologists, with 12 and 7 years of experience respectively (D.X. and S.D.M.), independently conducted the quantitative analysis. To minimize potential bias, both readers were blinded to clinical information and imaging acquisition settings. To ensure consistency, identical regions of interest (ROIs) were used by both radiologists during measurement, with care taken to avoid inclusion of vascular structures, necrotic zones, or image artifacts. Signal intensity (SI) measurements were obtained from three anatomical regions: the rectal tumor, adjacent normal rectal wall (distant from tumor), and background (non-anatomic noise region). Tumor ROIs were delineated by manually tracing the tumor borders on DWI images. For the normal tissue, ROIs were positioned in healthy rectal segments, distant enough to avoid tumor influence. Each ROI provided both the mean and standard deviation (SD) of SI. Based on these measurements, the following quantitative IQ parameters were computed: SNR = S lesion / SD background SIR = S lesion / SD normal tissue CNR= (S lesion – SD background )/ SD background where S lesion is the average SI of the tumor, and SD normal tissue and SD background represent the SD of SI in the normal rectal wall and background (air or noise) regions, respectively. Subjective assessment of IQ Two abdominal radiologists with 12 and 7 years of clinical experience independently implemented qualitative evaluations of the rFOV DWI images, specifically using the b = 1000 s/mm 2 series for assessment. Both readers were blinded to scanner field strength (3.0T vs. 5.0T) and to patient clinical information. High-resolution axial T2-weighted images were used as anatomical references during evaluation to aid in localization and comparison. Visual assessment focused on five key parameters: image sharpness, geometric distortion, artifact presence, lesion conspicuity, and overall perceived IQ. IQ was examined utilizing a 4-point Likert scale across five parameters: 1. Sharpness (1 = not sharp, 2 = slightly blurred, 3 = moderately sharp, 4 = clearly sharp); 2. Distortion (1 = severe, 2 = moderate, 3 = mild, 4 = none); 3. Artifacts (1 = severe, limiting diagnosis; 2 = moderate; 3 = mild; 4 = no artifacts); 4. Lesion conspicuity (1 = hard to detect; 2 = faintly visible; 3 = clearly identifiable; 4 = highly conspicuous with good contrast); 5. Overall IQ was derived by summing the scores of the first four categories. Quantitative ADC assessment of rectal cancer To ensure consistent and anatomically accurate measurement of ADC values, ROIs were initially delineated on high-resolution DWI images acquired at a b-value of 1000 s/mm 2 . These ROIs were drawn large enough to encompass the full extent of the tumor, and were subsequently transferred to the corresponding ADC maps for quantitative analysis. For each patient, ADC evaluations from 3.0T and 5.0T scans were reviewed concurrently to ensure alignment. ROI placement was carefully matched between the two field strengths by referencing identical anatomical landmarks, allowing for direct comparison. To improve measurement reproducibility and reduce sampling variability, three separate ROIs were selected across three distinct axial slices that contained visible tumor tissue. The mean ADC value for each patient was computed from these three measurements. During ROI placement, particular care was taken to exclude non-solid components such as vascular structures, necrotic zones, or cystic changes, which were identified and verified using T2-weighted images. This approach minimized potential bias from non-representative tissue. Finally, ADC measurements from both radiologists were averaged to obtain the final value used for statistical evaluation and correlation with tumor staging. Statistical Analysis Interobserver agreement for the quantitative IQ parameters was examined utilizing the intraclass correlation coefficient (ICC). Agreement strength was interpreted as follows: ICC values between 0–0.20 indicated poor or negligible agreement; 0.21–0.40 represented fair consistency; 0.41–0.60 moderate; 0.61–0.80 substantial; and 0.81–1.00 denoted near-perfect reliability. For the qualitative (subjective) image assessments, agreement between the two radiologists was analyzed utilizing weighted kappa (κ) statistics, with the strength of agreement categorized as: poor (κ = 0.00–0.20), fair (0.21–0.40), moderate (0.41–0.60), good (0.61–0.80), and excellent (0.81–1.00). Comparative analyses of quantitative IQ metrics (SNR, CNR, and SIR) between 3.0T and 5.0T rFOV DWI were conducted using paired samples t-tests. Subjective scores (e.g., sharpness, artifact level, lesion conspicuity) were compared employing the Wilcoxon signed-rank test, which was also applied for comparing tumor ADC values across the two field strengths. To investigate the relationship between ADC values and the histopathological T stage of rectal cancer, Spearman’s rank correlation coefficient was calculated. All statistical procedures were implemented utilizing SPSS software (version 27.0, IBM Corp., Armonk, NY, USA). A two-tailed P value < 0.05 was deemed statistical significance. Results Patient characteristics Between January and June 2024, a total of 36 patients (23 males and 13 females; mean age: 62.8 ± 11.2 years) with histologically verified rectal cancer were ultimately included here. All subjects successfully completed MRI examinations on both 3.0T and 5.0T systems without experiencing any adverse events or complications during scanning. Detailed clinicopathological data are summarized in Table 2 . Histological analysis revealed that 2 patients (5.6%) had T1 tumors, 11 (30.6%) were classified as T2, 20 (55.5%) were diagnosed with T3 tumors, and 3 patients (8.3%) presented with T4 stage disease. Table 2 Clinicopathological characteristics of the patients. N% Gender Male 23(63.8%) Female 13 (36.2%) Carcinoembryonic antigen (CEA) + 16(44.4%) - 20(55.6%) Carbohydrate antigen 199(CA-199) + 6(16.7%) - 30(83.3%) Location of tumor Upper 14(38.8%) middle 13(36.2%) Lower 9(25%) Pathology T stage T1 2(5.5%) T2 11(30.6%) T3 20(55.6%) T4 3(8.3%) Objective IQ metrics Quantitative analysis of objective IQ parameters obtained from rFOV DWI sequences at both magnetic field strengths is presented in Table 3 . The 5.0T system demonstrated significantly enhanced image metrics compared to the 3.0T system: SNR: 5.0T: 7.15 ± 1.96 vs. 3.0T: 3.45 ± 0.68 ( P < 0.001); CNR: 5.0T: 4.47 ± 1.74 vs. 3.0T: 1.75 ± 0.62 ( P < 0.001); SIR: 5.0T: 2.76 ± 0.77 vs. 3.0T: 2.06 ± 0.39 ( P < 0.001). Table 3 Comparisons of quantitative and qualitative assessments of rFOV DWI between 3.0T and 5.0T. Image parameter 3.0T 5.0T P value Signal-to-noise ratio (SNR) 3.45 ± 0.68 7.15 ± 1.96 < 0.001 Contrast-to-noise ratio (CNR) 1.75 ± 0.62 4.47 ± 1.74 < 0.001 Signal-intensity ratio (SIR) 2.06 ± 0.39 2.76 ± 0.77 < 0.01 Sharpness 2.37 ± 0.54 3.58 ± 0.5 < 0.01 Distortion 3.57 ± 0.58 3.53 ± 0.55 0.763 Lesion conspicuity 2.66 ± 0.69 3.59 ± 0.59 < 0.001 Artifacts 3.17 ± 0.54 3.07 ± 0.57 0.458 Total image quality 11.69 ± 1.50 13.90 ± 1.67 < 0.001 Subjective IQ evaluation Qualitative scoring of IQ, based on the 4-point Likert scale, is summarized in Table 3 . No statistically significant differences were witnessed between 3.0T and 5.0T in terms of: geometric distortion: 5.0T: 3.57 ± 0.58 vs. 3.0T: 3.53 ± 0.55 ( P = 0.763); presence of artifacts (e.g., motion, ghosting, susceptibility): 5.0T: 3.17 ± 0.54 vs. 3.0T: 3.07 ± 0.57 ( P = 0.458). However, the 5.0T rFOV DWI images received significantly higher ratings for image sharpness (5.0T: 3.58 ± 0.50 vs. 3.0T: 2.37 ± 0.54), lesion conspicuity (5.0T: 3.59 ± 0.59 vs. 3.0T: 2.66 ± 0.69), and overall IQ score (composite) (5.0T: 13.90 ± 1.67 vs. 3.0T: 11.69 ± 1.50) (all P < 0.001). An illustrative comparison of DWI images acquired at 3.0T and 5.0T is provided in Fig. 2 . Quantitative evaluation of tumor ADC values The comparison of ADC values obtained at 3.0T and 5.0T is summarized in Table 4 . A statistically significant difference was observed in the mean ADC values between the two field strengths, with the 5.0T scanner yielding higher measurements than the 3.0T system ([0.884 ± 0.128] ×10 − 3 mm 2 /s vs. [0.972 ± 0.138] ×10 − 3 mm 2 /s, P < 0.001) (Fig. 3 ). Stratified analysis by T stage demonstrated that ADC values varied significantly with tumor invasiveness. Specifically, tumors staged as T2, T3, and T4 showed markedly higher ADC readings at 5.0T relative to 3.0T: T2 stage ([0.908 ± 0.160]×10 − 3 mm 2 /s vs. [0.976 ± 0.135]×10 − 3 mm 2 /s), T3 stage ([0.876 ± 0.128]×10 − 3 mm 2 /s vs. [0.974 ± 0.147]×10 − 3 mm 2 /s), and T4 stage ([0.797 ± 0.092]×10 − 3 mm 2 /s vs. [0.905 ± 0.126]×10 − 3 mm 2 /s) (all P < 0.001). For the T1 subgroup (n = 2), the observed difference in ADC values between 3.0T and 5.0T was not statistically significant, despite a numerical trend ([1.007 ± 0.039] ×10 − 3 mm 2 /s vs. [1.026 ± 0.062] ×10 − 3 mm 2 /s, P = 0.655). Correlation analysis demonstrated a notable inverse relationship between tumor ADC values and histological T stage at both field strengths. For 3.0T, the correlation coefficient was r = − 0.685 ( P < 0.001), and for 5.0T, r = − 0.621 ( P < 0.001), indicating that lower ADC values were linked to higher T stages (Table 5 , Fig. 4 ). Table 4 Comparisons of ADC values of rFOV DWI between 3.0T and 5.0T related to histopathological T staging of rectal cancer. Staging 3.0T( ×10 − 3 mm 2 /s) 5.0T(×10 − 3 mm 2 /s) P value T1 1.007 ± 0.039 1.026 ± 0.062 0.655 T2 0.908 ± 0.160 0.976 ± 0.135 < 0.001 T3 0.876 ± 0.128 0.974 ± 0.147 < 0.001 T4 0.797 ± 0.092 0.905 ± 0.126 < 0.001 Mean ± SD 0.884 ± 0.128 0.972 ± 0.138 < 0.001 Table 5 Correlation between ADC values of rFOV DWI at 3.0T and 5.0T and histopathological T stages of rectal cancer . ADC value T Staging (r value) P value 3.0T -0.685(-0.452, -0.831) < 0.001 5.0T -0.621(-0.358, -0.792) < 0.001 Interobserver agreement on IQ and ADC measurements Interobserver reliability for objective IQ metrics demonstrated robust consistency across both field strengths (all P < 0.001). The ICC values ranged from 0.778 to 0.909 at 3.0 T and 5.0T. Subjective assessments showed comparably strong agreement, with κ values ranging from 0.733 to 0.850 at 3.0T and from 0.753 to 0.847 at 5.0T (Table 6 ). Regarding ADC measurements, inter-reader consistency was excellent, with ICCs ranging from 0.843 to 0.862 at both field strengths. Table 6 Interobserver agreement of image qualities (IQ) and ADC values of rFOV DWI between 3.0T and 5.0T Image parameter 3.0T 5.0T Signal-to-noise ratio (SNR) 0.881(0.769,0.939) 0.909(0.822,0.953) Contrast-to-noise ratio (CNR) 0.874(0.752,0.936) 0.893(0.790,0.945) Signal-intensity ratio (SIR) 0.831(0.571,0.914) 0.778(0.556,0.888) Sharpness 0.741(0.547,0.936) 0.771(0.560,0.982) Distortion 0.850(0.692,1.008) 0.847(0.677,1.017) Lesion conspicuity 0.766(0.593,0.939) 0.753(0.538,0.968) Artifacts 0.799(0.611,0.987) 0.835(0.655,1.015) Total image quality 0.733(0.623,0.843) 0.774(0.655,0.894) ADC 0.862(0.50,0.948) 0.843(0.711,0.918) Discussion In this prospective study, we demonstrated that reduced rFOV DWI at 5.0T MRI offers markedly improved IQ for rectal cancer imaging compared to 3.0T MRI. Both quantitative and qualitative assessments revealed statistically significant enhancements in image clarity, lesion visualization, and signal contrast at the higher field strength. Additionally, a significant inverse correlation was found between tumor ADC values and histological T stage, consistent across both 3.0T and 5.0T systems. Inter-reader agreement for both IQ and ADC measurements was strong, as evidenced by high ICCs and weighted kappa values, supporting the reproducibility of the findings. Earlier studies have reported the benefits of rFOV DWI over traditional full-FOV (fFOV) sequences in pelvic imaging, particularly for rectal cancer. rFOV techniques have been shown to significantly mitigate susceptibility-induced artifacts, geometric distortion, and blurring, leading to more accurate delineation of tumor boundaries and internal architecture [ 24 ] . Moreover, the ability of rFOV DWI to better characterize tumor heterogeneity, including the identification of cystic, hemorrhagic, and necrotic regions, enhances diagnostic confidence in both qualitative and quantitative interpretations [ 25 ] . The transition to 5.0T imaging introduces several advantages, primarily driven by increased SNR and CNR, both of which contribute to enhanced visualization of fine anatomical structures. This is aligned with earlier research suggesting that ultra-high field MRI improves the depiction of pathological features due to better intrinsic tissue contrast [ 26 , 27 ] . Our results reaffirm this, with rFOV DWI at 5.0T showing consistently superior objective IQ metrics and subjective ratings compared to 3.0T. These observations echo prior findings in abdominal imaging. For example, Zheng et al. [ 28 ] demonstrated that 5.0T MRI yielded higher-quality pancreatic images with greater SNR than 3.0T. Similarly, Zhang et al. [ 29 ] reported improved subjective scoring in abdominal DWI at 5.0T, and Zheng et al. [ 30 ] confirmed that 5.0T MRI is capable of providing anatomically and functionally adequate renal imaging. Although susceptibility artifacts are a known concern at even higher field strengths—such as 7.0T, where they are significantly more pronounced than at 3.0T [ 31 ] —our findings showed that artifact levels at 5.0T remained manageable. In our cohort, no differences were observed in artifact-related scores between the two field strengths (3.17 at 3.0T vs. 3.07 at 5.0T, P = 0.763). Importantly, the observed artifacts at 5.0T were minimal and did not compromise diagnostic utility. A possible explanation for the acceptable artifact burden at 5.0T lies in the relative insensitivity of motion-related artifacts to increased field strength. Instead, factors such as patient compliance, optimal positioning, and the use of stabilization devices may play a more significant role in maintaining image integrity across varying magnetic fields [ 32 ] . ADC, derived from DWI sequences, is a widely adopted quantitative parameter that reflects both the diffusion of water molecules in the extracellular-extravascular compartment and microvascular perfusion effects. Its utility as a non-invasive imaging biomarker in rectal cancer has been explored in multiple clinical applications, including histopathological T staging [ 33 ] , prediction of KRAS mutation status [ 34 ] , evaluation of treatment response to neoadjuvant chemoradiotherapy and patient prognosis [ 35 ] , as well as forecasting the risk of metachronous metastases [ 36 ] . Before the widespread adoption of 5.0T rFOV DWI in clinical practice, it is essential to determine whether ADC measurements are influenced by magnetic field strength. In the current study, we observed that mean ADC values obtained at 5.0T were distinctly higher than those acquired at 3.0T for rectal tumors. This observation contrasts with previous reports. For instance, Zheng et al. [ 28 ] found no significant differences in pancreatic ADC values between 3.0T and 5.0T using rFOV DWI. Similarly, Zhang et al. [ 29 ] demonstrated high inter-field consistency of ADC values for abdominal organs such as the liver, spleen, pancreas, and kidneys, while another study by the same group showed comparable ADC values between the renal cortex and medulla at both 3.0T and 5.0T [ 30 ] . Several factors may account for these discrepancies. First, inter-organ physiological variations could contribute to differential diffusion behavior across tissues. Second, the technical advantages of the rFOV DWI sequence—namely, superior fat suppression, improved spatial resolution, and reduced susceptibility artifacts—may lead to distinct ADC quantification characteristics in certain anatomical regions [ 37 ] . Moreover, ADC values are known to be affected by a range of factors including acquisition protocols, magnetic field strength, and b-value selection [ 38 ] . In this study, ADC values also showed stage-dependent variations. Significant differences were noted between ADC values at 3.0T and 5.0T for tumors classified as T2, T3, and T4. For T1 lesions, no notable difference was observed, although the small sample size (n = 2) may limit interpretability. These findings suggest that the relationship between ADC and tumor stage is influenced not only by biological factors such as intratumoral heterogeneity but also by the field strength of the MRI system. Several prior investigations have indicated that ADC values may reflect the biological aggressiveness of rectal tumors [ 39 , 40 ] . However, limited attention has been paid to the direct relationship between histopathological T staging and ADC measurements. In our current study, we identified a consistent inverse association between tumor ADC values and T stage at both 3.0T and 5.0T field strengths. This observation aligns with the findings of Yang et al. [ 24 ] , who also validated a negative correlation between T stage and ADC values, regardless of whether full or reduced FOV DWI sequences were used. We further observed a decreasing trend in ADC values with increasing T stage—from T1 through T4. This pattern is biologically plausible, as tumors with higher T stages typically exhibit increased cellular density, reduced extracellular space, and a more complex microenvironment. These factors restrict water molecule diffusion, thereby lowering ADC values [ 41 , 42 ] . Thus, the declining ADC across T stages may reflect underlying tumor aggressiveness and structural compactness. This study has several notable limitations. First, the investigation primarily focused on comparing IQ metrics rather than diagnostic accuracy or lesion detection rates across different field strengths. Second, the modest cohort size may constrain the broader applicability of our findings and increase the risk of statistical deviation. Third, due to the inclusion of only two T1-stage patients, conclusions regarding ADC differences at early stages must be interpreted with caution. A larger cohort would be necessary to validate these preliminary findings and support their clinical applicability. Conclusions In conclusion, our preliminary results demonstrate that rFOV DWI at 5.0T MRI offers superior IQ and improved lesion visualization compared to 3.0T MRI in rectal cancer imaging. The mean ADC values obtained at 5.0T were notably higher than those measured at 3.0T, and pretreatment ADC values from both field strengths were inversely linked to tumor T staging. These findings suggest that ADC measurements from high-resolution rFOV DWI hold potential as a non-invasive indicator for evaluating tumor invasiveness. Abbreviations MRI Magnetic Resonance Imaging IQ image quality rFOV reduced field-of-view DWI diffusion-weighted imaging ADC apparent diffusion coefficient SNR signal-to-noise ratio SIR signal-intensity ratio CNR contrast-to-noise ratio TE echo time FSE fast spin echo DCE dynamic-contrast enhanced ROI region of interest Declarations Ethics approval and consent to participate: This study was approved by the Institutional Ethics Committee of Xinhua Hospital (No. XHEC-C2024-072-1) and in accordance with the Declaration of Helsinki. Consent for publication: Not applicable. Funding: This study was supported by National Natural Science Foundation of China (No. 81901695), Shanghai Sailing Program (No.19YF1433100), Program of Shanghai Municipal Health Youth Talent (No. 2022YQ042), the Program of Shanghai Science and Technology Committee (No. 24TS1415200), and the Fundamental Research Funds for the Central Universities (No. YG2023QNA17and No. YG2025QNA39). Author Contribution Huanhuan Liu and Dengbin Wang contributed to study conception and design, Qiufeng Yin, Peirong Zhang, Zhongyang Zhang, Xing Zhang contributed to acquisition of data, Xue Dong, Dongmei Shi, Zhiwei Yang and Shaofeng Duan contributed to analysis and interpretation of data, Xue Dong contributed to drafting of manuscript, Huanhuan Liu and Dengbin Wang contributed to critical revision. Acknowledgement The authors gratefully acknowledge all of the investigators for their contributions to the study, as well as Kun Sun, who is a guarantor for the entire study. Data Availability Data are however available from the authors upon reasonable request and with permission of Liu Huanhuan. References Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin. 2024;74(3):229–63. Fernandes MC, Gollub MJ, Brown G. The importance of MRI for rectal cancer evaluation[J]. 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Design and evaluation of a hybrid radiofrequency applicator for magnetic resonance imaging and RF induced hyperthermia: electromagnetic field simulations up to 14.0 Tesla and proof-of-concept at 7.0 Tesla[J]. PLoS ONE. 2013;8(4):e61661. Lu Y, Wang Q, Zhang T, et al. Staging Liver Fibrosis: Comparison of Native T1 Mapping, T2 Mapping, and T1rho: An Experimental Study in Rats With Bile Duct Ligation and Carbon Tetrachloride at 11.7 T MRI[J]. J Magn Reson Imaging. 2022;55(2):507–17. Thomas DC, Oros-Peusquens AM, Schoneck M, et al. In Vivo Measurement of Rat Brain Water Content at 9.4 T MR Using Super-Resolution Reconstruction: Validation With Ex Vivo Experiments[J]. J Magn Reson Imaging. 2024;60(1):161–72. Peng Y, Li Z, Tang H, et al. Comparison of reduced field-of-view diffusion-weighted imaging (DWI) and conventional DWI techniques in the assessment of rectal carcinoma at 3.0T: Image quality and histological T staging[J]. J Magn Reson Imaging. 2018;47(4):967–75. Huang H, Zhou M, Gong T, et al. Feasibility of high-resolution readout-segmented echo-planar imaging with simultaneous multislice imaging in assessing rectal cancer[J]. Abdom Radiol (NY). 2023;48(7):2258–69. Barisano G, Sepehrband F, Ma S, et al. Clinical 7 T MRI: Are we there yet? A review about magnetic resonance imaging at ultra-high field[J]. Br J Radiol. 2019;92(1094):20180492. Li X, Zhu XH, Chen W. A Quantitative Comparison of (31)P Magnetic Resonance Spectroscopy RF Coil Sensitivity and SNR between 7T and 10.5T Human MRI Scanners Using a Loop-Dipole (31)P-(1)H Probe[J]. Sens (Basel), 2024,24(17). Zheng L, Yang C, Liang L, et al. T2-weighted MRI and reduced-FOV diffusion-weighted imaging of the human pancreas at 5 T: A comparison study with 3 T[J]. Med Phys. 2023;50(1):344–53. Zhang Y, Yang C, Liang L, et al. Preliminary Experience of 5.0 T Higher Field Abdominal Diffusion-Weighted MRI: Agreement of Apparent Diffusion Coefficient With 3.0 T Imaging[J]. J Magn Reson Imaging. 2022;56(4):1009–17. Zheng L, Yang C, Sheng R, et al. Renal imaging at 5 T versus 3 T: a comparison study[J]. Insights Imaging. 2022;13(1):155. Laader A, Beiderwellen K, Kraff O, et al. 1.5 versus 3 versus 7 Tesla in abdominal MRI: A comparative study[J]. PLoS ONE. 2017;12(11):e0187528. Schmidt GP, Wintersperger B, Graser A, et al. High-resolution whole-body magnetic resonance imaging applications at 1.5 and 3 Tesla: a comparative study[J]. Invest Radiol. 2007;42(6):449–59. Peng Y, Li Z, Tang H, et al. Comparison of reduced field-of-view diffusion-weighted imaging (DWI) and conventional DWI techniques in the assessment of rectal carcinoma at 3.0T: Image quality and histological T staging[J]. J Magn Reson Imaging. 2018;47(4):967–75. Xu Y, Xu Q, Sun H, et al. Could IVIM and ADC help in predicting the KRAS status in patients with rectal cancer?[J]. Eur Radiol. 2018;28(7):3059–65. Hu T, Gong J, Sun Y et al. Magnetic resonance imaging-based radiomics analysis for prediction of treatment response to neoadjuvant chemoradiotherapy and clinical outcome in patients with locally advanced rectal cancer: A large multicentric and validated study[J]. MedComm (2020), 2024,5(7):e609. Boca PB, Caraiani C, Popa L et al. The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study[J]. Biology (Basel), 2022,11(3). Lu Y, Hatzoglou V, Banerjee S, et al. Repeatability Investigation of Reduced Field-of-View Diffusion-Weighted Magnetic Resonance Imaging on Thyroid Glands[J]. J Comput Assist Tomogr. 2015;39(3):334–9. Shi J, Lin J, Zhou X, et al. Comparison of Reduced and Full Field of View in Diffusion-Weighted MRI on Image Quality: A Meta-Analysis[J]. J Magn Reson Imaging; 2024. Zhou M, Chen M, Luo M et al. Pathological prognostic factors of rectal cancer based on diffusion-weighted imaging, intravoxel incoherent motion, and diffusion kurtosis imaging[J]. Eur Radiol, 2024. Wu Q, Yi Y, Lai B, et al. Texture analysis of apparent diffusion coefficient maps: can it identify nonresponse to neoadjuvant chemotherapy for additional radiation therapy in rectal cancer patients?[J]. Gastroenterol Rep (Oxf). 2024;12:goae035. Lu ZH, Hu CH, Qian WX, et al. Preoperative diffusion-weighted imaging value of rectal cancer: preoperative T staging and correlations with histological T stage[J]. Clin Imaging. 2016;40(3):563–8. Pan YN, Gu MY, Mao QL, et al. The Clinical Value of Apparent Diffusion Coefficient of Readout Segmentation of Long Variable Echo Trains and Correlation With Ki-67 Expression in Distal Rectal Cancer[J]. J Comput Assist Tomogr. 2024;48(3):361–9. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Dec, 2025 Read the published version in BMC Medical Imaging → Version 1 posted Editorial decision: Revision requested 01 Oct, 2025 Reviews received at journal 30 Sep, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviews received at journal 30 Sep, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers invited by journal 27 Sep, 2025 Editor assigned by journal 24 Sep, 2025 Editor invited by journal 16 Sep, 2025 Submission checks completed at journal 15 Sep, 2025 First submitted to journal 15 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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15:33:19\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":114519,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFlow diagram indicating patient and tumor characteristics.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7533445/v1/332518b30c3cd4f7dd113305.png\"},{\"id\":93248167,\"identity\":\"146e64be-5427-474c-945e-99bce5d0939b\",\"added_by\":\"auto\",\"created_at\":\"2025-10-10 15:33:19\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":134117,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eImages for subjective assessment of image quality (IQ). a, b: assessment of artifacts. c,d: assessment of distortion. e,f: assessment of sharpness and lesion conspicuity. a: rFOV DWI at 3.0T shows mild susceptibility artifacts (thin arrow) produced in the rectal lumen adjacent to the lesion without obvious distortions. The IQ score for artifacts is 3. b: rFOV DWI at 5.0T shows moderate susceptibility artifacts (thin arrow) produced at the same location with slight distortions. c: rFOV DWI at 3.0T shows the smooth and clear wall of the base of bladder without distortion (arrow). The IQ score for distortion is 4. d: rFOV DWI at 5.0T shows the blurred wall of the base of the bladder with moderate distortion (arrow). The IQ score for distortion is 2. e: rFOV DWI at 3.0T shows the focus of rectal cancer with nearly even intermediate signal intensity, moderately sharp anatomic details, and recognizable lesion feature. The IQ scores for sharpness and lesion conspicuity are both 3. f: rFOV DWI at 5.0T shows the focus of rectal cancer with mixed intermediate signal intensity, sharp margin, and clear lesion feature, without obvious distortions or artifacts. The IQ scores for sharpness and lesion conspicuity are both 4.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Onlinefloatimage27.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7533445/v1/7ef5f1bc30fa0645f19903ce.png\"},{\"id\":93251537,\"identity\":\"bce00fb4-aa69-4f3d-a139-2bda8c15b005\",\"added_by\":\"auto\",\"created_at\":\"2025-10-10 15:49:19\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":228281,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eA patient with T3 stage rectal adenocarcinoma. a: High-resolution T2WI exhibits noticeable thickening of rectal wall. b,c: rFOV DWI\\u0026nbsp; at 3.0T (c) and 5.0T (d)\\u0026nbsp; images show corresponding rectal wall with partially high signal intensity. contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were 1.57 and 4.65 at 3.0T, and 3.26 and 7.47 at 5.0T, respectively. Higher scores are evaluated at 5.0T than 3.0T by both readers in terms of subjective image parameters. d,e: ADC maps of rFOV DWI at 3.0T (d) and 5.0T (e).Mean ADC values were 0.878×10\\u003csup\\u003e-3\\u003c/sup\\u003e mm\\u003csup\\u003e2\\u003c/sup\\u003e/s\\u0026nbsp; and 0.986×10\\u003csup\\u003e-3\\u003c/sup\\u003e mm\\u003csup\\u003e2\\u003c/sup\\u003e/s , respectively.\\u0026nbsp;\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Onlinefloatimage33.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7533445/v1/001783ce46ec968b9273a65b.png\"},{\"id\":93248173,\"identity\":\"f6cb9afc-8f77-44d3-b4a5-ac79f2a4c9e7\",\"added_by\":\"auto\",\"created_at\":\"2025-10-10 15:33:19\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":5106,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eBoxplot for the mean tumor ADC values correlated with histological staging of rectal cancer indicates significant differences for both 3.0T and 5.0T MRI.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Onlinefloatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7533445/v1/10f630455eede9f2f545c437.png\"},{\"id\":98244790,\"identity\":\"7f662e90-8be7-4ffd-9344-00cec83f9f27\",\"added_by\":\"auto\",\"created_at\":\"2025-12-15 16:15:14\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1309186,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7533445/v1/f05c9d85-ff43-4073-99d9-f5520970eb74.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Comparison of Reduced FOV Diffusion-weighted Imaging of Rectal Cancer at 5.0T ultra- high field versus 3.0T MRI: Image Quality and Histopathological T Staging\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eColorectal cancer ranks as the third most frequently diagnosed malignancy globally, with approximately 30\\u0026ndash;35% of cases originating in the rectum\\u003csup\\u003e[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]\\u003c/sup\\u003e. Owing to its exceptional soft tissue resolution and capacity to capture both anatomical and functional details, magnetic resonance imaging (MRI) has emerged as the preferred modality for preoperative assessment of rectal cancer. It plays a vital role in assessing tumor extent, evaluating lymphovascular involvement, tracking response to neoadjuvant treatment, and recognizing postoperative local recurrence\\u003csup\\u003e[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eDiffusion-weighted imaging (DWI), a functional MRI technique, captures differences in the diffusion behavior of water molecules within tissues, thereby providing insight into microstructural and cellular characteristics\\u003csup\\u003e[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]\\u003c/sup\\u003e. A key quantitative parameter derived from DWI is the apparent diffusion coefficient (ADC), which reflects the degree of water molecule mobility in tissue and serves as a valuable imaging biomarker. Previous studies have established the clinical utility of ADC measurements for identifying and characterizing pathological lesions, evaluating organ function, and monitoring therapeutic outcomes, particularly at commonly used field strengths such as 1.5T and 3.0T\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR6\\\" citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/sup\\u003e. In rectal cancer specifically, ADC values have shown potential as a non-invasive tool for stratifying tumors by histological T stage and tumor grade\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR9 CR10\\\" citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eTraditional pelvic DWI techniques are often challenged by inherent limitations such as suboptimal spatial resolution, chemical shift artifacts, geometric distortion, and Nyquist ghosting, all of which can compromise diagnostic confidence and restrict broader clinical application. Enhancing spatial resolution in DWI not only improves lesion delineation but also facilitates advanced image analyses, including extraction of minimum ADC values, radiomic feature analysis, histogram evaluation, and detection of peripheral \\u0026ldquo;rim\\u0026rdquo; signs\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR13\\\" citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]\\u003c/sup\\u003e. The reduced field-of-view (rFOV) strategy refers to MicroView technology of United Imaging Healthcare, which uniquely employs tilted intersecting excitation and refocusing pulses to separately excite two slices of signals and refocus the excited spin proton signals in the intersecting region of the two slices. This enables micro FOV imaging of a small region of interest, achieving higher spatial resolution in the same time and reducing image blurring and distortion caused by magnetic susceptibility effects\\u003csup\\u003e[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]\\u003c/sup\\u003e. In addition, since no imaging signal is generated outside the region of interest, MicroView can mitigate both aliasing artifacts and motion artifacts. Furthermore, due to the reduced imaging FOV in the phase encoding direction, the number of phase encoding steps can be effectively reduced, thereby realizing free choice of phase encoding direction, breaking the directional limitation of conventional FOV imaging\\u003csup\\u003e[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eIn parallel with technical advancements in DWI, the field of MRI has seen a marked progression in ultra-high field imaging, with systems developed at 4.7T, 7.0T, and even up to 14.0T over the past decade\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR18 CR19 CR20\\\" citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]\\u003c/sup\\u003e. Nonetheless, in vivo human imaging has largely remained within the scope of 3.0T or below, with higher field strengths primarily reserved for preclinical or small-animal investigations\\u003csup\\u003e[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]\\u003c/sup\\u003e. Recently, the introduction of a whole-body 5.0T MRI system presents new possibilities for pelvic imaging applications. One of the primary limitations of high-resolution DWI is the associated drop in signal-to-noise ratio (SNR), which can introduce bias in ADC estimation due to the noise floor effect. However, 5.0T MRI offers an SNR advantage over 3.0T, potentially counterbalancing this limitation. Furthermore, the high-performance gradients available on 5.0T platforms allow for reduced echo time (TE), enhancing both resolution and SNR of DWI sequences. This advancement may contribute to more accurate and reproducible ADC measurements, thereby improving diagnostic precision and staging capabilities.\\u003c/p\\u003e\\u003cp\\u003eTo date, no study has systematically evaluated the application of high-resolution rFOV DWI at 5.0T in the context of rectal cancer. Thus, the current study aimed to: (1) compare subjective and objective image quality (IQ) of rFOV DWI acquired at 5.0T versus 3.0T; and (2) investigate the association between tumor ADC values and T stage of rectal cancer.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003ePatients\\u003c/h2\\u003e\\u003cp\\u003eThis prospective study was granted by the institutional ethics committee (Approval No. XHEC-C2024-072-1), and written informed consent was gained from all participants prior to inclusion. Between January and June 2024, a total of 110 individuals with clinically and pathologically confirmed rectal cancer were initially recruited. Patients meeting the criteria outlined below were enrolled: 1) diagnosis of rectal cancer confirmed via biopsy; 2) availability of pathological T staging results; 3) underwent preoperative MRI scans incorporating reduced rFOV DWI on both 3.0T and 5.0T MRI platforms. Exclusion criteria were as follows: 1) receipt of neoadjuvant chemoradiotherapy prior to MRI examination; 2) histopathological diagnosis of predominantly mucinous adenocarcinoma; and 3) inadequate IQ attributable to bowel movement or motion artifacts. Out of the 110 initially screened patients, 5 were excluded: One case had received chemoradiotherapy prior to imaging; One patient presented with mucinous histology on final pathology; Two individuals did not undergo surgical treatment at our institution, preventing histological correlation; One dataset was excluded due to severe image degradation from motion artifacts. The patient enrollment procedures and tumor characteristics are detailed in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eMRI examinations\\u003c/h3\\u003e\\n\\u003cp\\u003eAll participants underwent MRI scans on both a 3.0T system (uMR 790, United Imaging Healthcare, Shanghai, China) and a 5.0T whole-body system (uMR Jupiter, United Imaging Healthcare). To minimize physiological variability and technical bias, the time interval between the two imaging sessions was kept within 12 hours. For the 3.0T MRI protocol, patients were scanned in the supine position utilizing a 32-channel phased-array body coil. The imaging protocol included: sagittal fast spin echo (FSE) T2-weighted imaging (T2WI), oblique high-resolution axial FSE T2WI (aligned perpendicular to tumor axis), coronal high-resolution FSE T2WI, and multi-shot echo-planar imaging-based reduced rFOV DWI. Two b-values (0 and 1000 s/mm\\u003csup\\u003e2\\u003c/sup\\u003e) were used for DWI acquisition. The plane for axial DWI was aligned perpendicular to the tumor\\u0026rsquo;s longitudinal axis, as determined from sagittal T2WI images. No bowel preparation was administered prior to imaging.\\u003c/p\\u003e\\u003cp\\u003eThe 5.0T MRI protocol incorporated the following sequences: standard sagittal FSE T2WI, oblique axial FSE T1WI, oblique high-resolution axial FSE T2WI, coronal FSE T2WI, rFOV DWI, and dynamic contrast-enhanced (DCE) imaging. Notably, DCE sequences were only acquired at 5.0T to limit repeated contrast agent exposure. As previous studies have emphasized the role of temporal resolution over spatial resolution in DCE imaging, omitting this sequence at 3.0T was deemed appropriate \\u003csup\\u003e[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]\\u003c/sup\\u003e. Aside from the inherent difference in field strength, MRI sequences on both scanners were harmonized to use nearly identical imaging parameters, especially for FSE T2WI and DWI protocols. Specific technical settings for each scanner are detailed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eAcquisition settings for rFOV DWI at 3.0T and 5.0T\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"3\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eField strengths\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003e3.0T 5.0T\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTR (ms)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3171\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e200\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTE (ms)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e85.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e65.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFOV (mm)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e130\\u0026times;200\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e130\\u0026times;200\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMatrix\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e208\\u0026times;320\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e208\\u0026times;320\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVoxel size (mm)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.625\\u0026times;0.625\\u0026times;4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.625\\u0026times;0.625\\u0026times;4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSlices\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e25\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e25\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eThickness/Gap (mm)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4/0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4/0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eb-value (s/mm2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0,1000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0,1000\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAcquisition time\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4min30sec\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2min48sec\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eTE echo time, TR repetition time, FOV field of view,\\u003c/p\\u003e\\u003cp\\u003eADC maps were automatically generated on the vendor\\u0026rsquo;s workstation (United Imaging Healthcare) utilizing a mono-exponential model applied along three orthogonal directions. Both 3.0T and 5.0T DWI datasets were acquired using the same rFOV method, namely the proprietary MicroView technique.\\u003c/p\\u003e\\n\\u003ch3\\u003eImage analysis\\u003c/h3\\u003e\\n\\u003cp\\u003e\\u003cb\\u003eObjective assessment of IQ\\u003c/b\\u003e Two board- certified abdominal radiologists, with 12 and 7 years of experience respectively (D.X. and S.D.M.), independently conducted the quantitative analysis. To minimize potential bias, both readers were blinded to clinical information and imaging acquisition settings. To ensure consistency, identical regions of interest (ROIs) were used by both radiologists during measurement, with care taken to avoid inclusion of vascular structures, necrotic zones, or image artifacts. Signal intensity (SI) measurements were obtained from three anatomical regions: the rectal tumor, adjacent normal rectal wall (distant from tumor), and background (non-anatomic noise region). Tumor ROIs were delineated by manually tracing the tumor borders on DWI images. For the normal tissue, ROIs were positioned in healthy rectal segments, distant enough to avoid tumor influence. Each ROI provided both the mean and standard deviation (SD) of SI. Based on these measurements, the following quantitative IQ parameters were computed:\\u003c/p\\u003e\\u003cp\\u003eSNR\\u0026thinsp;=\\u0026thinsp;S\\u003csub\\u003elesion\\u003c/sub\\u003e/ SD\\u003csub\\u003ebackground\\u003c/sub\\u003e\\u003c/p\\u003e\\u003cp\\u003eSIR\\u0026thinsp;=\\u0026thinsp;S\\u003csub\\u003elesion\\u003c/sub\\u003e/ SD\\u003csub\\u003enormal tissue\\u003c/sub\\u003e\\u003c/p\\u003e\\u003cp\\u003eCNR= (S\\u003csub\\u003elesion\\u003c/sub\\u003e \\u0026ndash; SD\\u003csub\\u003ebackground\\u003c/sub\\u003e)/ SD\\u003csub\\u003ebackground\\u003c/sub\\u003e\\u003c/p\\u003e\\u003cp\\u003ewhere S\\u003csub\\u003elesion\\u003c/sub\\u003e is the average SI of the tumor, and SD\\u003csub\\u003enormal tissue\\u003c/sub\\u003e and SD\\u003csub\\u003ebackground\\u003c/sub\\u003e represent the SD of SI in the normal rectal wall and background (air or noise) regions, respectively.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eSubjective assessment of IQ\\u003c/b\\u003e Two abdominal radiologists with 12 and 7 years of clinical experience independently implemented qualitative evaluations of the rFOV DWI images, specifically using the b\\u0026thinsp;=\\u0026thinsp;1000 s/mm\\u003csup\\u003e2\\u003c/sup\\u003e series for assessment. Both readers were blinded to scanner field strength (3.0T vs. 5.0T) and to patient clinical information. High-resolution axial T2-weighted images were used as anatomical references during evaluation to aid in localization and comparison. Visual assessment focused on five key parameters: image sharpness, geometric distortion, artifact presence, lesion conspicuity, and overall perceived IQ. IQ was examined utilizing a 4-point Likert scale across five parameters:\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e1. Sharpness (1\\u0026thinsp;=\\u0026thinsp;not sharp, 2\\u0026thinsp;=\\u0026thinsp;slightly blurred, 3\\u0026thinsp;=\\u0026thinsp;moderately sharp, 4\\u0026thinsp;=\\u0026thinsp;clearly sharp);\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e2. Distortion (1\\u0026thinsp;=\\u0026thinsp;severe, 2\\u0026thinsp;=\\u0026thinsp;moderate, 3\\u0026thinsp;=\\u0026thinsp;mild, 4\\u0026thinsp;=\\u0026thinsp;none);\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e3. Artifacts (1\\u0026thinsp;=\\u0026thinsp;severe, limiting diagnosis; 2\\u0026thinsp;=\\u0026thinsp;moderate; 3\\u0026thinsp;=\\u0026thinsp;mild; 4\\u0026thinsp;=\\u0026thinsp;no artifacts);\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e4. Lesion conspicuity (1\\u0026thinsp;=\\u0026thinsp;hard to detect; 2\\u0026thinsp;=\\u0026thinsp;faintly visible; 3\\u0026thinsp;=\\u0026thinsp;clearly identifiable; 4\\u0026thinsp;=\\u0026thinsp;highly conspicuous with good contrast);\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e5. Overall IQ was derived by summing the scores of the first four categories.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eQuantitative ADC assessment of rectal cancer\\u003c/b\\u003e To ensure consistent and anatomically accurate measurement of ADC values, ROIs were initially delineated on high-resolution DWI images acquired at a b-value of 1000 s/mm\\u003csup\\u003e2\\u003c/sup\\u003e. These ROIs were drawn large enough to encompass the full extent of the tumor, and were subsequently transferred to the corresponding ADC maps for quantitative analysis. For each patient, ADC evaluations from 3.0T and 5.0T scans were reviewed concurrently to ensure alignment. ROI placement was carefully matched between the two field strengths by referencing identical anatomical landmarks, allowing for direct comparison. To improve measurement reproducibility and reduce sampling variability, three separate ROIs were selected across three distinct axial slices that contained visible tumor tissue. The mean ADC value for each patient was computed from these three measurements. During ROI placement, particular care was taken to exclude non-solid components such as vascular structures, necrotic zones, or cystic changes, which were identified and verified using T2-weighted images. This approach minimized potential bias from non-representative tissue. Finally, ADC measurements from both radiologists were averaged to obtain the final value used for statistical evaluation and correlation with tumor staging.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStatistical Analysis\\u003c/h2\\u003e\\u003cp\\u003eInterobserver agreement for the quantitative IQ parameters was examined utilizing the intraclass correlation coefficient (ICC). Agreement strength was interpreted as follows: ICC values between 0\\u0026ndash;0.20 indicated poor or negligible agreement; 0.21\\u0026ndash;0.40 represented fair consistency; 0.41\\u0026ndash;0.60 moderate; 0.61\\u0026ndash;0.80 substantial; and 0.81\\u0026ndash;1.00 denoted near-perfect reliability. For the qualitative (subjective) image assessments, agreement between the two radiologists was analyzed utilizing weighted kappa (κ) statistics, with the strength of agreement categorized as: poor (κ\\u0026thinsp;=\\u0026thinsp;0.00\\u0026ndash;0.20), fair (0.21\\u0026ndash;0.40), moderate (0.41\\u0026ndash;0.60), good (0.61\\u0026ndash;0.80), and excellent (0.81\\u0026ndash;1.00). Comparative analyses of quantitative IQ metrics (SNR, CNR, and SIR) between 3.0T and 5.0T rFOV DWI were conducted using paired samples t-tests. Subjective scores (e.g., sharpness, artifact level, lesion conspicuity) were compared employing the Wilcoxon signed-rank test, which was also applied for comparing tumor ADC values across the two field strengths. To investigate the relationship between ADC values and the histopathological T stage of rectal cancer, Spearman\\u0026rsquo;s rank correlation coefficient was calculated.\\u003c/p\\u003e\\u003cp\\u003eAll statistical procedures were implemented utilizing SPSS software (version 27.0, IBM Corp., Armonk, NY, USA). A two-tailed \\u003cem\\u003eP\\u003c/em\\u003e value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was deemed statistical significance.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003ePatient characteristics\\u003c/h2\\u003e\\u003cp\\u003eBetween January and June 2024, a total of 36 patients (23 males and 13 females; mean age: 62.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.2 years) with histologically verified rectal cancer were ultimately included here. All subjects successfully completed MRI examinations on both 3.0T and 5.0T systems without experiencing any adverse events or complications during scanning. Detailed clinicopathological data are summarized in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. Histological analysis revealed that 2 patients (5.6%) had T1 tumors, 11 (30.6%) were classified as T2, 20 (55.5%) were diagnosed with T3 tumors, and 3 patients (8.3%) presented with T4 stage disease.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eClinicopathological characteristics of the patients.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"3\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eN%\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eGender\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e23(63.8%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eFemale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e13 (36.2%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eCarcinoembryonic antigen (CEA)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e+\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e16(44.4%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e20(55.6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eCarbohydrate antigen 199(CA-199)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e+\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6(16.7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e30(83.3%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eLocation of tumor\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eUpper\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e14(38.8%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003emiddle\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e13(36.2%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eLower\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e9(25%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003ePathology T stage\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eT1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2(5.5%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eT2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e11(30.6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eT3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e20(55.6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eT4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3(8.3%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eObjective IQ metrics\\u003c/h3\\u003e\\n\\u003cp\\u003eQuantitative analysis of objective IQ parameters obtained from rFOV DWI sequences at both magnetic field strengths is presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. The 5.0T system demonstrated significantly enhanced image metrics compared to the 3.0T system: SNR: 5.0T: 7.15\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.96 vs. 3.0T: 3.45\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.68 (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001); CNR: 5.0T: 4.47\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.74 vs. 3.0T: 1.75\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.62 (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001); SIR: 5.0T: 2.76\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.77 vs. 3.0T: 2.06\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.39 (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eComparisons of quantitative and qualitative assessments of rFOV DWI between 3.0T and 5.0T.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eImage parameter\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.0T\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5.0T\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e value\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSignal-to-noise ratio (SNR)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.45\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.68\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e7.15\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.96\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eContrast-to-noise ratio (CNR)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.75\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.62\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4.47\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.74\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSignal-intensity ratio (SIR)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.06\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.39\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.76\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.77\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSharpness\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.37\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.54\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.58\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDistortion\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.57\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.58\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.53\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.55\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.763\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLesion conspicuity\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.66\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.69\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.59\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.59\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eArtifacts\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.17\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.54\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.07\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.57\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.458\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTotal image quality\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e11.69\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.50\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e13.90\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.67\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\n\\u003ch3\\u003eSubjective IQ evaluation\\u003c/h3\\u003e\\n\\u003cp\\u003eQualitative scoring of IQ, based on the 4-point Likert scale, is summarized in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. No statistically significant differences were witnessed between 3.0T and 5.0T in terms of: geometric distortion: 5.0T: 3.57\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.58 vs. 3.0T: 3.53\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.55 (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.763); presence of artifacts (e.g., motion, ghosting, susceptibility): 5.0T: 3.17\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.54 vs. 3.0T: 3.07\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.57 (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.458). However, the 5.0T rFOV DWI images received significantly higher ratings for image sharpness (5.0T: 3.58\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.50 vs. 3.0T: 2.37\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.54), lesion conspicuity (5.0T: 3.59\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.59 vs. 3.0T: 2.66\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.69), and overall IQ score (composite) (5.0T: 13.90\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.67 vs. 3.0T: 11.69\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.50) (all \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). An illustrative comparison of DWI images acquired at 3.0T and 5.0T is provided in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eQuantitative evaluation of tumor ADC values\\u003c/h2\\u003e\\u003cp\\u003eThe comparison of ADC values obtained at 3.0T and 5.0T is summarized in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. A statistically significant difference was observed in the mean ADC values between the two field strengths, with the 5.0T scanner yielding higher measurements than the 3.0T system ([0.884\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.128] \\u0026times;10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;3\\u003c/sup\\u003e mm\\u003csup\\u003e2\\u003c/sup\\u003e/s vs. [0.972\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.138] \\u0026times;10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;3\\u003c/sup\\u003e mm\\u003csup\\u003e2\\u003c/sup\\u003e/s, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Stratified analysis by T stage demonstrated that ADC values varied significantly with tumor invasiveness. Specifically, tumors staged as T2, T3, and T4 showed markedly higher ADC readings at 5.0T relative to 3.0T: T2 stage ([0.908\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.160]\\u0026times;10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;3\\u003c/sup\\u003e mm\\u003csup\\u003e2\\u003c/sup\\u003e/s vs. [0.976\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.135]\\u0026times;10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;3\\u003c/sup\\u003e mm\\u003csup\\u003e2\\u003c/sup\\u003e/s), T3 stage ([0.876\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.128]\\u0026times;10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;3\\u003c/sup\\u003e mm\\u003csup\\u003e2\\u003c/sup\\u003e/s vs. [0.974\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.147]\\u0026times;10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;3\\u003c/sup\\u003e mm\\u003csup\\u003e2\\u003c/sup\\u003e/s), and T4 stage ([0.797\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.092]\\u0026times;10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;3\\u003c/sup\\u003e mm\\u003csup\\u003e2\\u003c/sup\\u003e/s vs. [0.905\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.126]\\u0026times;10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;3\\u003c/sup\\u003emm\\u003csup\\u003e2\\u003c/sup\\u003e/s) (all \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). For the T1 subgroup (n\\u0026thinsp;=\\u0026thinsp;2), the observed difference in ADC values between 3.0T and 5.0T was not statistically significant, despite a numerical trend ([1.007\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.039] \\u0026times;10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;3\\u003c/sup\\u003e mm\\u003csup\\u003e2\\u003c/sup\\u003e/s vs. [1.026\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.062] \\u0026times;10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;3\\u003c/sup\\u003e mm\\u003csup\\u003e2\\u003c/sup\\u003e/s, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.655). Correlation analysis demonstrated a notable inverse relationship between tumor ADC values and histological T stage at both field strengths. For 3.0T, the correlation coefficient was r = \\u0026minus;\\u0026thinsp;0.685 (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and for 5.0T, r = \\u0026minus;\\u0026thinsp;0.621 (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), indicating that lower ADC values were linked to higher T stages (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eComparisons of ADC values of rFOV DWI between 3.0T and 5.0T related to histopathological T staging of rectal cancer.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStaging\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.0T(\\u0026nbsp;\\u0026times;10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;3\\u003c/sup\\u003emm\\u003csup\\u003e2\\u003c/sup\\u003e/s)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5.0T(\\u0026times;10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;3\\u003c/sup\\u003emm\\u003csup\\u003e2\\u003c/sup\\u003e/s)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e value\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.007\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.039\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.026\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.062\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.655\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.908\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.160\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.976\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.135\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.876\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.128\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.974\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.147\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.797\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.092\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.905\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.126\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.884\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.128\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.972\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.138\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eCorrelation between ADC values of rFOV DWI at 3.0T and 5.0T and histopathological T stages of rectal cancer .\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"3\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eADC value\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eT\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eStaging\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e(r value)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eP value\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e3.0T\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.685(-0.452, -0.831)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e5.0T\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.621(-0.358, -0.792)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eInterobserver agreement on IQ and ADC measurements\\u003c/h2\\u003e\\u003cp\\u003eInterobserver reliability for objective IQ metrics demonstrated robust consistency across both field strengths (all P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). The ICC values ranged from 0.778 to 0.909 at 3.0 T and 5.0T. Subjective assessments showed comparably strong agreement, with κ values ranging from 0.733 to 0.850 at 3.0T and from 0.753 to 0.847 at 5.0T (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). Regarding ADC measurements, inter-reader consistency was excellent, with ICCs ranging from 0.843 to 0.862 at both field strengths.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eInterobserver agreement of image qualities (IQ) and ADC values of rFOV DWI between 3.0T and 5.0T\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"3\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eImage parameter\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.0T\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5.0T\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSignal-to-noise ratio (SNR)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.881(0.769,0.939)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.909(0.822,0.953)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eContrast-to-noise ratio (CNR)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.874(0.752,0.936)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.893(0.790,0.945)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSignal-intensity ratio (SIR)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.831(0.571,0.914)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.778(0.556,0.888)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSharpness\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.741(0.547,0.936)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.771(0.560,0.982)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDistortion\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.850(0.692,1.008)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.847(0.677,1.017)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLesion conspicuity\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.766(0.593,0.939)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.753(0.538,0.968)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eArtifacts\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.799(0.611,0.987)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.835(0.655,1.015)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTotal image quality\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.733(0.623,0.843)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.774(0.655,0.894)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eADC\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.862(0.50,0.948)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.843(0.711,0.918)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eIn this prospective study, we demonstrated that reduced rFOV DWI at 5.0T MRI offers markedly improved IQ for rectal cancer imaging compared to 3.0T MRI. Both quantitative and qualitative assessments revealed statistically significant enhancements in image clarity, lesion visualization, and signal contrast at the higher field strength. Additionally, a significant inverse correlation was found between tumor ADC values and histological T stage, consistent across both 3.0T and 5.0T systems. Inter-reader agreement for both IQ and ADC measurements was strong, as evidenced by high ICCs and weighted kappa values, supporting the reproducibility of the findings.\\u003c/p\\u003e\\u003cp\\u003eEarlier studies have reported the benefits of rFOV DWI over traditional full-FOV (fFOV) sequences in pelvic imaging, particularly for rectal cancer. rFOV techniques have been shown to significantly mitigate susceptibility-induced artifacts, geometric distortion, and blurring, leading to more accurate delineation of tumor boundaries and internal architecture \\u003csup\\u003e[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]\\u003c/sup\\u003e. Moreover, the ability of rFOV DWI to better characterize tumor heterogeneity, including the identification of cystic, hemorrhagic, and necrotic regions, enhances diagnostic confidence in both qualitative and quantitative interpretations \\u003csup\\u003e[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]\\u003c/sup\\u003e. The transition to 5.0T imaging introduces several advantages, primarily driven by increased SNR and CNR, both of which contribute to enhanced visualization of fine anatomical structures. This is aligned with earlier research suggesting that ultra-high field MRI improves the depiction of pathological features due to better intrinsic tissue contrast \\u003csup\\u003e[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]\\u003c/sup\\u003e. Our results reaffirm this, with rFOV DWI at 5.0T showing consistently superior objective IQ metrics and subjective ratings compared to 3.0T. These observations echo prior findings in abdominal imaging. For example, Zheng et al. \\u003csup\\u003e[\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]\\u003c/sup\\u003e demonstrated that 5.0T MRI yielded higher-quality pancreatic images with greater SNR than 3.0T. Similarly, Zhang et al. \\u003csup\\u003e[\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]\\u003c/sup\\u003e reported improved subjective scoring in abdominal DWI at 5.0T, and Zheng et al. \\u003csup\\u003e[\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]\\u003c/sup\\u003e confirmed that 5.0T MRI is capable of providing anatomically and functionally adequate renal imaging. Although susceptibility artifacts are a known concern at even higher field strengths\\u0026mdash;such as 7.0T, where they are significantly more pronounced than at 3.0T \\u003csup\\u003e[\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]\\u003c/sup\\u003e\\u0026mdash;our findings showed that artifact levels at 5.0T remained manageable. In our cohort, no differences were observed in artifact-related scores between the two field strengths (3.17 at 3.0T vs. 3.07 at 5.0T, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.763). Importantly, the observed artifacts at 5.0T were minimal and did not compromise diagnostic utility. A possible explanation for the acceptable artifact burden at 5.0T lies in the relative insensitivity of motion-related artifacts to increased field strength. Instead, factors such as patient compliance, optimal positioning, and the use of stabilization devices may play a more significant role in maintaining image integrity across varying magnetic fields \\u003csup\\u003e[\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eADC, derived from DWI sequences, is a widely adopted quantitative parameter that reflects both the diffusion of water molecules in the extracellular-extravascular compartment and microvascular perfusion effects. Its utility as a non-invasive imaging biomarker in rectal cancer has been explored in multiple clinical applications, including histopathological T staging \\u003csup\\u003e[\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]\\u003c/sup\\u003e, prediction of KRAS mutation status \\u003csup\\u003e[\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]\\u003c/sup\\u003e, evaluation of treatment response to neoadjuvant chemoradiotherapy and patient prognosis \\u003csup\\u003e[\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]\\u003c/sup\\u003e, as well as forecasting the risk of metachronous metastases \\u003csup\\u003e[\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]\\u003c/sup\\u003e. Before the widespread adoption of 5.0T rFOV DWI in clinical practice, it is essential to determine whether ADC measurements are influenced by magnetic field strength. In the current study, we observed that mean ADC values obtained at 5.0T were distinctly higher than those acquired at 3.0T for rectal tumors. This observation contrasts with previous reports. For instance, Zheng et al. \\u003csup\\u003e[\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]\\u003c/sup\\u003e found no significant differences in pancreatic ADC values between 3.0T and 5.0T using rFOV DWI. Similarly, Zhang et al. \\u003csup\\u003e[\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]\\u003c/sup\\u003e demonstrated high inter-field consistency of ADC values for abdominal organs such as the liver, spleen, pancreas, and kidneys, while another study by the same group showed comparable ADC values between the renal cortex and medulla at both 3.0T and 5.0T \\u003csup\\u003e[\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]\\u003c/sup\\u003e. Several factors may account for these discrepancies. First, inter-organ physiological variations could contribute to differential diffusion behavior across tissues. Second, the technical advantages of the rFOV DWI sequence\\u0026mdash;namely, superior fat suppression, improved spatial resolution, and reduced susceptibility artifacts\\u0026mdash;may lead to distinct ADC quantification characteristics in certain anatomical regions \\u003csup\\u003e[\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]\\u003c/sup\\u003e. Moreover, ADC values are known to be affected by a range of factors including acquisition protocols, magnetic field strength, and b-value selection \\u003csup\\u003e[\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]\\u003c/sup\\u003e. In this study, ADC values also showed stage-dependent variations. Significant differences were noted between ADC values at 3.0T and 5.0T for tumors classified as T2, T3, and T4. For T1 lesions, no notable difference was observed, although the small sample size (n\\u0026thinsp;=\\u0026thinsp;2) may limit interpretability. These findings suggest that the relationship between ADC and tumor stage is influenced not only by biological factors such as intratumoral heterogeneity but also by the field strength of the MRI system.\\u003c/p\\u003e\\u003cp\\u003eSeveral prior investigations have indicated that ADC values may reflect the biological aggressiveness of rectal tumors \\u003csup\\u003e[\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]\\u003c/sup\\u003e. However, limited attention has been paid to the direct relationship between histopathological T staging and ADC measurements. In our current study, we identified a consistent inverse association between tumor ADC values and T stage at both 3.0T and 5.0T field strengths. This observation aligns with the findings of Yang et al. \\u003csup\\u003e[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]\\u003c/sup\\u003e, who also validated a negative correlation between T stage and ADC values, regardless of whether full or reduced FOV DWI sequences were used. We further observed a decreasing trend in ADC values with increasing T stage\\u0026mdash;from T1 through T4. This pattern is biologically plausible, as tumors with higher T stages typically exhibit increased cellular density, reduced extracellular space, and a more complex microenvironment. These factors restrict water molecule diffusion, thereby lowering ADC values \\u003csup\\u003e[\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]\\u003c/sup\\u003e. Thus, the declining ADC across T stages may reflect underlying tumor aggressiveness and structural compactness.\\u003c/p\\u003e\\u003cp\\u003eThis study has several notable limitations. First, the investigation primarily focused on comparing IQ metrics rather than diagnostic accuracy or lesion detection rates across different field strengths. Second, the modest cohort size may constrain the broader applicability of our findings and increase the risk of statistical deviation. Third, due to the inclusion of only two T1-stage patients, conclusions regarding ADC differences at early stages must be interpreted with caution. A larger cohort would be necessary to validate these preliminary findings and support their clinical applicability.\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eIn conclusion, our preliminary results demonstrate that rFOV DWI at 5.0T MRI offers superior IQ and improved lesion visualization compared to 3.0T MRI in rectal cancer imaging. The mean ADC values obtained at 5.0T were notably higher than those measured at 3.0T, and pretreatment ADC values from both field strengths were inversely linked to tumor T staging. These findings suggest that ADC measurements from high-resolution rFOV DWI hold potential as a non-invasive indicator for evaluating tumor invasiveness.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cdiv id=\\\"AGS1\\\" class=\\\"AbbreviationGroupSection\\\"\\u003e\\u003cdiv class=\\\"Heading\\\"\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionList\\\"\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eMRI\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eMagnetic Resonance Imaging\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eIQ\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eimage quality\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003erFOV\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003ereduced field-of-view\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eDWI\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003ediffusion-weighted imaging\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eADC\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eapparent diffusion coefficient\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eSNR\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003esignal-to-noise ratio\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eSIR\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003esignal-intensity ratio\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eCNR\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003econtrast-to-noise ratio\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eTE\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eecho time\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eFSE\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003efast spin echo\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eDCE\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003edynamic-contrast enhanced\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eROI\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eregion of interest\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eEthics approval and consent to participate:\\u003c/h2\\u003e\\u003cp\\u003eThis study was approved by the Institutional Ethics Committee of Xinhua Hospital (No. XHEC-C2024-072-1) and in accordance with the Declaration of Helsinki.\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication:\\u003c/strong\\u003e\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\u003c/p\\u003e\\u003ch2\\u003eFunding:\\u003c/h2\\u003e\\u003cp\\u003eThis study was supported by National Natural Science Foundation of China (No. 81901695), Shanghai Sailing Program (No.19YF1433100), Program of Shanghai Municipal Health Youth Talent (No. 2022YQ042), the Program of Shanghai Science and Technology Committee (No. 24TS1415200), and the Fundamental Research Funds for the Central Universities (No. YG2023QNA17and No. YG2025QNA39).\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eHuanhuan Liu and Dengbin Wang contributed to study conception and design, Qiufeng Yin, Peirong Zhang, Zhongyang Zhang, Xing Zhang contributed to acquisition of data, Xue Dong, Dongmei Shi, Zhiwei Yang and Shaofeng Duan contributed to analysis and interpretation of data, Xue Dong contributed to drafting of manuscript, Huanhuan Liu and Dengbin Wang contributed to critical revision.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\u003cp\\u003eThe authors gratefully acknowledge all of the investigators for their contributions to the study, as well as Kun Sun, who is a guarantor for the entire study.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eData are however available from the authors upon reasonable request and with permission of Liu Huanhuan.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eBray F, Laversanne M, Sung H, et al. 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J Comput Assist Tomogr. 2024;48(3):361\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"Rectal Cancer, reduced field-of-view DWI, 5.0T MRI, T staging\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7533445/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7533445/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003ePurpose\\u003c/h2\\u003e\\u003cp\\u003eTo compare image quality (IQ) of reduced field-of-view (rFOV) DWI for rectal cancer at 5.0 T compared with 3.0T and determine whether tumor ADC values are correlated with histopathological T staging.\\u003c/p\\u003e\\u003ch2\\u003eMaterials and Methods\\u003c/h2\\u003e\\u003cp\\u003eIn a prospective cohort, 36 patients diagnosed with rectal cancer underwent MRI scans on both 3.0T and 5.0T systems. Two experienced radiologists separately evaluated the subjective and objective IQ parameters. Objective IQ metrics were statistically analyzed utilizing paired t-tests. Subjective assessments were compared utilizing the Wilcoxon signed-rank test. Tumor ADC values obtained at the two magnetic field strengths were further compared, and their association with histopathological T stage was examined through Spearman\\u0026rsquo;s rank correlation.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e\\u003cp\\u003eObjective measures demonstrated evidently improved IQ on 5.0T rFOV DWI relative to 3.0T (all \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Subjective evaluations confirmed superior image clarity, lesion delineation, and overall diagnostic confidence on the 5.0T platform (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). The two systems demonstrated comparable performance with respect to image artifacts and geometric distortions, showing no meaningful statistical divergence. However, the mean tumor ADC values differed significantly between 3.0T and 5.0T imaging (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). A notable inverse correlation was identified between ADC values and histopathological T stage at both field strengths (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e\\u003cp\\u003erFOV DWI at 5.0T offers enhanced IQ and improved tumor visualization relative to 3.0T. The mean tumor ADC values were significantly different at 3.0T and 5.0T, which could be utilized for assessing histopathological T staging of rectal cancer.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Comparison of Reduced FOV Diffusion-weighted Imaging of Rectal Cancer at 5.0T ultra- high field versus 3.0T MRI: Image Quality and Histopathological T Staging\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-10-10 15:33:14\",\"doi\":\"10.21203/rs.3.rs-7533445/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-10-01T11:17:57+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-09-30T08:12:19+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"40608032963390748086511970983324104644\",\"date\":\"2025-09-30T07:51:15+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-09-30T04:48:49+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"77632580134689453425492786005475478176\",\"date\":\"2025-09-30T02:18:10+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-09-28T01:01:20+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-09-25T00:29:47+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-09-16T14:20:27+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-09-15T09:53:26+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Medical Imaging\",\"date\":\"2025-09-15T09:34:01+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"f21a596e-6b7c-42bf-8584-54f5297624c8\",\"owner\":[],\"postedDate\":\"October 10th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-12-15T16:09:15+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-7533445\",\"link\":\"https://doi.org/10.1186/s12880-025-02076-3\",\"journal\":{\"identity\":\"bmc-medical-imaging\",\"isVorOnly\":false,\"title\":\"BMC Medical Imaging\"},\"publishedOn\":\"2025-12-09 15:59:08\",\"publishedOnDateReadable\":\"December 9th, 2025\"},\"versionCreatedAt\":\"2025-10-10 15:33:14\",\"video\":\"\",\"vorDoi\":\"10.1186/s12880-025-02076-3\",\"vorDoiUrl\":\"https://doi.org/10.1186/s12880-025-02076-3\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7533445\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7533445\",\"identity\":\"rs-7533445\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}