The Zoom diffusion-weighted imaging sequence is used in gastric tumors: clinical utility, image quality, ADC value, and entropy value evaluation

The Zoom diffusion-weighted imaging sequence is used in gastric tumors: clinical utility, image quality, ADC value, and entropy value evaluation | 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 The Zoom diffusion-weighted imaging sequence is used in gastric tumors: clinical utility, image quality, ADC value, and entropy value evaluation Huan Xie, Hanwei Wang, Zhile Cao, Mimi Zhao, Diyou Chen, Yu Guo, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7330659/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To evaluate the clinical utility, image quality, apparent diffusion coefficient (ADC) value, and entropy value of diffusion weighted imaging (DWI) using echo planar imaging (EPI) with zonally oblique multi-slice (Zoom-DWI) of the gastric tumors. In addition, these values were compared with those obtained from general single-shot EPI with parallel imaging technique (General-DWI). Materials and Methods This retrospective study included 70 participants with histologically confirmed gastric tumors who underwent MRI. DWI acquisition was performed using free-breathing two-dimensional fat-suppressed General-DWI and Zoom-DWI. The image quality was qualitatively evaluated using a five-point Likert scale. Three reviewers evaluated the diagnostic performance regarding the structural conspicuity and boundary delineation of gastric tumors. Furthermore, quantitative analysis included measurement of the mean ADC and entropy values with the tumor regions. Qualitative, quantitative and diagnostic performance parameters were compared between General-DWI and Zoom-DWI using paired t test. The mean ADC and entropy values were correlation analysis between General-DWI and Zoom-DWI using the linear regression. Results The mean image quality scores for imaging noise and the mean ADC values in gastric tumors were higher in General-DWI compared with Zoom-DWI ( p < 0.05). Conversely, the tumors’ boundaries scores and sharpness scores were higher in Zoom-DWI compared with General-DWI ( p = 0.0001). However, no significant difference was observed in the mean entropy values of the gastric tumors between General-DWI and Zoom-DWI ( p = 0.788). Correlation analysis demonstrated an extremely strong correlation for both mean ADC and entropy values between the two techniques (R = 0.906 and 0.776, respectively). Additionally, the performance of Zoom-DWI images in identifying structural conspicuity of gastric tumors ( p < 0.05) and boundaries delineation ( p < 0.05) surpassed that of General-DWI. Conclusion Zoom-DWI outperformed General-DWI in tumor diagnostic performance analysis and visualization despite higher ADC values in General-DWI, with strong inter-technique correlations validating Zoom-DWI's clinical utility for gastric tumors. DWI gastric tumors imaging quality ADC entropy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction As one of the most common malignant tumors digestive system, gastric tumors account for over 1,000,000 new cases and approximately 780,000 deaths annually [ 1 – 5 ], representing a predominant cause of global cancer-related mortality [ 6 ]. Magnetic resonance diffusion weighted imaging (MR-DWI) can quantify the movements of water molecules and provide the apparent diffusion coefficient (ADC) map. It has been extensively employed for tumor identification, especially for benign-malignant distinction [ 7 , 8 ], as well as non-invasive in vivo without ionizing radiation non-invasively [ 9 ]. MR-DWI is an increasingly common clinical tool for evaluating gastric tumors, and scientists believe this imaging technique could also be used to predict how long a patient will survive. [ 10 ]. For contemporary DWI, the conventional and predominant technique is the general field of view (FOV) a single-shot echo planar imaging (EPI) pulse sequence. Its prevalence is attributable to two key advantages: high-velocity data acquisition and a reduced susceptibility to motion-induced artifacts. However, this technique has several limitations, including low resolution, susceptibility artifacts and geometric distortion [ 11 ]. Although sensitivity encoding (SENSE) and parallel imaging acceleration technology are valuable for ameliorating EPI-related distortions and reducing scan duration, their utility is constrained by a fundamental trade-off. Specifically, escalating the acceleration factor results in a degradation of the signal-to-noise ratio (SNR) due to amplified noise. [ 12 – 14 ]. In recent years, using EPI with zonally oblique multi-slice diffusion weighted imaging (Zoom-DWI) acquisition techniques have been used in MR-DWI of the gastric tumors and other parts [ 15 – 23 ]. The Zoom technique is a small FOV imaging technique, the use a 2D radiofrequency pulse to excite that covers only the organ of interest [ 24 , 25 ]. Thus, this technique can be shortened readout echo train length, increase resolution, reduced susceptibility artifacts and geometry deformation. While previous studies have demonstrated that Zoom-DWI can improve the imaging quality of gastric tumors DWI compared with General-DWI [ 15 , 16 ], the differences in geometric deformation between Zoom-DWI and General-DWI are not always significant [ 19 , 26 , 27 ]. Furthermore, it is not clear whether it leads to altered qualitative and quantitative assessment of gastric tumors and it is not an in common use sequence for clinical MR scanner. Accordingly, the present investigation was designed to assess the diagnostic performance of Zoom-DWI in the characterization of the gastric tumors. A comparative analysis was performed against conventional DWI, evaluating key parameters including image quality, ADC and entropy values. Materials and Methods Participants This study has been approved by the Ethical Committee of xxxx, xxxx (NO.2022-223-01), and all participants had previously provided written informed consent. The study cohort was composed of 77 consecutive patients diagnosed with histologically confirmed gastric cancer who had underwent abdominal MRI between March 2023 and October 2024. Of the initial 77 participants, 7 were excluded due to the presence of a gastric tube, communication disorders, or contraindications to raceanisodamine hydrochloride. The final study cohort therefore consisted of 70 patients (47 men, 23 women; mean age: 61.37 ± 11.30 years old; age range: 33–84 years; clinical stage: stage I (n=6), stage IIA (n=7), stage IIB (n=4), stage III (n=45), stage IVA (n=4), stage IVB (n=8)). Data Acquisition All abdominal MRI examinations were conducted on a 3.0 T MR system (Ingenia 3.0T CX; Philips Healthcare, the Netherlands) with a 16-channel digital coil. DWI images were obtained using a respiratory-gated, two-dimensional, fat-suppressed, single-shot EPI pulse sequence. Both General-DWI and Zoom-DWI acquisition were performed. A regular undersampling pattern based on a parallel imaging acceleration factor was utilized for both DWI sequences. Key imaging parameters are detailed below: (1) General-DWI acquisition ( 2′51″ acquisition; pulse repetition time (TR) = 2000ms; time to echo (TE) = 50ms; flip angle = 90°; field of view = 380 x 380 x 244mm; Thickness= 2.0x2.0x6.0mm; acquisition orientation = transverse; bvalues= 50, 600, and 1000 s/mm 2 ; in-plane mSENSE acceleration = 2x1), and (2) Zoom-DWI acquisition ( 2′51″ acquisition; pulse TR = 2000ms; TE = 50ms; flip angle = 90°; field of view = 180 x 180 x 98mm; Thickness= 2.0x2.0x6.0mm; acquisition orientation = transverse; bvalues= 50, 600, and 1000 s/mm 2 ; in-plane mSENSE acceleration=2x1). Iterative reconstruction algorithm of mSENSE can achieve reduced image noise. In addition to the primary sequences, the standard abdominal MRI protocol included several other acquisitions. First, dual-echo T1-weighted in-phase and opposed-phase images were obtained (TR/TE1/TE2: 120/1.15/2.30 ms; matrix, 252 × 177; FOV, 400 × 320 mm; section thickness, 6 mm; and acquisition time for 30 sections, 7s×2 ). Subsequently, two transverse T2-weighted turbo spin-echo sequences were performed using respiratory gating and Multi-Vane motion correction. The first T2-weighted sequence incorporated fat suppression (TR/TE, 2200/120 ms; matrix, 252 × 252; FOV, 380 × 380 mm; section thickness, 6 mm; and acquisition time for 32 sections, 156s), while the second was acquired without it (TR/TE, 2500/120 ms; matrix, 252 × 252; FOV, 380 × 380 mm; section thickness, 6 mm; and acquisition time for 32 sections, 156s). Finally, we also acquired s breath-hold, multi-phase dynamic-enhanced imaging series. Data P rocessing A) Pre-processing (image registration): In order to ensure accurate alignment of gastric tumors tissues in different sequence images, the registration process is divided into three steps and performed using the rigid body transformation algorithm (Rigids) of ANTs (Advanced Normalization Tools) tool, and manually corrected by a senior radiologist. For the first registration, the T2 weighted image (T2WI) of the conventional scan was fixed, and the General-DWI was registered to the T2WI. For the second registration, the Zoom-DWI is also registered to the T2WI. For the third registration, the General-DWI image was fixed, and the Zoom-DWI image was registered to the Geneal-DWI image. Finally, the registration results were checked by a senior radiologist, and the mismatch images were repeatedly corrected; B) Image segmentation: Segmentation of gastric tumors tissue was performed using the software 3D slicer 5.6.2 (https://www.slicer.org/). Tumor tissue was segmented and corrected on the maximum-level images of General-DWI and Zoom-DWI using the semi-automatic selection method (Level Tracing) in 3D slicer; C) ROI merge: Using the logical operation (Logical operation) of intersection (Intersect) method in the 3D slicer segmentation tool, the repeated regions of the same patient two-sequence segmentation images were extracted as the final region of interest (ROI: Region of Interest); D) The ROI region matches: The final segmented ROI was applied to the DWI and ZOOM DWI images; E) Statistical eigenvalue was extracted: Statistical feature values of the ROI in General-DWI images and Zoom-DWI images were calculated using Pyradiomics v3.1.0 (open source 3D Slicer extension module). We measure the entropy value to compare the amount of image information in the overlapping ROI region of the two sequences; F) Measurement of the ADC values: ADC values for General-DWI and Zoom-DWI were measured at the final area of interest level using a IntelliSpacePortal 10.1 post-processing workstation, and all images were taken in three measurements, independently by the same three technicians, the workflow of the is summarized in Fig.1. Qualitative I mage Evaluation Three radiologists with more than 10 years of experience in abdominal MRI independently and randomly assessed General-DWI and Zoom-DWI images acquired at high b value ( b = 1000 s/mm 2 ). A qualitative assessment of two parameters, image quality (regarding noise), sharpness and the delineation of gastric tumor boundaries, was conducted using a five-point Likert scale. The detailed contents are presented in Table 1. During the evaluation, radiologists were permitted to freely adjust the window level settings of DWI images at their discretion. Table 1. Image quality assessment using the 5-point Likert scale. Score Image noise Sharpness Tumors boundaries 1 Non-diagnostic Non-diagnostic Unidentifiable 2 Substantial impact on diagnosis Not sharp Difficulties in delineating lesion boundary 3 Moderate impact on diagnosis A little sharp Seen with poorly defined edges 4 Minimal impact on diagnosis Moderately sharp Well seen with poorly defined edges 5 No impact on diagnosis Satisfyingly sharp Well seen with well-defined edges Quantitative I mage Evaluation Subsequently, a quantitative analysis was performed by the same reviewers independently, including conducting image processing, and calculatingentropy values and ADC values of the gastric tumors. In addition, the average of the entropy values and ADC values triplicate measurements was calculated. Diagnostic performance analysis: s tructural conspicuity evaluation and boundaries delineation The reviewers (reviewer-1, reviewer-2, reviewer-3) evaluated the structural conspicuity and boundary delineation of gastric tumors across both image groups. The assessment encompassed the following criteria: ● Structural conspicuity (0 = not well visualized, 1 = well visualized). ● Boundaries delineation (0 = not fully delineated, 1 = fully delineated). Statistical A nalysis All statistical analyses were carried out using GraphPad Prism 8.0.1 and SPSS 23.0. To evaluate differences between General-DWI and Zoom-DWI, we performed an independent samples t-test on both qualitative, quantitative and diagnostic performance parameters. The inter-reviewer reliability was quantified using the intraclass correlation coefficient (ICC). The degree of agreement was categorized as poor (ICC < 0.50), moderate (0.50–0.75), good (0.75–0.90), or excellent (≥0.90). ADC value and entropy value were correlation analysis between General-DWI and Zoom-DWI using the linear regression. Similarly, the correlation strength were interpreted to represent extremely weak (0.80), where high correlations indicating a high degree of validity for the Zoom-DWI scans. P < 0.05 was considered as the significance level. Results Demographic The basic demographic information for the study cohort were as follows: height (mean ± SD, 160.7 ± 8.09 cm; range, 140–176 cm), body weight (mean ± SD, 57.63 ± 8.48 kg; range, 37.4–75.9 kg), and body mass index (mean ± SD, 22.3 ± 2.79 kg/m 2 , range, 16.34–28.40kg/m 2 ). The clinical stage of the gastric tumors was as follows: stage I (n = 6), stage IIA (n = 7), stage IIB (n = 4), stage III (n = 45), stage IVA (n = 4), and stage IVB (n = 8). Qualitative Image Analysis Table 2 presents the summary of the qualitative image evaluation, including the image quality scores of image noise, sharpness and gastric tumors boundaries, ICC values among three reviewers, as well as group comparisons between General-DWI and Zoom-DWI. Regarding image noise specifically, the mean scores assigned by the three reviewers were consistently higher for General-DWI than Zoom-DWI ( P = 0.0019, Fig. 2 ). Conversely, the mean image quality scores for tumors boundaries and sharpness were consistently lower in General-DWI than Zoom-DWI ( P = 0.0001, Fig. 2 ). Inter-reviewer reliability among the three reviewers was excellent for assessing image noise (ICC = 0.92–0.93) and good for sharpness(ICC = 0.85–0.86), while demonstrating moderate to good agreement for evaluating tumor boundaries (ICC = 0.69–0.83). Table 2 The image quality scores for imaging noise and tumors boundaries. Parameter General-DWI Zoom-DWI P value Imaging noise Reviewer-1 3.79 ± 0.89(2–5) 3.60 ± 1.01(2–5) 0.047 Reviewer-2 3.71 ± 0.94(2–5) 3.50 ± 1.05(1–5) 0.019 Reviewer-3 3.78 ± 0.95(2–5) 3.54 ± 1.10(1–5) 0.006 ICC (95%CI) 0.93 (0.90–0.95) 0.92 (0.88–0.94) N.A. Mean scores reviewer (1–3) 3.76 ± 0.91(2–5) 3.55 ± 1.03(1.33-5.00) 0.019 Sharpness Reviewer-1 3.27 ± 0.84(2–5) 3.81 ± 0.78(2–5) 0.0001 Reviewer-2 3.24 ± 0.85(2–5) 3.86 ± 0.83(2–5) 0.0001 Reviewer-3 3.30 ± 0.88(1–5) 3.90 ± 0.85(2–5) 0.0001 ICC (95%CI) 0.85(0.78–0.90) 0.86(0.79–0.91) N.A. Mean scores reviewer (1–3) 3.27 ± 0.75(1.67–4.67) 3.86 ± 0.72(2–5) 0.0001 Tumors boundaries Reviewer-1 3.44 ± 0.92(2–5) 3.80 ± 0.82(2–5) 0.0001 Reviewer-2 3.31 ± 0.96(1–5) 3.76 ± 0.89(2–5) 0.0001 Reviewer-3 3.36 ± 1.01(1–5) 3.79 ± 0.89(2–5) 0.0001 ICC (95%CI) 0.83 (0.76–0.89) 0.69 (0.58–0.78) N.A. Mean scores reviewer (1–3) 3.37 ± 0.91(1.33-5) 3.78 ± 0.77(2–5) 0.0001 (Note. DWI, diffusion-weighted imaging; General-DWI, DWI using general single-shot echo planar imaging with parallel imaging; Zoom-DWI, DWI using echo planar imaging with zonally oblique mult-slice ; CI, confidence interval; ICC, intraclass correlation coefficient; N.A., not applicable.) Quantitative Image Analysis The mean ADC values, entropy values, and ICC for the gastric tumors of General-DWI and Zoom-DWI are showed in Tables 3 , Fig. 3 and Fig. 4 . Statistically significant differences between General-DWI and Zoom-DWI were observed only for the mean ADC value of gastric tumors in all three reviewers ( P = 0.0001), with General-DWI producing higher values. Conversely, there was no significant difference in the overall mean entropy values between the two protocols ( P = 0.788). A subsequent analysis of individual reviewer data revealed that this lack of significance was not uniform, as a significant difference in entropy was identified in the measurements provided by reviewer 2 ( P = 0.009). The inter- reviewer reliability among the three reviewers was excellent for ADC values (ICC = 0.94–0.96) but demonstrated more moderate to good agreement for entropy values (ICC = 0.69–0.79). Table 3 ADC values and entropy values of gastric tumors on General-DWI and Zoom-DWI. Parameter General-DWI Zoom-DWI P value ADC value (x10 − 3 mm 2 /s) Reviewer-1 1.42 ± 0.27 1.34 ± 0.25 0.0001 Reviewer-2 1.45 ± 0.26 1.33 ± 0.24 0.0001 Reviewer-3 1.42 ± 0.27 1.34 ± 0.27 0.0001 ICC (95%CI) 0.94 (0.91–0.96) 0.96 (0.95–0.98) N.A. Mean ADC value reviewer (1–3) 1.43 ± 0.26 1.33 ± 0.25 0.0001 Entropy value Reviewer-1 4.58 ± 0.84 4.59 ± 0.78 0.973 Reviewer-2 4.75 ± 0.79 4.89 ± 0.71 0.009 Reviewer-3 4.79 ± 0.82 4.68 ± 0.71 0.093 ICC (95%CI) 0.69 (0.58–0.78) 0.79 (0.72–0.86) N.A. Mean entropy value reviewer (1–3) 4.70 ± 0.73 4.72 ± 0.68 0.788 (Note. DWI, diffusion-weighted imaging; General-DWI, DWI using general single-shot echo planar imaging with parallel imaging; Zoom-DWI, DWI using echo planar imaging with zonally oblique mult-slice ; ADC, apparent diffusion coefficient; CI, confidence interval; ICC, intraclass correlation coefficient; N.A., not applicable.) Convergent Validity Analyses The correlation of ADC and entropy values measured between the General-DWI and Zoom-DWI is presented in Fig. 5 and Fig. 6 , respectively. In the gastric tumors ADC values, the mean ADC value correlation analysis results between General-DWI and Zoom-DWI showed the extremely strength correlation in three reviewers (R = 0.906). In the gastric tumors entropy values, the mean entropy value correlation analysis results between General-DWI and Zoom-DWI the strength correlation in three reviewers ( P = 0.776). The mean ADC value and mean entropy value correlation analysis results indicating, the Zoom-DWI scans is validity to the gastric tumors. Diagnostic performance analysis: structural conspicuity evaluation and boundaries delineation Additionally, the structural conspicuity evaluation and boundaries delineation of gastric tumors were more clearly and comprehensively depicted on Zoom-DWI images compared with General-DWI images. The visualization and delineated of the gastric tumors structural conspicuity and boundaries delineation improved using the Zoom-DWI images than using the General-DWI images are showed in Tables 4 (p < 0.05 for all the above). Table 4 Gastric tumor structural conspicuity evaluation and boundaries delineation with Zoom-DWI images and General-DWI images in 70 participants Parameter Zoom-DWI General-DWI P value Structural conspicuity Reviewer-1 65/70(92.9) 57/70(81.4) 0.008 Reviewer-2 66/70(94.3) 56/70(80.0) 0.002 Reviewer-3 66/70(94.3) 58/70(82.9) 0.008 Boundaries delineation Reviewer-1 64/70(91.4) 58/70(84.3) 0.03 Reviewer-2 63/70(90.0) 56/70(80.0) 0.04 Reviewer-3 63/70(90.0) 56/70(80.0) 0.02 (Note. DWI, diffusion-weighted imaging; General-DWI, DWI using general single-shot echo planar imaging with parallel imaging; Zoom-DWI, DWI using echo planar imaging with zonally oblique mult-slice.) Abbreviations Zoom Zonally oblique multi-slice ADC Apparent diffusion coefficient DWI Diffusion weighted imaging EPI Echo planar imaging FOV Field of view SENSE Sensitivity encoding mSENSE Modified sensitivity encoding ANTs Advanced Normalization Tools ICC Intraclass correlation coefficient CI Confidence interval Declarations Author Contribution All authors contributed to the study conception and design. Huan Xie, Hanwei wang and Zhile Cao contributed the equally as first author, Xueqin Wang, Qisheng Ran and Shunan Wang contributed the equally as corresponding author. Guarantor of integrity of entire study, SW. Study concepts/study design or data acquisition of data analysis/interpretation, all authors. Manuscript drafting or manuscript revision for important intellectual content, HX. Manuscript final version approval, all authors. Agrees to ensure any questions related to the work are appropriately resolved, all authors. Literature research, HX. Clinical studies, HX, HW and XW. 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High-resolution diffusion-weighted magnetic resonance imaging in patients with locally advanced breast cancer. Acad Radiol. 2012;19(5):526–34. Takeuchi M, Matsuzaki K, Harada M. Evaluating Myometrial Invasion in Endometrial Cancer: Comparison of Reduced Field-of-view Diffusion-weighted Imaging and Dynamic Contrast-enhanced MR Imaging. Magn Reson Med Sci. 2018;17(1):28–34. Ota T, Hori M, Onishi H, Sakane M, Tsuboyama T, Tatsumi M, Nakamoto A, Kimura T, Narumi Y, Tomiyama N. Preoperative staging of endometrial cancer using reduced field-of-view diffusion-weighted imaging: a preliminary study. Eur Radiol. 2017;27(12):5225–5235. Feng Z, Min X, Sah VK, Li L, Cai J, Deng M, Wang L. Comparison of field-of-view (FOV) optimized and constrained undistorted single shot (FOCUS) with conventional DWI for the evaluation of prostate cancer. Clin Imaging. 2015 Sep-Oct;39(5):851–5. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7330659","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":499423470,"identity":"e1e6ad7d-5eb7-4903-8aa0-e8c994edd4a7","order_by":0,"name":"Huan Xie","email":"","orcid":"","institution":"Daping Hospital, Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Xie","suffix":""},{"id":499423472,"identity":"8596aab2-70bd-498c-8e0d-b7c1773acf4b","order_by":1,"name":"Hanwei Wang","email":"","orcid":"","institution":"Daping Hospital, Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hanwei","middleName":"","lastName":"Wang","suffix":""},{"id":499423474,"identity":"5b746b53-09fd-46ff-a840-e371162318c9","order_by":2,"name":"Zhile Cao","email":"","orcid":"","institution":"Daping Hospital, Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhile","middleName":"","lastName":"Cao","suffix":""},{"id":499423476,"identity":"01101abd-3b5f-44fc-9c87-0410f9b168ce","order_by":3,"name":"Mimi Zhao","email":"","orcid":"","institution":"Daping Hospital, Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mimi","middleName":"","lastName":"Zhao","suffix":""},{"id":499423477,"identity":"c6eefff1-9070-410e-945d-08608288d4a9","order_by":4,"name":"Diyou Chen","email":"","orcid":"","institution":"Daping Hospital, Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Diyou","middleName":"","lastName":"Chen","suffix":""},{"id":499423478,"identity":"d5c69544-0640-4a35-8d41-b368fda4dc6b","order_by":5,"name":"Yu Guo","email":"","orcid":"","institution":"Daping Hospital, Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Guo","suffix":""},{"id":499423480,"identity":"6a9ccb86-756b-4ca5-9f47-8978348b2f18","order_by":6,"name":"Junling Liu","email":"","orcid":"","institution":"Daping Hospital, Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junling","middleName":"","lastName":"Liu","suffix":""},{"id":499423482,"identity":"58ff79a1-34db-4898-90f9-719cdb0a1098","order_by":7,"name":"Letian Zhang","email":"","orcid":"","institution":"Daping Hospital, Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Letian","middleName":"","lastName":"Zhang","suffix":""},{"id":499423484,"identity":"0452169e-b38f-44fd-bf31-d93f7e266cca","order_by":8,"name":"Xueqin Wang","email":"","orcid":"","institution":"Daping Hospital, Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xueqin","middleName":"","lastName":"Wang","suffix":""},{"id":499423486,"identity":"ce503e28-fb58-4662-8d1c-cb9fc6871f61","order_by":9,"name":"Qisheng Ran","email":"","orcid":"","institution":"Daping Hospital, Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qisheng","middleName":"","lastName":"Ran","suffix":""},{"id":499423487,"identity":"93d800fe-fbca-40ce-b7a4-fd4293f40116","order_by":10,"name":"Shunan Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYLACxgYQyXwAyk0gWgtbYgOpWngMidNicPzs4Zc/dxzO45/d8/3RzZzDDPzsOQYMP3fg0XImL82a98zhYok7Zzc25247zCDZ88aAsfcMbi1mB3LMjBnbDic23MiFaDG4kWPAzNiGR8v5N2aGP4Fa5t/IeQjWYk9Qy40c4we8QC0bbuQwQmyRIKDF/sYbM2betvTEjTfSDGfnbkvnkTjzrOBgLx4tkv05xh9/tlknzruR/OBz7jZrOf725I0PfuLRAgRsEsg8HhBxAK8GYEL5QEDBKBgFo2AUjHQAAEbgWza44acQAAAAAElFTkSuQmCC","orcid":"","institution":"Daping Hospital, Army Medical University","correspondingAuthor":true,"prefix":"","firstName":"Shunan","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-08-09 01:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7330659/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7330659/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89398807,"identity":"2bdfd050-c248-48c9-ac72-e5530ce564d3","added_by":"auto","created_at":"2025-08-19 13:54:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":98885,"visible":true,"origin":"","legend":"\u003cp\u003eImage processing workflow: A) Pre-processing (image registration); B) Image segmentation; C) ROI merge; D) The ROI region matches; E) Statistical eigenvalue was extracted; F) Measurement of the ADC values.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7330659/v1/a6ff0ab21808d7ce015bc20d.jpg"},{"id":89397265,"identity":"cd727b92-932e-4dbc-8337-60c4776deb54","added_by":"auto","created_at":"2025-08-19 13:46:32","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70133,"visible":true,"origin":"","legend":"\u003cp\u003eDiffusion-weighted imaging (DWI) images focused on the gastric in three with gastric cancer. Figure a, c, e DWI image using general single-shot EPI with parallel imaging technique (General-DWI), and Figure b, d, f DWI image using echo planar imaging (EPI) with zonally oblique multi-slice (Zoom-DWI). The image noise of the General-DWI was better than that of the Zoom-DWI, and the tumors boundaries show of the Zoom-DWI was better than that of the General-DWI, as illustrated by Figure a v.s. b, c v.s. d, and e v.s. f.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7330659/v1/468019367633d32eaf7c1522.jpg"},{"id":89398808,"identity":"7c356f7a-ee9a-4952-a604-76d1631aa00d","added_by":"auto","created_at":"2025-08-19 13:54:32","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":39409,"visible":true,"origin":"","legend":"\u003cp\u003eThe ADC values and mean ADC value was higher in General-DWI compared with Zoom-DWI in three reviewers.\u003c/p\u003e\n\u003cp\u003e(Note. DWI, diffusion-weighted imaging; General-DWI, DWI using general single-shot echo planar imaging with parallel imaging; Zoom-DWI, DWI using echo planar imaging with zonally oblique mult-slice ; ADC, apparent diffusion coefficient; ***, P＜0.001.)\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7330659/v1/3b9bab4dac692e59385dcb4c.jpg"},{"id":89397268,"identity":"5a27632c-7d62-47d5-9954-2610d72792f2","added_by":"auto","created_at":"2025-08-19 13:46:32","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":36453,"visible":true,"origin":"","legend":"\u003cp\u003eThe entropy values and mean entropy value was be close to in General-DWI compared with Zoom-DWI in three reviewers.\u003c/p\u003e\n\u003cp\u003e(Note. DWI, diffusion-weighted imaging; General-DWI, DWI using general single-shot echo planar imaging with parallel imaging; Zoom-DWI, DWI using echo planar imaging with zonally oblique mult-slice ; **, P＜0.01; ns, not significant.)\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7330659/v1/f93b8c12e65395b75c623512.jpg"},{"id":89397266,"identity":"32cad874-a61f-4b27-aed4-bd5f65684425","added_by":"auto","created_at":"2025-08-19 13:46:32","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":71321,"visible":true,"origin":"","legend":"\u003cp\u003eThe ADC values and mean ADC value correlation analysis results between General-DWI and Zoom-DWI the extremely strength correlation in three reviewers.\u003c/p\u003e\n\u003cp\u003e(Note. DWI, diffusion-weighted imaging; General-DWI, DWI using general single-shot echo planar imaging with parallel imaging; Zoom-DWI, DWI using echo planar imaging with zonally oblique mult-slice ; ADC, apparent diffusion coefficient.)\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7330659/v1/6d652c69123b08adb3702869.jpg"},{"id":89397269,"identity":"b5c98bcc-a900-4726-b1e1-01074941073a","added_by":"auto","created_at":"2025-08-19 13:46:32","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":72361,"visible":true,"origin":"","legend":"\u003cp\u003eThe entropy values and mean entropy value correlation analysis results between General-DWI and Zoom-DWI the strength correlation in three reviewers.\u003c/p\u003e\n\u003cp\u003e(Note. DWI, diffusion-weighted imaging; General-DWI, DWI using general single-shot echo planar imaging with parallel imaging; Zoom-DWI, DWI using echo planar imaging with zonally oblique mult-slice.)\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7330659/v1/0a2424d96b7c001dd5519d49.jpg"},{"id":90355024,"identity":"8a1c7876-aa8b-4fcf-86f3-06e8c91e96fb","added_by":"auto","created_at":"2025-09-01 20:16:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1186905,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7330659/v1/f34c1c07-e51e-4540-bfc1-971167f38762.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Zoom diffusion-weighted imaging sequence is used in gastric tumors: clinical utility, image quality, ADC value, and entropy value evaluation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs one of the most common malignant tumors digestive system, gastric tumors account for over 1,000,000 new cases and approximately 780,000 deaths annually [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], representing a predominant cause of global cancer-related mortality [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Magnetic resonance diffusion weighted imaging (MR-DWI) can quantify the movements of water molecules and provide the apparent diffusion coefficient (ADC) map. It has been extensively employed for tumor identification, especially for benign-malignant distinction [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], as well as non-invasive \u003cem\u003ein vivo\u003c/em\u003e without ionizing radiation non-invasively [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. MR-DWI is an increasingly common clinical tool for evaluating gastric tumors, and scientists believe this imaging technique could also be used to predict how long a patient will survive. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor contemporary DWI, the conventional and predominant technique is the general field of view (FOV) a single-shot echo planar imaging (EPI) pulse sequence. Its prevalence is attributable to two key advantages: high-velocity data acquisition and a reduced susceptibility to motion-induced artifacts. However, this technique has several limitations, including low resolution, susceptibility artifacts and geometric distortion [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Although sensitivity encoding (SENSE) and parallel imaging acceleration technology are valuable for ameliorating EPI-related distortions and reducing scan duration, their utility is constrained by a fundamental trade-off. Specifically, escalating the acceleration factor results in a degradation of the signal-to-noise ratio (SNR) due to amplified noise. [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn recent years, using EPI with zonally oblique multi-slice diffusion weighted imaging (Zoom-DWI) acquisition techniques have been used in MR-DWI of the gastric tumors and other parts [\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19 CR20 CR21 CR22\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The Zoom technique is a small FOV imaging technique, the use a 2D radiofrequency pulse to excite that covers only the organ of interest [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Thus, this technique can be shortened readout echo train length, increase resolution, reduced susceptibility artifacts and geometry deformation. While previous studies have demonstrated that Zoom-DWI can improve the imaging quality of gastric tumors DWI compared with General-DWI [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], the differences in geometric deformation between Zoom-DWI and General-DWI are not always significant [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Furthermore, it is not clear whether it leads to altered qualitative and quantitative assessment of gastric tumors and it is not an in common use sequence for clinical MR scanner.\u003c/p\u003e\u003cp\u003eAccordingly, the present investigation was designed to assess the diagnostic performance of Zoom-DWI in the characterization of the gastric tumors. A comparative analysis was performed against conventional DWI, evaluating key parameters including image quality, ADC and entropy values.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has been approved by the Ethical Committee of xxxx, xxxx\u0026nbsp;(NO.2022-223-01), and all participants had previously provided written informed consent.\u0026nbsp;The study cohort was composed of 77 consecutive patients diagnosed with histologically confirmed gastric cancer who had underwent abdominal MRI between March 2023 and October 2024. Of the initial 77 participants, 7 were excluded due to the presence of a\u0026nbsp;gastric tube, communication disorders, or contraindications to raceanisodamine hydrochloride. The final study cohort therefore consisted of 70 patients (47 men, 23 women; mean age: 61.37 \u0026plusmn; 11.30 years old; age range: 33\u0026ndash;84 years; clinical stage: stage I (n=6), stage IIA (n=7), stage IIB (n=4), stage III (n=45), stage IVA (n=4), stage IVB (n=8)).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eAcquisition\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll abdominal MRI examinations were conducted on a 3.0 T MR system (Ingenia 3.0T CX; Philips Healthcare, the Netherlands) with a 16-channel digital coil. DWI images were obtained using a respiratory-gated, two-dimensional, fat-suppressed, single-shot EPI pulse sequence. Both General-DWI and Zoom-DWI acquisition were performed. A regular undersampling pattern based on a parallel imaging acceleration factor was utilized for both DWI sequences. Key imaging parameters are detailed below: (1) General-DWI\u0026nbsp;acquisition ( 2\u0026prime;51\u0026Prime; acquisition; pulse repetition time (TR) = 2000ms; time to echo (TE) = 50ms; flip angle = 90\u0026deg;; field of view = 380 x 380 x 244mm; Thickness= 2.0x2.0x6.0mm; acquisition orientation = transverse;\u0026nbsp;bvalues= 50, 600, and 1000 s/mm\u003csup\u003e2\u003c/sup\u003e;\u0026nbsp;in-plane mSENSE acceleration = 2x1), and (2) Zoom-DWI acquisition ( 2\u0026prime;51\u0026Prime; acquisition; pulse TR = 2000ms; TE = 50ms; flip angle = 90\u0026deg;; field of view = 180 x 180 x 98mm; Thickness= 2.0x2.0x6.0mm; acquisition orientation = transverse;\u0026nbsp;bvalues= 50, 600, and 1000 s/mm\u003csup\u003e2\u003c/sup\u003e; in-plane mSENSE acceleration=2x1). Iterative reconstruction algorithm of mSENSE can achieve reduced image noise.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition to the primary sequences, the standard abdominal MRI protocol included several other acquisitions. First, dual-echo T1-weighted in-phase and opposed-phase images were obtained (TR/TE1/TE2: 120/1.15/2.30 ms; matrix, 252 \u0026times; 177; FOV, 400 \u0026times; 320 mm; section thickness, 6 mm; and acquisition time for 30 sections, 7s\u0026times;2 ). Subsequently, two transverse T2-weighted turbo spin-echo sequences were performed using respiratory gating and Multi-Vane motion correction. The first T2-weighted sequence incorporated fat suppression (TR/TE, 2200/120 ms; matrix, 252 \u0026times; 252; FOV, 380 \u0026times; 380 mm; section thickness, 6 mm; and acquisition time for 32 sections, 156s), while the second was acquired without it (TR/TE, 2500/120 ms; matrix, 252 \u0026times; 252; FOV, 380 \u0026times; 380 mm; section thickness, 6 mm; and acquisition time for 32 sections, 156s). Finally, we also acquired s breath-hold, multi-phase dynamic-enhanced imaging series.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003erocessing\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA) Pre-processing (image registration): In order to ensure accurate alignment of gastric tumors tissues in different sequence images, the registration process is divided into three steps and performed using the rigid body transformation algorithm (Rigids) of ANTs (Advanced Normalization Tools) tool, and manually corrected by a senior radiologist. For the first registration, the T2 weighted image (T2WI) of the conventional scan was fixed, and the General-DWI was registered to the T2WI. For the second registration, the Zoom-DWI is also registered to the T2WI. For the third registration, the General-DWI image was fixed, and the Zoom-DWI image was registered to the Geneal-DWI image. Finally, the registration results were checked by a senior radiologist, and the mismatch images were repeatedly corrected; B) Image segmentation: Segmentation of gastric tumors tissue was performed using the software 3D slicer 5.6.2 (https://www.slicer.org/). Tumor tissue was segmented and corrected on the maximum-level images of General-DWI and Zoom-DWI using the semi-automatic selection method (Level Tracing) in 3D slicer; C) ROI merge: Using the logical operation (Logical operation) of intersection (Intersect) method in the 3D slicer segmentation tool, the repeated regions of the same patient two-sequence segmentation images were extracted as the final region of interest (ROI: Region of Interest); D) The ROI region matches: The final segmented ROI was applied to the DWI and ZOOM DWI images; E) Statistical eigenvalue was extracted: Statistical feature values of the ROI in General-DWI images and Zoom-DWI images were calculated using Pyradiomics v3.1.0 (open source 3D Slicer extension module). We measure the entropy value to compare the amount of image information in the overlapping ROI region of the two sequences; F) Measurement of the ADC values: ADC values for General-DWI and Zoom-DWI were measured at the final area of interest level using a IntelliSpacePortal 10.1 post-processing workstation, and all images were taken in three measurements, independently by the same three technicians, the workflow of the is summarized in Fig.1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eQualitative\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eI\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003emage\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eEvaluation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree radiologists with more than 10 years of experience in abdominal MRI independently and randomly assessed General-DWI and Zoom-DWI images acquired at high \u003cem\u003eb\u0026nbsp;\u003c/em\u003evalue (\u003cem\u003eb\u0026nbsp;\u003c/em\u003e= 1000 s/mm\u003csup\u003e2\u003c/sup\u003e). A qualitative assessment of two parameters, image quality (regarding noise), sharpness\u0026nbsp;and the delineation of gastric tumor boundaries, was conducted using a five-point\u0026nbsp;Likert\u0026nbsp;scale.\u0026nbsp;The detailed\u0026nbsp;contents\u0026nbsp;are\u0026nbsp;presented in Table 1.\u0026nbsp;During the evaluation, radiologists were permitted to freely adjust the window level settings of DWI images at their discretion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eImage quality assessment using the 5-point Likert scale.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"559\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003eImage noise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eSharpness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eTumors boundaries\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003eNon-diagnostic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eNon-diagnostic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eUnidentifiable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003eSubstantial impact on diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eNot sharp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eDifficulties in delineating\u003c/p\u003e\n \u003cp\u003elesion boundary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003eModerate impact on diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eA little sharp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eSeen with poorly defined edges\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003eMinimal impact on diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eModerately sharp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eWell seen with poorly defined\u003c/p\u003e\n \u003cp\u003eedges\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003eNo impact on diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eSatisfyingly sharp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eWell seen with well-defined\u003c/p\u003e\n \u003cp\u003eedges\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eQuantitative\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eI\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003emage\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eEvaluation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubsequently, a quantitative analysis was performed by the same reviewers independently, including conducting image processing, and calculatingentropy values and ADC values of the gastric tumors. In addition, the average of the entropy values and ADC values triplicate measurements was calculated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDiagnostic performance analysis:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003es\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003etructural conspicuity\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;evaluation and\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eboundaries\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003edelineation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe reviewers (reviewer-1, reviewer-2, reviewer-3) evaluated the structural conspicuity and boundary delineation of gastric tumors across both image groups. The assessment encompassed the following criteria:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e● Structural conspicuity (0 = not well visualized, 1 = well visualized).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e● Boundaries delineation (0 = not fully delineated, 1 = fully delineated).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eA\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003enalysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were carried out using GraphPad Prism 8.0.1 and SPSS 23.0. To evaluate differences between General-DWI and Zoom-DWI, we performed an independent samples t-test on both qualitative, quantitative and diagnostic performance parameters. The inter-reviewer reliability was quantified using the intraclass correlation coefficient (ICC). The degree of agreement was categorized as poor (ICC \u0026lt; 0.50), moderate (0.50\u0026ndash;0.75), good (0.75\u0026ndash;0.90), or excellent (\u0026ge;0.90). ADC value and entropy value were correlation analysis between General-DWI and Zoom-DWI using the linear regression. Similarly, the correlation strength were interpreted to represent extremely weak (\u0026lt;0.20), weak (0.21\u0026ndash;0.40), moderate (0.41\u0026ndash;0.60), strong (0.61\u0026ndash;0.80), and extremely strong (\u0026gt;0.80), where high correlations indicating a high degree of validity for the Zoom-DWI scans. \u0026nbsp;P \u0026lt; 0.05 was considered as the significance level.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eDemographic\u003c/h2\u003e\n \u003cp\u003eThe basic demographic information for the study cohort were as follows: height (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, 160.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.09 cm; range, 140\u0026ndash;176 cm), body weight (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, 57.63\u0026thinsp;\u0026plusmn;\u0026thinsp;8.48 kg; range, 37.4\u0026ndash;75.9 kg), and body mass index (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, 22.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79 kg/m\u003csup\u003e2\u003c/sup\u003e, range, 16.34\u0026ndash;28.40kg/m\u003csup\u003e2\u003c/sup\u003e). The clinical stage of the gastric tumors was as follows: stage I (n\u0026thinsp;=\u0026thinsp;6), stage IIA (n\u0026thinsp;=\u0026thinsp;7), stage IIB (n\u0026thinsp;=\u0026thinsp;4), stage III (n\u0026thinsp;=\u0026thinsp;45), stage IVA (n\u0026thinsp;=\u0026thinsp;4), and stage IVB (n\u0026thinsp;=\u0026thinsp;8).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eQualitative Image Analysis\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the summary of the qualitative image evaluation, including the image quality scores of image noise, sharpness and gastric tumors boundaries, ICC values among three reviewers, as well as group comparisons between General-DWI and Zoom-DWI. Regarding image noise specifically, the mean scores assigned by the three reviewers were consistently higher for General-DWI than Zoom-DWI (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.0019, Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Conversely, the mean image quality scores for tumors boundaries and sharpness were consistently lower in General-DWI than Zoom-DWI (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.0001, Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Inter-reviewer reliability among the three reviewers was excellent for assessing image noise (ICC\u0026thinsp;=\u0026thinsp;0.92\u0026ndash;0.93) and good for sharpness(ICC\u0026thinsp;=\u0026thinsp;0.85\u0026ndash;0.86), while demonstrating moderate to good agreement for evaluating tumor boundaries (ICC\u0026thinsp;=\u0026thinsp;0.69\u0026ndash;0.83).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe image quality scores for imaging noise and tumors boundaries.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeneral-DWI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZoom-DWI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImaging noise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89(2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01(2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94(2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05(1\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95(2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10(1\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICC (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93 (0.90\u0026ndash;0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92 (0.88\u0026ndash;0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN.A.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean scores reviewer (1\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91(2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03(1.33-5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSharpness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84(2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78(2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85(2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83(2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88(1\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85(2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICC (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85(0.78\u0026ndash;0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86(0.79\u0026ndash;0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN.A.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean scores reviewer (1\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75(1.67\u0026ndash;4.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72(2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumors boundaries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92(2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82(2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96(1\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89(2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01(1\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89(2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICC (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83 (0.76\u0026ndash;0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69 (0.58\u0026ndash;0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN.A.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean scores reviewer (1\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91(1.33-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77(2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e(Note. DWI, diffusion-weighted imaging; General-DWI, DWI using general single-shot echo planar imaging with parallel imaging; Zoom-DWI, DWI using echo planar imaging with zonally oblique mult-slice ; CI, confidence interval; ICC, intraclass correlation coefficient; N.A., not applicable.)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eQuantitative Image Analysis\u003c/h2\u003e\n \u003cp\u003eThe mean ADC values, entropy values, and ICC for the gastric tumors of General-DWI and Zoom-DWI are showed in Tables \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Statistically significant differences between General-DWI and Zoom-DWI were observed only for the mean ADC value of gastric tumors in all three reviewers (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.0001), with General-DWI producing higher values. Conversely, there was no significant difference in the overall mean entropy values between the two protocols (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.788). A subsequent analysis of individual reviewer data revealed that this lack of significance was not uniform, as a significant difference in entropy was identified in the measurements provided by reviewer 2 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009). The inter- reviewer reliability among the three reviewers was excellent for ADC values (ICC\u0026thinsp;=\u0026thinsp;0.94\u0026ndash;0.96) but demonstrated more moderate to good agreement for entropy values (ICC\u0026thinsp;=\u0026thinsp;0.69\u0026ndash;0.79).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eADC values and entropy values of gastric tumors on General-DWI and Zoom-DWI.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeneral-DWI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZoom-DWI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADC value (x10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003emm\u003csup\u003e2\u003c/sup\u003e/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICC (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94 (0.91\u0026ndash;0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96 (0.95\u0026ndash;0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN.A.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean ADC value reviewer (1\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEntropy value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICC (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69 (0.58\u0026ndash;0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79 (0.72\u0026ndash;0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN.A.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean entropy value reviewer (1\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e(Note. DWI, diffusion-weighted imaging; General-DWI, DWI using general single-shot echo planar imaging with parallel imaging; Zoom-DWI, DWI using echo planar imaging with zonally oblique mult-slice ; ADC, apparent diffusion coefficient; CI, confidence interval; ICC, intraclass correlation coefficient; N.A., not applicable.)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eConvergent Validity Analyses\u003c/h2\u003e\n \u003cp\u003eThe correlation of ADC and entropy values measured between the General-DWI and Zoom-DWI is presented in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, respectively. In the gastric tumors ADC values, the mean ADC value correlation analysis results between General-DWI and Zoom-DWI showed the extremely strength correlation in three reviewers (R\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.906). In the gastric tumors entropy values, the mean entropy value correlation analysis results between General-DWI and Zoom-DWI the strength correlation in three reviewers (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.776). The mean ADC value and mean entropy value correlation analysis results indicating, the Zoom-DWI scans is validity to the gastric tumors.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eDiagnostic performance analysis: structural conspicuity evaluation and boundaries delineation\u003c/h2\u003e\n \u003cp\u003eAdditionally, the structural conspicuity evaluation and boundaries delineation of gastric tumors were more clearly and comprehensively depicted on Zoom-DWI images compared with General-DWI images. The visualization and delineated of the gastric tumors structural conspicuity and boundaries delineation improved using the Zoom-DWI images than using the General-DWI images are showed in Tables \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all the above).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGastric tumor structural conspicuity evaluation and boundaries delineation with Zoom-DWI images and General-DWI images in 70 participants\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZoom-DWI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeneral-DWI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStructural conspicuity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65/70(92.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57/70(81.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66/70(94.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56/70(80.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66/70(94.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58/70(82.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBoundaries delineation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64/70(91.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58/70(84.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63/70(90.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56/70(80.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReviewer-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63/70(90.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56/70(80.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e(Note. DWI, diffusion-weighted imaging; General-DWI, DWI using general single-shot echo planar imaging with parallel imaging; Zoom-DWI, DWI using echo planar imaging with zonally oblique mult-slice.)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eZoom \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Zonally oblique multi-slice\u003c/p\u003e\n\u003cp\u003eADC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Apparent diffusion coefficient\u003c/p\u003e\n\u003cp\u003eDWI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Diffusion weighted imaging\u003c/p\u003e\n\u003cp\u003eEPI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Echo planar imaging\u003c/p\u003e\n\u003cp\u003eFOV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Field of view\u003c/p\u003e\n\u003cp\u003eSENSE \u0026nbsp; \u0026nbsp; \u0026nbsp;Sensitivity encoding\u003c/p\u003e\n\u003cp\u003emSENSE \u0026nbsp; \u0026nbsp; Modified\u0026nbsp;sensitivity encoding\u003c/p\u003e\n\u003cp\u003eANTs \u0026nbsp; \u0026nbsp; \u0026nbsp; Advanced Normalization Tools\u003c/p\u003e\n\u003cp\u003eICC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Intraclass correlation coefficient\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Confidence interval\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Huan Xie, Hanwei wang and Zhile Cao contributed the equally as first author, Xueqin Wang, Qisheng Ran and Shunan Wang contributed the equally as corresponding author. Guarantor of integrity of entire study, SW. Study concepts/study design or data acquisition of data analysis/interpretation, all authors. Manuscript drafting or manuscript revision for important intellectual content, HX. Manuscript final version approval, all authors. Agrees to ensure any questions related to the work are appropriately resolved, all authors. Literature research, HX. Clinical studies, HX, HW and XW. Statistical analysis, HX and ZC. Manuscript editing, HX, QR and SW. All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to express our gratitude to the Department of Radiology, Daping Hospital, Army Medical University for their support to this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGai, Q.Z., Li, X.L., Li, N. et al. Clinical significance of multi-slice spiral CT, MRI combined with gastric contrast-enhanced ultrasonography in the diagnosis of T staging of gastric cancer. Clin Transl Oncol 23, 2036\u0026ndash;2045 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGlobal Cancer Observatory, International Agency for Research on Cancer. Cancer Fact Sheets, Digestive Organs. Available on: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gco.iarc.fr/today/fact sheetscancers\u003c/span\u003e\u003cspan address=\"https://gco.iarc.fr/today/fact sheetscancers\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (Accessed on 8 February 2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL et al. (2021) Global cancer statis-tics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71:209\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJia Fu, Lei Tang, Zi-Yu Li et al.Diffusion kurtosis imaging in the prediction of poor responses of locally advanced gastric cancer to neoadjuvant chemotherap.EUR J of RADIOL.2020.128:108974.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThe global, regional, and national burden of stomach cancer in 195 countries, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease study 2017, Lancet Gastroenterol. Hepatol. 5 (2020) 42\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136(5):E359-86.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ed'Assignies G, Fina P, Bruno O, Vullierme MP, Tubach F, Paradis V, Sauvanet A, Ruszniewski P, Vilgrain V. High sensitivity of diffusion-weighted MR imaging for the detection of liver metastases from neuroendocrine tumors: comparison with T2-weighted and dynamic gadolinium-enhanced MR imaging. Radiology. 2013;268(2):390\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eH.A. Kareem, A. Abdel Rady, The utility of DW-MRI in the diagnosis of pancreatic focal lesions, Egypt. J. Radiol. Nucl. 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AJR Am J Roentgenol. 2014;203(1):85\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYanasak NE, Kelly MJ. MR imaging artifacts and parallel imaging techniques with calibration scanning: a new twist on old problems. Radiographics. 2014 Mar-Apr;34(2):532\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eObara M, Kwon J, Yoneyama M, Ueda Y, Cauteren MV. Technical Advancements in Abdominal Diffusion-weighted Imaging. Magn Reson Med Sci. 2023;22(2):191\u0026ndash;208.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJaermann T, Pruessmann KP, Valavanis A, Kollias S, Boesiger P. Influence of SENSE on image properties in high-resolution single-shot echo-planar DTI. Magn Reson Med. 2006;55(2):335\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Q, Xu WY, Sun NN, Feng QX, Zhu ZN, Hou YJ, Sang ZT, Li FY, Li BW, Xu H, Liu XS, Zhang YD. MRI versus Dual-Energy CT in Local-Regional Staging of Gastric Cancer. Radiology. 2024;312(1):e232387.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHou YJ, Sang ZT, Li Q, Feng QX, Wu J, Nickel MD, Hsu YC, Wang WZ, Wu CJ, Xu H, Liu XS. Advanced Multiparametric MRI Strategies for Tumor Restaging After Neoadjuvant Therapy in Locally Advanced Gastric Cancer. Ann Surg Oncol. 2025 Feb 3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaraca L, \u0026Ouml;zdemir ZM, Kahraman A, Yılmaz E, Akatlı A, Kural H. Endometrial carcinoma detection with 3.0 Tesla imaging: which sequence is more useful. Eur Rev Med Pharmacol Sci. 2022;26(21):8098\u0026ndash;8104.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang Q, Wang Y, Meng X, Li J, Shen Y, Hu X, Feng C, Li Z, Kamel I. Amide Proton Transfer-Weighted Imaging Combined with ZOOMit Diffusion Kurtosis Imaging in Predicting Lymph Node Metastasis of Cervical Cancer. Bioengineering (Basel). 2023;10(3):331.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu L, Wei L, Wang S, Fu C, Benker T, Zhao J. Better lesion conspicuity translates into improved prostate cancer detection: comparison of non-parallel-transmission-zoomed-DWI with conventional-DWI. Abdom Radiol (NY). 2021;46(12):5659\u0026ndash;5668.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlizadeh M, Poplawski MM, Fisher J, Gorniak RJT, Dresner A, Mohamed FB, Flanders AE. Zonally Magnified Oblique Multislice and Non-Zonally Magnified Oblique Multislice DWI of the Cervical Spinal Cord. AJNR Am J Neuroradiol. 2018;39(8):1555\u0026ndash;1561.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi J, Tian XN, Zhao BG, Wang N, Zhang YJ, Zhang L. Diagnostic value of cervical spine ZOOM-DWI in cervical spondylotic myelopathy. Eur Spine J. 2024;33(3):1223\u0026ndash;1229.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTian XN, Li SN, Zhao BG, Wang N, Gao T, Zhang L. The apparent diffusion coefficient based on small-field DWI is superior to T2-weighted imaging in evaluating neurological dysfunction of degenerative cervical myelopathy. Eur Spine J. 2024;33(10):3949\u0026ndash;3956.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu R, Jiang G, Gao P, Li G, Nie L, Yan J, Jiang M, Duan R, Zhao Y, Luo J, Yin Y, Li C. Non-invasive Amide Proton Transfer Imaging and ZOOM Diffusion-Weighted Imaging in Differentiating Benign and Malignant Thyroid Micronodules. Front Endocrinol (Lausanne). 2018;9:747.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStocker D, Manoliu A, Becker AS, Barth BK, Nanz D, Klarh\u0026ouml;fer M, Donati OF. Image Quality and Geometric Distortion of Modern Diffusion-Weighted Imaging Sequences in Magnetic Resonance Imaging of the Prostate. Invest Radiol. 2018;53(4):200\u0026ndash;206.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTamada T, Ream JM, Doshi AM, Taneja SS, Rosenkrantz AB. Reduced Field-of-View Diffusion-Weighted Magnetic Resonance Imaging of the Prostate at 3 Tesla: Comparison With Standard Echo-Planar Imaging Technique for Image Quality and Tumor Assessment. J Comput Assist Tomogr. 2017 Nov/Dec;41(6):949\u0026ndash;956.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStocker D, Manoliu A, Becker AS, Barth BK, Nanz D, Klarh\u0026ouml;fer M, Donati OF. Image Quality and Geometric Distortion of Modern Diffusion-Weighted Imaging Sequences in Magnetic Resonance Imaging of the Prostate. Invest Radiol. 2018;53(4):200\u0026ndash;206.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrendle C, Martirosian P, Schwenzer NF, Kaufmann S, Kruck S, Kramer U, Notohamiprodjo M, Nikolaou K, Schraml C. Diffusion-weighted imaging in the assessment of prostate cancer: Comparison of zoomed imaging and conventional technique. Eur J Radiol. 2016;85(5):893\u0026ndash;900.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim H, Lee JM, Yoon JH, Jang JY, Kim SW, Ryu JK, Kannengiesser S, Han JK, Choi BI. Reduced Field-of-View Diffusion-Weighted Magnetic Resonance Imaging of the Pancreas: Comparison with Conventional Single-Shot Echo-Planar Imaging. Korean J Radiol. 2015 Nov-Dec;16(6):1216\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSinger L, Wilmes LJ, Saritas EU, Shankaranarayanan A, Proctor E, Wisner DJ, Chang B, Joe BN, Nishimura DG, Hylton NM. High-resolution diffusion-weighted magnetic resonance imaging in patients with locally advanced breast cancer. Acad Radiol. 2012;19(5):526\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTakeuchi M, Matsuzaki K, Harada M. Evaluating Myometrial Invasion in Endometrial Cancer: Comparison of Reduced Field-of-view Diffusion-weighted Imaging and Dynamic Contrast-enhanced MR Imaging. Magn Reson Med Sci. 2018;17(1):28\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOta T, Hori M, Onishi H, Sakane M, Tsuboyama T, Tatsumi M, Nakamoto A, Kimura T, Narumi Y, Tomiyama N. Preoperative staging of endometrial cancer using reduced field-of-view diffusion-weighted imaging: a preliminary study. Eur Radiol. 2017;27(12):5225\u0026ndash;5235.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFeng Z, Min X, Sah VK, Li L, Cai J, Deng M, Wang L. Comparison of field-of-view (FOV) optimized and constrained undistorted single shot (FOCUS) with conventional DWI for the evaluation of prostate cancer. Clin Imaging. 2015 Sep-Oct;39(5):851\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"DWI, gastric tumors, imaging quality, ADC, entropy","lastPublishedDoi":"10.21203/rs.3.rs-7330659/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7330659/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo evaluate the clinical utility, image quality, apparent diffusion coefficient (ADC) value, and entropy value of diffusion weighted imaging (DWI) using echo planar imaging (EPI) with zonally oblique multi-slice (Zoom-DWI) of the gastric tumors. In addition, these values were compared with those obtained from general single-shot EPI with parallel imaging technique (General-DWI).\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e\u003cp\u003eThis retrospective study included 70 participants with histologically confirmed gastric tumors who underwent MRI. DWI acquisition was performed using free-breathing two-dimensional fat-suppressed General-DWI and Zoom-DWI. The image quality was qualitatively evaluated using a five-point Likert scale. Three reviewers evaluated the diagnostic performance regarding the structural conspicuity and boundary delineation of gastric tumors. Furthermore, quantitative analysis included measurement of the mean ADC and entropy values with the tumor regions. Qualitative, quantitative and diagnostic performance parameters were compared between General-DWI and Zoom-DWI using paired \u003cem\u003et\u003c/em\u003e test. The mean ADC and entropy values were correlation analysis between General-DWI and Zoom-DWI using the linear regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe mean image quality scores for imaging noise and the mean ADC values in gastric tumors were higher in General-DWI compared with Zoom-DWI (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). Conversely, the tumors\u0026rsquo; boundaries scores and sharpness scores were higher in Zoom-DWI compared with General-DWI (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.0001). However, no significant difference was observed in the mean entropy values of the gastric tumors between General-DWI and Zoom-DWI (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.788). Correlation analysis demonstrated an extremely strong correlation for both mean ADC and entropy values between the two techniques (R\u0026thinsp;=\u0026thinsp;0.906 and 0.776, respectively). Additionally, the performance of Zoom-DWI images in identifying structural conspicuity of gastric tumors (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05) and boundaries delineation (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05) surpassed that of General-DWI.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eZoom-DWI outperformed General-DWI in tumor diagnostic performance analysis and visualization despite higher ADC values in General-DWI, with strong inter-technique correlations validating Zoom-DWI's clinical utility for gastric tumors.\u003c/p\u003e","manuscriptTitle":"The Zoom diffusion-weighted imaging sequence is used in gastric tumors: clinical utility, image quality, ADC value, and entropy value evaluation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 13:46:27","doi":"10.21203/rs.3.rs-7330659/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3569f2d5-898a-4181-83ed-8b47be6b190c","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T20:08:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 13:46:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7330659","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7330659","identity":"rs-7330659","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}