Journal of Magnetic Resonance Imaging最新文献

{"title":"Right Heart Strain in Acute Myocardial Infarction: Toward an Integrated Atrioventricular Perspective.","authors":"Liya Dai, Chenying Lu","doi":"10.1002/jmri.70353","DOIUrl":"https://doi.org/10.1002/jmri.70353","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147838735","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":"Assessment of Robustness of MRI Radiomic Features in Four Abdominal Organs: Impact of Deep Learning Reconstruction and Segmentation.","authors":"Jingyu Zhong, Yue Xing, Yangfan Hu, Xianwei Liu, Shun Dai, Defang Ding, Junjie Lu, Jiarui Yang, Yue Li, Yang Song, Minda Lu, Dominik Nickel, Wenjie Lu, Huan Zhang, Weiwu Yao","doi":"10.1002/jmri.70342","DOIUrl":"https://doi.org/10.1002/jmri.70342","url":null,"abstract":"<p><strong>Background: </strong>The impact of deep learning (DL) reconstruction and segmentation on MRI radiomics stability has not been fully assessed.</p><p><strong>Purpose: </strong>To investigate the effects of acquisition, reconstruction, and segmentation on the reproducibility and variability of radiomic features in abdominal MRI.</p><p><strong>Study type: </strong>Prospective.</p><p><strong>Population: </strong>37 volunteers (22 men; mean age ± standard deviation, 37.4 ± 11.0 years).</p><p><strong>Field strength/sequence: </strong>3.0-T; axial turbo spin echo T2-weighted image, and fat-suppressed T2-weighted image using a half-Fourier acquisition single-shot turbo spin echo technique, each acquired four times with conventional or accelerated techniques, reconstructed with standard or DL algorithms.</p><p><strong>Assessment: </strong>Regions of interest were automatically generated by a DL neural network for liver, spleen, and right and left kidneys, followed by manual correction. We extracted 107 features using PyRadiomics after z-score normalization.</p><p><strong>Statistical tests: </strong>The reproducibility between acquisitions, reconstructions, and segmentations was evaluated using intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). The variability among the four scans was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). p < 0.05 was considered significant.</p><p><strong>Results: </strong>The mean ICC (0.518-0.608; 0.606-0.681) and CCC (0.515-0.603; 0.601-0.680) values were low for both manual and automatic segmentation regardless of image acquisition and reconstruction, using conventional acquisition with standard reconstruction as reference. The mean ICC (0.535-0.713) and CCC (0.531-0.714) values were low between manual and automatic segmentation, regardless of image acquisition and reconstruction. The median CV (10.0%-17.5%; 8.9%-15.5%) and QCD (5.3%-8.5%; 5.1%-8.3%) values were moderate but still adequate for both manual and automatic segmentation among different scans.</p><p><strong>Conclusion: </strong>Given the substantial impact of accelerated acquisition and DL reconstruction on the robustness of radiomics features in abdominal MRI, caution should be exercised when utilizing images with different acquisition and reconstruction techniques in radiomics analysis. The automatic segmentation cannot replace manual segmentation due to insufficient robustness of radiomics features.</p><p><strong>Evidence level: </strong>2.</p><p><strong>Technical efficacy: </strong>Stage 1.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147839099","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 \"Hypointensity on Carotid Plaque MRI and Its Relationship to Calcification: Histopathologic Validation With Quantitative Susceptibility Mapping\".","authors":"Zihan Ning, Xihai Zhao","doi":"10.1002/jmri.70362","DOIUrl":"https://doi.org/10.1002/jmri.70362","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147838699","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 \"Assessment of Robustness of MRI Radiomic Features in Four Abdominal Organs: Impact of Deep Learning Reconstruction and Segmentation\".","authors":"Grace McIlvain, Zhuoyu Shi","doi":"10.1002/jmri.70366","DOIUrl":"https://doi.org/10.1002/jmri.70366","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147838378","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":"Characterization of Pulmonary Functional Abnormalities in Systemic Sclerosis Using Xenon MRI.","authors":"Dawson Shaver, Steven Haworth, Evan Graumann, Bradie Frizzell, David H Lee, Jiwoong Choi, Chase S Hall, Scott M Matson, Mark Hamblin, Mario Castro, Paul S Schmidt, Peter J Niedbalski","doi":"10.1002/jmri.70363","DOIUrl":"https://doi.org/10.1002/jmri.70363","url":null,"abstract":"<p><strong>Background: </strong>Xenon MRI is increasingly used to evaluate patients with interstitial lung disease (ILD) and pulmonary hypertension (PH), both of which are common manifestations of systemic sclerosis (SSc). As such, Xe-MRI may be suited to interrogate lung function impairment in SSc.</p><p><strong>Purpose: </strong>To characterize xenon MRI signatures in SSc, toward evaluating the utility of xenon MRI as a method to elucidate mechanisms of regional lung function impairment in this population.</p><p><strong>Study type: </strong>Prospective.</p><p><strong>Population: </strong>Of 25 participants initially imaged, 21 participants (18 females) with SSc were included. Sixteen healthy volunteers (13 females) were enrolled.</p><p><strong>Fieldstrength/sequence: </strong>3 T, 1-point Dixon imaging using xenon MRI.</p><p><strong>Assessment: </strong>Xenon MRI measures including ventilation defect percent, membrane uptake, red blood cell (RBC) transfer, RBC defect percent, and RBC oscillation amplitude were generated. Measures were compared across healthy and SSc groups and correlated with standard clinical measures, including demographics, pulmonary function tests, CT lung density measures, and pulmonary artery pressure.</p><p><strong>Statistical tests: </strong>Due to a small number of male participants, statistical analysis was limited to female participants. Wilcoxon, t-tests, or Fisher's exact tests were used to compare between healthy and SSc groups. Pearson's correlation was used to correlate xenon MRI with clinical measures. p < 0.05 was considered significant.</p><p><strong>Results: </strong>Despite a relatively mild burden of pulmonary disease, SSc participants exhibited significantly lower RBC/Membrane ratio (0.25 [IQR, 0.10] vs. 0.34 [0.05]) and RBC transfer (0.22 ± 0.07 vs. 0.28 ± 0.07), and significantly greater RBC defect percent (22.8 [IQR, 16.9] vs. 11.9 [13.7]) compared to healthy volunteers.</p><p><strong>Data conclusion: </strong>Xenon MRI measures, including RBC transfer, RBC/Membrane, and RBC defect percent were markedly different in female SSc patients compared to age-matched healthy volunteers, suggesting that xenon MRI may be an effective method for examining regional impairments to pulmonary gas exchange in SSc.</p><p><strong>Evidence level: </strong>2.</p><p><strong>Technical efficacy: </strong>Stage 2.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147839054","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":"Automated Ultrashort Echo Time (UTE) MRI Low Signal Volume Analysis in Stable Pediatric Cystic Fibrosis and After Elexacaftor/Tezacaftor/Ivacaftor Therapy.","authors":"Brandon Zanette, Jared Bierbrier, Samal Munidasa, Daniel Genkin, Miranda Kirby, Felix Ratjen, Giles Santyr","doi":"10.1002/jmri.70333","DOIUrl":"https://doi.org/10.1002/jmri.70333","url":null,"abstract":"<p><strong>Background: </strong>Ultrashort echo time (UTE) MRI overcomes the low signal intensity and short T₂* of pulmonary tissues, improving image quality. Abnormally low UTE signal intensities are associated with hallmarks of cystic fibrosis (CF), including gas trapping and lung hyperexpansion.</p><p><strong>Purpose: </strong>To explore short-term repeatability and sensitivity to treatment of low signal volume (LSV) from UTE MRI in pediatric CF.</p><p><strong>Study-type: </strong>Single-site, retrospective, longitudinal.</p><p><strong>Subjects: </strong>Thirteen participants with stable CF (6M/7F, median age = 15 years old) were scanned at baseline and 1-month to evaluate short-term repeatability. Subsequently, 14 CF participants (7M/7F median age = 16 years old) were scanned pre- and 1-month post-initiation of elexacaftor/tezacaftor/ivacaftor (ETI).</p><p><strong>Field strength/sequence: </strong>Three-dimensional stack-of-spirals for UTE, 2-dimensional gradient-echo for hyperpolarized xenon (Xe-MRI), 3-dimensional gradient-echo for thoracic cavity estimation at 3 T.</p><p><strong>Assessment: </strong>LSV was analyzed from UTE MRI. Same-day spirometry, multiple-breath washout, and Xe-MRI were also performed to compare to LSV.</p><p><strong>Statistical tests: </strong>Differences were assessed with the Wilcoxon matched-pairs signed-rank test. Bland-Altman analysis and the Intraclass Correlation Coefficient (ICC) were used to assess 1-month repeatability in stable CF. Relationships between measures were assessed with Spearman correlation. p < 0.05 was considered significant.</p><p><strong>Results: </strong>Baseline LSV was correlated with forced expiratory volume in 1 s (FEV₁), FEV₁ to forced vital capacity ratio (FEV₁/FVC), lung clearance index (LCI), and ventilation defect percent (VDP) (all |ρ| ≥ 0.50). LSV was not significantly different after 1-month (20.1 [10.9-25.5]% vs. 21.7 [12.7-28.2]%, p = 0.4548) in stable pediatric CF with Bland-Altman bias < 1% and ICC = 0.93. LSV was significantly reduced from 18.8 [12.0-30.3]% to 16.4 [7.8-19.6]% after 1-month of ETI and correlated with absolute differences in FEV₁, FEV₁/FVC, LCI, and VDP (all |ρ| ≥ 0.59).</p><p><strong>Data conclusion: </strong>LSV analysis was feasible and repeatable in pediatric CF over a 1-month period. LSV was significantly reduced 1-month after ETI treatment, indicating sensitivity to reduced gas trapping, hyperexpansion, and obstruction.</p><p><strong>Evidence level: </strong>4.</p><p><strong>Technical efficacy: </strong>1.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147816393","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":"AI-Powered Gradient Echo Plural Contrast Imaging (AI-GEPCI)-A Comprehensive Neurological Protocol From a Single MRI Scan.","authors":"Jeramy Lewis, Manu S Goyal, Gregory F Wu, Yuyang Hu, Alexander L Sukstanskii, Satya V V N Kothapalli, Anne H Cross, Ulugbek Kamilov, Dmitriy A Yablonskiy","doi":"10.1002/jmri.70345","DOIUrl":"10.1002/jmri.70345","url":null,"abstract":"<p><strong>Background: </strong>MRI is essential for diagnosing and monitoring neurological diseases. Conventional protocols require multiple sequences to obtain complementary contrasts, increasing scan time, cost, and tolerability. Generating multiple contrasts from a single acquisition may streamline workflow while maintaining clinical utility.</p><p><strong>Purpose: </strong>To train attention-based convolutional neural networks (ACNNs) to generate clinical-quality Fluid-Attenuated-Inversion-Recovery (FLAIR), Magnetization-Prepared-Rapid-Gradient-Echo (MPRAGE), R2* maps, and derived contrasts from a single Gradient Echo Plural Contrast Imaging (GEPCI) acquisition.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Population: </strong>43 MRI scans from individuals with multiple sclerosis (25/18 F/M, 49 ± 11 years-of-age).</p><p><strong>Field strength/sequence: </strong>3 T MRI, 3D GEPCI, MPRAGE, and FLAIR.</p><p><strong>Assessment: </strong>Technical quality of AI-generated contrasts was evaluated against directly acquired MRI using structural similarity index (SSIM). Clinical image quality was assessed by physicians. Lesion volumes and counts were obtained using automated segmentation.</p><p><strong>Statistical tests: </strong>One-sample one-sided Wilcoxon signed-rank test was used to establish the clinical quality of images. Agreement between native- and AI-derived lesion volume and lesion count measurements was assessed using intraclass correlation coefficients (ICC). Quantitative accuracy for R2* maps was evaluated using normalized root-mean-square error (NRMSE).</p><p><strong>Results: </strong>AI-generated FLAIR and MPRAGE achieved mean SSIM values of 0.923 ± 0.028 and 0.935 ± 0.022, respectively. Generated R2* maps achieved a mean SSIM of 0.996 ± 0.006 and NRMSE of 0.031 ± 0.020. Physicians-assigned mean clinical quality ratings of 4.2 for GEPCI-FLAIR and 4.5 for GEPCI-MPRAGE exceeded the 4.0 clinical standard on a 1-to-5 scale. Lesion volume and count comparisons from automated segmentation showed strong agreement between AI-generated and ground-truth measurements: R² = 0.988 and R² = 0.933, ICC = 0.988 and ICC = 0.967, respectively.</p><p><strong>Data conclusion: </strong>AI-GEPCI generated multiple clinically relevant MRI contrasts from a single GEPCI acquisition with high similarity to corresponding acquired images, supporting high-quality, intrinsically co-registered multi-contrast brain evaluation.</p><p><strong>Evidence level: </strong>2.</p><p><strong>Technical efficacy: </strong>Stage 1.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147816372","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":"Comparison Between Respiratory-Triggered and Free-Breathing Signal Acquisition Methods for Single-Voxel Phosphorus Magnetic Resonance Spectroscopy of the Liver on a Clinical System.","authors":"Kumi Ozaki, Jihun Kwon, Marc Van Cauteren, Yasutomo Katsumata, Yukichi Tanahashi, Satoshi Goshima","doi":"10.1002/jmri.70352","DOIUrl":"https://doi.org/10.1002/jmri.70352","url":null,"abstract":"<p><strong>Background: </strong>The optimal protocol for clinical liver ³¹P-magnetic resonance spectroscopy (MRS) remains unclear. Single-voxel ³¹P-MRS using image-selected in vivo spectroscopy (ISIS) employs respiratory-triggering (RT) or free-breathing (FB) acquisition. RT provides robust data but prolongs scan duration; FB allows faster acquisition but may suffer from low signal-to-noise ratio (SNR). Yet, direct comparison using a regulatory-approved coil in a clinical setting has not been reported.</p><p><strong>Purpose: </strong>To compare ³¹P-MRS data stability and robustness between RT and FB.</p><p><strong>Study type: </strong>Prospective.</p><p><strong>Population: </strong>24 volunteers (19 male/5 female).</p><p><strong>Field strength/sequence: </strong>3 T MRI; single-voxel ³¹P-MRS using ISIS (free induction decay-based) with approved ³¹P coil.</p><p><strong>Methods: </strong>³¹P-MRS was performed using RT and FB techniques (128 and 192 signal averages, respectively; expected scan duration ~13 min each). Spectra were analyzed using jMRUI. SNR and peak areas for PME, Pi, PDE, α-ATP, and NADPH, normalized using γ-ATP, were compared. PME/PDE and NADPH/(PME + PDE) ratios were also compared.</p><p><strong>Statistical test: </strong>Paired t-tests and Bland-Altman analysis were used. A p value < 0.05 was considered significant.</p><p><strong>Results: </strong>Scan duration was significantly longer for RT (15 min 44 s) than FB (12 min 56 s). No significant differences were observed for SNR (p = 0.570), NADPH/(PME + PDE) (p = 0.931), PME/γ-ATP (p = 0.556), Pi/γ-ATP (p = 0.931), α-ATP/γ-ATP (p = 0.332), or NADPH/γ-ATP (p = 0.394). Significant differences were noted for PDE/γ-ATP (RT 1.68 vs. FB 1.35, p = 0.003) and PME/PDE (RT 0.434 vs. FB 0.489, p = 0.046). Bland-Altman analysis showed near-zero fixed biases and no proportional bias, with limits of agreement from -0.53 to 0.62 (PME), -0.30 to 0.30 (Pi), -0.41 to 1.07 (PDE), -0.45 to 0.43 (α-ATP), and -0.80 to 0.90 (NADPH).</p><p><strong>Data conclusion: </strong>³¹P-MRS of the liver showed equivalent stability and robustness for RT and FB. FB yielded comparable data within a shorter, predictable scan duration.</p><p><strong>Evidence level: </strong>2.</p><p><strong>Technical efficacy: </strong>1.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147816326","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":"Analysis of Upper Airway Morphology Using Four-Dimensional Dynamic MRI With Active Deep Learning-Based Automatic Segmentation.","authors":"Cheng-Yang Yu, Meng-Chen Chung, Yunn-Jy Chen, Han-Wei Wang, Jonathan X Zhou, Shih-Lung Chen, Kevin T Chen, Tiffany Ting-Fang Shih","doi":"10.1002/jmri.70237","DOIUrl":"10.1002/jmri.70237","url":null,"abstract":"<p><strong>Background: </strong>Upper-airway morphology changes during breathing can be captured with cine 4D MRI. Active-learning nnU-Net reduces manual labeling while maintaining accuracy.</p><p><strong>Purpose: </strong>For automatic upper airway segmentation on free-breathing cine 4D MRI using active learning and quantifying dynamic changes under two mouth positions.</p><p><strong>Study type: </strong>Prospective cross-sectional study.</p><p><strong>Population: </strong>Eighty-four OSA (obstructive sleep apnea)-free adults (28 M/56F; 18-80 years; 33 with sleep-related breathing symptoms). Segmentation performance was evaluated on an internal test set (n = 18).</p><p><strong>Fieldstrength/sequence: </strong>3T, free-breathing time-resolved imaging with interleaved stochastic trajectories (TWIST) sequence under closed- and open-mouth positions.</p><p><strong>Assessment: </strong>Manual annotations by a technologist (radiologist-verified) served as reference standard and training labels for an active-learning nnU-Net (68 training; four fixed validation). Total airway length, cross-sectional area (CSA), and total airway volume were computed at each anatomical level and compared across mouth positions, sex, and sleep-related symptom status, and independent predictors were identified.</p><p><strong>Statistical tests: </strong>Paired/unpaired t or Mann-Whitney U test (two-sided p = 0.05). Predictor selection by 10-fold LASSO; effects estimated via ordinary least squares with cluster-robust standard errors.</p><p><strong>Results: </strong>Segmentation achieved a dice 0.959 ± 0.019 (test set). Open-mouth breathing significantly lengthened the total airway (7.92 ± 1.07 vs. 7.41 ± 0.93 cm) and reduced retropalatal CSA (1.51 ± 0.68 vs. 1.80 ± 0.69 cm²). Coefficients of variation (CVs) for CSA and volume were significantly higher with 20-s open-mouth breathing. Males (n = 28) exhibited significantly larger airway volumes than females (closed 27.94 ± 4.87 vs. 19.82 ± 3.26 cm³; open 30.26 ± 5.94 vs. 20.94 ± 3.85 cm³). Symptomatic individuals (n = 33) had significantly longer airways (closed 7.96 ± 0.96 vs. 7.04 ± 0.70 cm; open 8.54 ± 1.01 vs. 7.52 ± 0.91 cm), narrower open-mouth retropalatal CSA (1.24 ± 0.51 vs. 1.68 ± 0.72 cm²), and greater retropalatal CSA dynamic variability. Multivariable regression confirmed mouth position, symptoms, and sex as independent predictors.</p><p><strong>Data conclusion: </strong>Four-dimensional cine MRI with active-learning nnU-Net can automatically quantify dynamic upper airway morphology, demonstrating systematic differences and dynamic variability.</p><p><strong>Evidence level: </strong>2.</p><p><strong>Technical efficacy: </strong>Stage 2.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":"63 5","pages":"1404-1417"},"PeriodicalIF":3.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13066510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147645559","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":"Editorial for \"Multidisciplinary Protocol for 1.5T MRI in Adult Patients With Active Implantable Medical Devices: Safety and Efficacy in a Five-Year Single-Center Experience\".","authors":"Jianfeng Zheng","doi":"10.1002/jmri.70284","DOIUrl":"10.1002/jmri.70284","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"1402-1403"},"PeriodicalIF":3.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147365449","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}