Journal of Magnetic Resonance Imaging最新文献

{"title":"Associations of Postencephalitic Epilepsy Using Multi-Contrast Whole Brain MRI: A Large Self-Supervised Vision Foundation Model Strategy.","authors":"Ronghui Gao, Anjiao Peng, Yifei Duan, Mengyao Chen, Tao Zheng, Meng Zhang, Lei Chen, Huaiqiang Sun","doi":"10.1002/jmri.29734","DOIUrl":"https://doi.org/10.1002/jmri.29734","url":null,"abstract":"<p><strong>Background: </strong>Postencephalitic epilepsy (PEE) is a severe neurological complication following encephalitis. Early identification of individuals at high risk for PEE is important for timely intervention.</p><p><strong>Purpose: </strong>To develop a large self-supervised vision foundation model using a big dataset of multi-contrast head MRI scans, followed by fine-tuning with MRI data and follow-up outcomes from patients with PEE to develop a PEE association model.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Population: </strong>Fifty-seven thousand six hundred twenty-one contrast-enhanced head MRI scans from 34,871 patients for foundation model construction, and head MRI scans from 144 patients with encephalitis (64 PEE, 80 N-PEE) for the PEE association model.</p><p><strong>Field strength/sequence: </strong>1.5-T, 3-T, T1-weighted imaging, T2-weighted imaging, fluid attenuated inversion recovery, T1-weighted contrast-enhanced imaging.</p><p><strong>Assessment: </strong>The foundation model was developed using self-supervised learning and cross-contrast context recovery. Patients with encephalitis were monitored for a median of 3.7 years (range 0.7-7.5 years), with epilepsy diagnosed according to International League Against Epilepsy. Occlusion sensitivity mapping highlighted brain regions involved in PEE classifications. Model performance was compared with DenseNet without pre-trained weights.</p><p><strong>Statistical tests: </strong>Performance was assessed via confusion matrices, accuracy, sensitivity, specificity, precision, F1 score, and area under the receiver operating characteristic curve (AUC). The DeLong test evaluated AUC between the two models (P < 0.05 for statistical significance).</p><p><strong>Results: </strong>The PEE association model achieved accuracy, sensitivity, specificity, precision, F1 score, and AUC of 79.3% (95% CI: 0.71-0.92), 92.3% (95% CI: 0.80-1.00), 68.8% (95% CI: 0.55-0.87), 70.6% (95% CI: 0.61-0.90), 80.0% (95% CI: 0.71-0.93), and 81.0% (95% CI: 0.68-0.92), respectively. A significant AUC improvement was found compared to DenseNet (Delong test, P = 0.03). The association model focused on brain regions affected by encephalitis.</p><p><strong>Data conclusion: </strong>Using extensive unlabeled data via self-supervised learning addressed the limitations of supervised tasks with limited data. The fine-tuned foundation model outperformed DenseNet, which was trained exclusively on task data.</p><p><strong>Plain language summary: </strong>This research develops a model to assess the occurrence epilepsy after encephalitis, a severe brain inflammation condition. By using over 57,000 brain scans, the study trains a computer program to recognize patterns in brain images. The model analyzes whole-brain scans to identify areas commonly affected by the disease, such as the temporal and frontal lobes. It was tested on data from patients with encephalitis and showed better performance than o","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143080140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of Multiband Technique on Temporal Diffusion Spectroscopy and Its Diagnostic Value in Breast Tumors.","authors":"Jie Ding, Zhen Zhang, Hongyan Xiao, Lijia Zhi, Xiuzheng Yue, Dazhi Chen, Rongrong Zhu, Lili Yang, Chao You, Yajia Gu","doi":"10.1002/jmri.29715","DOIUrl":"https://doi.org/10.1002/jmri.29715","url":null,"abstract":"<p><strong>Background: </strong>Temporal diffusion spectroscopy (TDS) is a noninvasive diffusion imaging technique used to characterizing cellular microstructures. The influence of multiband (MB) on TDS, particularly in breast tumor imaging remain unknown.</p><p><strong>Purpose: </strong>To investigate the influence of MB on TDS in terms of scanning time, image quality, and quantitative parameters and to assess the diagnostic value of TDS with MB in breast tumors.</p><p><strong>Study type: </strong>Prospective.</p><p><strong>Population: </strong>Seventy-one women with 71 confirmed lesions.</p><p><strong>Field strength/sequence: </strong>3.0 T; oscillating gradient spin-echo (OGSE), OGSE with MB, and pulsed gradient spin-echo, and routine magnetic resonance imaging squences.</p><p><strong>Assessment: </strong>TDS with MB was used to assess diagnostic efficacy in differentiating benign and malignant tumors. A comparison of scanning time and image quality was performed in 17 patients. Imaging parameters were analyzed using limited spectrally edited diffusion (IMPULSED) and apparent diffusion coefficient (ADC) values were compared between MB and non-MB protocols. The cell diameter from TDS was compared with histopathological measurements in 21 patients.</p><p><strong>Statistical tests: </strong>Bland-Altman plot, paired t test, Mann-Whitney U test, kappa test, DeLong's test, intraclass correlation coefficient agreement, receiver operating characteristic curve, area under the curve (AUC), and simple linear regression, with statistical significance set at P < 0.05.</p><p><strong>Results: </strong>The TDS with MB protocol had a shorter average scanning time than that without MB protocol (7 minutes 22 seconds vs. 12 minutes 28 seconds); image quality was improved by reducing image artifacts. Most IMPULSED parameters and ADC values did not significantly differ between the MB and non-MB protocols (P = 0.23, P = 0.17). The IMPULSED parameters of cellularity and intracellular volume fraction achieved the highest AUC values for distinguishing breast tumors (0.865 and 0.821, respectively), surpassing the diagnostic efficiency of conventional ADC-1000 (0.776). The correlation between IMPULSED parameters and microscopic cell size was strong (r = 0.842).</p><p><strong>Data conclusion: </strong>The MB technique improved the TDS protocol's efficiency and reduced the image artifacts. TDS parameters correlated with pathological findings and showed good performance in differentiating benign from malignant breast tumors.</p><p><strong>Plain language summary: </strong>We explored the impact of simultaneous multislice acquisition technology on temporal diffusion spectroscopy (TDS) and whether combining this method could help distinguish benign from malignant breast tumors. Our findings showed that simultaneous multislice acquisition technology shortened the scanning time and improved image quality by reducing motion-related issues. Additionally, measurements of cell size","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143074596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial for \"Identifying Primary Sites of Spinal Metastases: Expert-Derived Features vs. ResNet50 Model Using Non-Enhanced MRI\".","authors":"Grace McIlvain, Zhuoyu Shi","doi":"10.1002/jmri.29723","DOIUrl":"https://doi.org/10.1002/jmri.29723","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143066038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of Static and Low-Frequency Magnetic Fields on Gene Expression.","authors":"Vitalii Zablotskii, Oksana Gorobets, Svitlana Gorobets, Tatyana Polyakova","doi":"10.1002/jmri.29726","DOIUrl":"https://doi.org/10.1002/jmri.29726","url":null,"abstract":"<p><p>Substantial research over the past two decades has established that magnetic fields affect fundamental cellular processes, including gene expression. However, since biological cells and subcellular components exhibit diamagnetic behavior and are therefore subjected to very small magnetic forces that cannot directly compete with the viscoelastic and bioelectric intracellular forces responsible for cellular machinery functions, it becomes challenging to understand cell-magnetic field interactions and to reveal the mechanisms through which these interactions differentially influence gene expression in cells. The limited understanding of the molecular mechanisms underlying biomagnetic effects has hindered progress in developing effective therapeutic applications of magnetic fields. This review examines the expanding body of literature on genetic events during static and low-frequency magnetic field exposure, focusing particularly on how changes in gene expression interact with cellular machinery. To address this, we conducted a systematic review utilizing extensive search strategies across multiple databases. We explore the intracellular mechanisms through which transcription functions may be modified by a magnetic field in contexts where other cellular signaling pathways are also activated by the field. This review summarizes key findings in the field, outlines the connections between magnetic fields and gene expression changes, identifies critical gaps in current knowledge, and proposes directions for future research. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 4.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143066042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Anti-Motion Ultra-Fast Quantitative MRI in Neurological Disorder Imaging: Insights From Huntington's Disease","authors":"Fei Wu MD,&nbsp;Haiyang Luo MD, PhD,&nbsp;Xiao Wang MD, PhD,&nbsp;Qinqin Yang PhD,&nbsp;Yuchuan Zhuang PhD,&nbsp;Liangjie Lin PhD,&nbsp;Yanbo Dong PhD,&nbsp;Andrey Tulupov MD, PhD,&nbsp;Yong Zhang MD, PhD,&nbsp;Shuhui Cai PhD,&nbsp;Zhong Chen PhD,&nbsp;Congbo Cai PhD,&nbsp;Jianfeng Bao PhD,&nbsp;Jingliang Cheng MD, PhD","doi":"10.1002/jmri.29682","DOIUrl":"10.1002/jmri.29682","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Conventional quantitative MRI (qMRI) scan is time-consuming and highly sensitive to movements, posing great challenges for quantitative images of individuals with involuntary movements, such as Huntington's disease (HD).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To evaluate the potential of our developed ultra-fast qMRI technique, multiple overlapping-echo detachment (MOLED), in overcoming involuntary head motion and its capacity to quantitatively assess tissue changes in HD.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Study Type&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Prospective.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Phantom/Subjects&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;A phantom comprising 13 tubes of MnCl&lt;sub&gt;2&lt;/sub&gt; at varying concentrations, 5 healthy volunteers (male/female: 1/4), 22 HD patients (male/female: 14/8) and 27 healthy controls (male/female: 15/12).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Field Strength/Sequence&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;3.0 T. MOLED-T2 sequence, MOLED-T2* sequence, T2-weighted spin-echo sequence, T1-weighted gradient echo sequence, and T2-dark-fluid sequence.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Assessment&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;T1-weighted images were reconstructed into high-resolution images, followed by segmentation to delineate regions of interest (ROIs). Subsequently, the MOLED T2 and T2* maps were aligned with the high-resolution images, and the ROIs were transformed into the MOLED image space using the transformation matrix and warp field. Finally, T2 and T2* values were extracted from the MOLED relaxation maps.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Statistical Tests&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Bland–Altman analysis, independent &lt;i&gt;t&lt;/i&gt; test, Mann–Whitney &lt;i&gt;U&lt;/i&gt; test, Pearson correlation analysis, and Spearman correlation analysis, &lt;i&gt;P&lt;/i&gt; &lt; 0.05 was considered statistically significant.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;MOLED-T2 and MOLED-T2* sequences demonstrated good accuracy (Meandiff = − 0.20%, SDdiff = 1.05%, and Meandiff = −1.73%, SDdiff = 10.98%, respectively), and good repeatability (average intraclass correlation coefficient: 0.856 and 0.853, respectively). More important, MOLED T2 and T2* maps remained artifact-free across all HD patients, even in the presence of apparent head motions. Moreover, there were significant differences in T2 and T2* values across multiple ROIs between HD and controls.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 ","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":"61 6","pages":"2455-2468"},"PeriodicalIF":3.3,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143066033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial for “Combining Multifrequency Magnetic Resonance Elastography With Automatic Segmentation to Assess Renal Function in Patients With Chronic Kidney Disease”","authors":"Ilkay S. Idilman,&nbsp;Musturay Karcaaltincaba","doi":"10.1002/jmri.29721","DOIUrl":"10.1002/jmri.29721","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":"61 6","pages":"2556-2557"},"PeriodicalIF":3.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143052736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combining Multifrequency Magnetic Resonance Elastography With Automatic Segmentation to Assess Renal Function in Patients With Chronic Kidney Disease","authors":"Qiumei Liang MS,&nbsp;Haiwei Lin MS,&nbsp;Junfeng Li MS,&nbsp;Peiyin Luo MS,&nbsp;Ruirui Qi MS,&nbsp;Qiuyi Chen MS,&nbsp;Fanqi Meng BS,&nbsp;Haodong Qin MS,&nbsp;Feifei Qu PhD,&nbsp;Youjia Zeng MD,&nbsp;Wenjing Wang MD,&nbsp;Jiandong Lu MD,&nbsp;Bingsheng Huang PhD,&nbsp;Yueyao Chen MD","doi":"10.1002/jmri.29719","DOIUrl":"10.1002/jmri.29719","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Multifrequency MR elastography (mMRE) enables noninvasive quantification of renal stiffness in patients with chronic kidney disease (CKD). Manual segmentation of the kidneys on mMRE is time-consuming and prone to increased interobserver variability.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To evaluate the performance of mMRE combined with automatic segmentation in assessing CKD severity.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Study Type&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Prospective.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Participants&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;A total of 179 participants consisting of 95 healthy volunteers and 84 participants with CKD.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Field Strength/Sequence&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;3 T, single shot spin echo planar imaging sequence.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Assessment&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Participants were randomly assigned into training (&lt;i&gt;n&lt;/i&gt; = 58), validation (&lt;i&gt;n&lt;/i&gt; = 15), and test (&lt;i&gt;n&lt;/i&gt; = 106) sets. Test set included 47 healthy volunteers and 58 CKD participants with different stages (21 stage 1–2, 22 stage 3, and 16 stage 4–5) based on estimated glomerular filtration rate (eGFR). Shear wave speed (SWS) values from mMRE was measured using automatic segmentation constructed through the nnU-Net deep-learning network. Standard manual segmentation was created by a radiologist. In the test set, the automatically segmented renal SWS were compared between healthy volunteers and CKD subgroups, with age as a covariate. The association between SWS and eGFR was investigated in participants with CKD.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Statistical Tests&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Dice similarity coefficient (DSC), analysis of covariance, Pearson and Spearman correlation analyses. &lt;i&gt;P&lt;/i&gt; &lt; 0.05 was considered statistically significant.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Mean DSCs between standard manual and automatic segmentation were 0.943, 0.901, and 0.970 for the renal cortex, medulla, and parenchyma, respectively. The automatically quantified cortical, medullary, and parenchymal SWS were significantly correlated with eGFR (&lt;i&gt;r&lt;/i&gt; = 0.620, 0.605, and 0.640, respectively). Participants with CKD stage 1–2 exhibited significantly lower cortical SWS values compa","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":"61 6","pages":"2543-2555"},"PeriodicalIF":3.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmri.29719","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143052734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the Potential of Quantitative Susceptibility Mapping for Detecting Iron Deposition of Renal Fibrosis in a Rabbit Model","authors":"Tingting Zha MD,&nbsp;Zhiping Zhang MD,&nbsp;Liang Pan MD,&nbsp;Lei Peng MS,&nbsp;Yanan Du MS,&nbsp;Peng Wu MD,&nbsp;Jie Chen MD,&nbsp;Wei Xing MD","doi":"10.1002/jmri.29722","DOIUrl":"10.1002/jmri.29722","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;As ferroptosis is a key factor in renal fibrosis (RF), iron deposition monitoring may help evaluating RF. The capability of quantitative susceptibility mapping (QSM) for detecting iron deposition in RF remains uncertain.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To investigate the potential of QSM to detect iron deposition in RF.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Study Type&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Animal model.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Animal Model&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Eighty New Zealand rabbits were randomly divided into control (N = 10) and RF (N = 70) groups, consisting of baseline, 7, 14, 21, and 28 days (N = 12 in each), and longitudinal (N = 10) subgroups. RF was induced via unilateral renal arteria stenosis.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Field Strength/Sequence&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;3 T, QSM with gradient echo, arterial spin labeling with gradient spin echo.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Assessment&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Bilateral kidney QSM values (&lt;i&gt;χ&lt;/i&gt;) in the cortex (&lt;i&gt;χ&lt;/i&gt;&lt;sub&gt;CO&lt;/sub&gt;) and outer medulla (&lt;i&gt;χ&lt;/i&gt;&lt;sub&gt;OM&lt;/sub&gt;) were evaluated with histopathology.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Statistical Tests&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Analysis of variance, Kruskal–Wallis, Spearman's correlation, and the area under the receiver operating characteristic curve (AUC). &lt;i&gt;P&lt;/i&gt; &lt; 0.05 was significant.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;In fibrotic kidneys, &lt;i&gt;χ&lt;/i&gt;&lt;sub&gt;CO&lt;/sub&gt; decreased at 7 days ([−6.69 ± 0.98] × 10&lt;sup&gt;−2&lt;/sup&gt; ppm) and increased during 14–28 days ([−1.85 ± 2.11], [0.14 ± 0.58], and [1.99 ± 0.60] × 10&lt;sup&gt;−2&lt;/sup&gt; ppm, respectively), while the &lt;i&gt;χ&lt;/i&gt;&lt;sub&gt;OM&lt;/sub&gt; had the opposite trend. Both significantly correlated with histopathology (|&lt;i&gt;r&lt;/i&gt;| = 0.674–0.849). AUC of QSM for distinguishing RF degrees was 0.692–0.993. In contralateral kidneys, the &lt;i&gt;χ&lt;/i&gt;&lt;sub&gt;CO&lt;/sub&gt; initially decreased ([−6.67 ± 0.84] × 10&lt;sup&gt;−2&lt;/sup&gt; ppm) then recovered to baseline ([−4.81 ± 0.89] × 10&lt;sup&gt;−2&lt;/sup&gt; ppm), while the &lt;i&gt;χ&lt;/i&gt;&lt;sub&gt;OM&lt;/sub&gt; at 7–28 days ([2.58 ± 1.40], [2.25 ± 1.83], [2.49 ± 2.11], [2.43 ± 1.32] × 10&lt;sup&gt;−2&lt;/sup&gt; ppm, respectively) were significantly higher than baseline ([0.54 ± 0.18] × 10&lt;sup&gt;−2&lt;/sup&gt; ppm).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Data Conclusion&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Different iron deposition patterns were observed in RF with QS","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":"61 6","pages":"2558-2569"},"PeriodicalIF":3.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143059366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial for \"Left Ventricular Hemodynamic Forces Changes in Fabry Disease: A Cardiac Magnetic Resonance Study\".","authors":"Scott D Flamm","doi":"10.1002/jmri.29724","DOIUrl":"https://doi.org/10.1002/jmri.29724","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143052661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Primary Sites of Spinal Metastases: Expert-Derived Features vs. ResNet50 Model Using Nonenhanced MRI.","authors":"Ke Liu, Jinlai Ning, Siyuan Qin, Jun Xu, Dapeng Hao, Ning Lang","doi":"10.1002/jmri.29720","DOIUrl":"https://doi.org/10.1002/jmri.29720","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The spinal column is a frequent site for metastases, affecting over 30% of solid tumor patients. Identifying the primary tumor is essential for guiding clinical decisions but often requires resource-intensive diagnostics.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To develop and validate artificial intelligence (AI) models using noncontrast MRI to identify primary sites of spinal metastases, aiming to enhance diagnostic efficiency.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Study type: &lt;/strong&gt;Retrospective.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Population: &lt;/strong&gt;A total of 514 patients with pathologically confirmed spinal metastases (mean age, 59.3 ± 11.2 years; 294 males) were included, split into a development set (360) and a test set (154).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Field strength/sequence: &lt;/strong&gt;Noncontrast sagittal MRI sequences (T1-weighted, T2-weighted, and fat-suppressed T2) were acquired using 1.5 T and 3 T scanners.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Assessment: &lt;/strong&gt;Two models were evaluated for identifying primary sites of spinal metastases: the expert-derived features (EDF) model using radiologist-identified imaging features and a ResNet50-based deep learning (DL) model trained on noncontrast MRI. Performance was assessed using accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve (ROC-AUC) for top-1, top-2, and top-3 indicators.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Statistical tests: &lt;/strong&gt;Statistical analyses included Shapiro-Wilk, t tests, Mann-Whitney U test, and chi-squared tests. ROC-AUCs were compared via DeLong tests, with 95% confidence intervals from 1000 bootstrap replications and significance at P &lt; 0.05.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The EDF model outperformed the DL model in top-3 accuracy (0.88 vs. 0.69) and AUC (0.80 vs. 0.71). Subgroup analysis showed superior EDF performance for common sites like lung and kidney (e.g., kidney F1: 0.94 vs. 0.76), while the DL model had higher recall for rare sites like thyroid (0.80 vs. 0.20). SHapley Additive exPlanations (SHAP) analysis identified sex (SHAP: -0.57 to 0.68), age (-0.48 to 0.98), T1WI signal intensity (-0.29 to 0.72), and pathological fractures (-0.76 to 0.25) as key features.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Data conclusion: &lt;/strong&gt;AI techniques using noncontrast MRI improve diagnostic efficiency for spinal metastases. The EDF model outperformed the DL model, showing greater clinical potential.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Plain language summary: &lt;/strong&gt;Spinal metastases, or cancer spreading to the spine, are common in patients with advanced cancer, often requiring extensive tests to determine the original tumor site. Our study explored whether artificial intelligence could make this process faster and more accurate using noncontrast MRI scans. We tested two methods: one based on radiologists' expertise in identifying imaging features and another using a deep learning model trained to analyze MRI images. The expert-based method was more reliable, correctly identifying the tumor site in 88% of cases when considering th","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143046937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}