Journal of Magnetic Resonance Imaging最新文献

{"title":"Editorial for \"Association of Myelin Disruption and Iron Accumulation on MRI With Parkinson's Disease Severity\".","authors":"Alexandra G Roberts, Mert Şişman, Alexey V Dimov","doi":"10.1002/jmri.70002","DOIUrl":"https://doi.org/10.1002/jmri.70002","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144248272","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":"Broad Consent in Healthcare Research: What Is Efficient, What Is Right?","authors":"Jitka Starekova, Mark E Schweitzer","doi":"10.1002/jmri.70000","DOIUrl":"https://doi.org/10.1002/jmri.70000","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144248271","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":"Correction to \"Pediatric Z-Score Calculator of Cardiac MRI Volumetric Measurements\".","authors":"","doi":"10.1002/jmri.29828","DOIUrl":"https://doi.org/10.1002/jmri.29828","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144234353","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":"The Effect of Elevated Blood Pressure on Rich-Club Organization: A Multicenter MR Diffusion Tensor Imaging Study From Prehypertension to Hypertension.","authors":"Yang Li, Hu Xu, Ranchao Wang, Yu Shen, Yu Yang, Yue Yu, Xingbing Chen, Hui Su","doi":"10.1002/jmri.29835","DOIUrl":"https://doi.org/10.1002/jmri.29835","url":null,"abstract":"<p><strong>Background: </strong>Hypertension-induced alterations in brain network topology remain poorly understood, and diffusion tensor imaging (DTI) offers a promising approach for detecting early structural changes.</p><p><strong>Hypothesis: </strong>Rich-club organization undergoes progressive disruption from prehypertension to hypertension, and these alterations may serve as potential imaging biomarkers for hypertension.</p><p><strong>Study type: </strong>Cross-sectional.</p><p><strong>Subjects: </strong>Five hundred thirteen participants (150 healthy controls, 175 prehypertensive individuals, and 188 hypertensive patients).</p><p><strong>Sequence: </strong>DTI with an echo planar imaging sequence at 3.0 T.</p><p><strong>Assessment: </strong>Whole-brain structural networks were constructed using deterministic fiber tracking. Modularity, rich-club organization (rich-club, feeder and local connections), small-world property, global efficiency, local efficiency, clustering coefficient, and nodal efficiency were quantified using graph-theoretical analysis. Network-based statistics (NBS) were applied to identify significant group differences in white matter connectivity.</p><p><strong>Statistical tests: </strong>Analysis of variance for group comparisons, with post hoc least significant difference t-testing. Logistic regression assessed the predictive power of network features, while Pearson correlation evaluated relationships between blood pressure and network disruptions. Area under the receiver operating characteristic (ROC) curve (AUC) was used to assess diagnostic performance. A significance threshold of p < 0.05 was applied.</p><p><strong>Results: </strong>Prehypertensive individuals exhibited significant early reductions in feeder connections, whereas hypertensive patients demonstrated widespread significant deterioration in rich-club connections. A statistically significant compensatory increase in local connection strength was observed in prehypertension but declined in hypertension. Logistic regression confirmed that rich-club connection strength and density effectively differentiated hypertensive individuals, with ROC analysis showing good discriminatory power (AUC: 0.803 and 0.816, respectively).</p><p><strong>Data conclusion: </strong>This study showed progressive disruption of rich-club organization in prehypertension and hypertension. This disruption has the potential to be an early neuroimaging biomarker for identifying individuals at risk of hypertension-related brain dysfunction.</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":3.3,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144234354","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":"Functional Sodium MRI in the Measurement of Corticomedullary Sodium Content and Its Role in Differentiating Between Transplanted Kidneys With Superior and Inferior Graft Function.","authors":"Gen Chen, Zhouyan Liao, Siyuan Ma, Pan Luo, Baodi Deng, Xiaoxiao Zhang, Qiuxia Wang, Hao Tang, Xia Lu, Xuemei Hu, Nianqiao Gong, Zhen Li","doi":"10.1002/jmri.70001","DOIUrl":"https://doi.org/10.1002/jmri.70001","url":null,"abstract":"<p><strong>Background: </strong>Sodium MRI (²³Na-MRI) measuring tissue sodium content may directly assess the corticomedullary gradient (CMG) in the kidney. However, it is understudied in transplanted kidneys.</p><p><strong>Purpose: </strong>To investigate associations between CMG in renal graft and urine concentrating ability, estimated glomerular filtration rate (eGFR), and biopsy-determined fibrosis scores, and to determine if CMG could differentiate between renal grafts with superior and inferior graft function.</p><p><strong>Study type: </strong>Prospective.</p><p><strong>Subjects: </strong>57 participants (39 males) with renal transplantation.</p><p><strong>Field strength/sequence: </strong>3D T2-weighted turbo spin echo sequence and ²³Na-MR imaging at 3 T.</p><p><strong>Assessment: </strong>Urine specific gravity and eGFR were assessed as measures of kidney function. Thirty-eight participants underwent biopsy within 2 days of MRI, and the Banff fibrosis score was assessed. The average medulla to cortex ratio (MCR) was determined from ²³Na-MRI analysis. Transplanted kidneys were divided into those with superior graft function (SGF, eGFR ≥ 45 mL/min/1.73 m²) and inferior graft function (IGF, eGFR < 45 mL/min/1.73 m²) groups.</p><p><strong>Statistical tests: </strong>Correlation analysis (Pearson or Spearman coefficient, r), intraclass correlation coefficient, and area under the receiver-operating characteristic curve (AUC).</p><p><strong>Results: </strong>MCR was 1.27 ± 0.11 and mean urine specific gravity was 1.013 ± 0.005. MCR was significantly correlated with mean urine specific gravity (r = 0.32) and with eGFR (r = 0.795). MCR distinguished IGF (n = 42) from SGF (n = 15) with an AUC of 0.851 (95% CI: 0.732, 0.931) with a cutoff of ≥ 1.295. MCR was significantly correlated with the Banff fibrosis score of tubulitis in areas of interstitial fibrosis and tubular atrophy (t-IFTA) (r = 0.347).</p><p><strong>Data conclusion: </strong>²³Na-MRI has the potential to show the CMG in transplanted kidneys, with MCR being correlated with urine concentrating ability. In addition, the transplanted kidney CMG was related to eGFR and the Banff fibrosis score t-IFTA.</p><p><strong>Evidence level: </strong>Level 2.</p><p><strong>Technical efficacy: </strong>Stage 2.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144225708","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":"Association of Myelin Disruption and Iron Accumulation on MRI With Parkinson's Disease Severity.","authors":"Xiaolu Li, Shuting Bu, Huize Pang, Hongmei Yu, Mengwan Zhao, Juzhou Wang, Yueluan Jiang, Mathias Nittka, Yu Liu, Guoguang Fan","doi":"10.1002/jmri.29832","DOIUrl":"https://doi.org/10.1002/jmri.29832","url":null,"abstract":"<p><strong>Background: </strong>Myelin degeneration and iron accumulation are key features of Parkinson's disease (PD), yet their interrelationship and contribution to disease severity remain unclear.</p><p><strong>Purpose: </strong>To assess alterations in myelin content and iron deposition in PD, investigate their interrelationship and associations with disease severity.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Population: </strong>Fifty-three PD patients (27 females; median age 67 years) and 30 age- and sex-matched healthy controls (HCs) (14 females; median age 64 years).</p><p><strong>Field strength/sequence: </strong>3-T, two-dimensional section-selective steady-state free precession for MR fingerprinting, three-dimensional multiecho gradient-recalled echo for quantitative susceptibility mapping (QSM), and 3D T1-weighted gradient echo sequence.</p><p><strong>Assessment: </strong>Cognitive impairment was evaluated using the Montreal Cognitive Assessment (MoCA), while motor dysfunction was evaluated with the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III). Thirty-five white and gray matter regions of interest (ROIs) were defined using the MNI atlas. Myelin water fraction (MWF) was quantified using multicomponent relaxometry, and iron deposition was assessed via QSM.</p><p><strong>Statistical tests: </strong>Voxel-wise comparisons were performed between PD and HCs. Stepwise regression analyses explored associations between significantly altered imaging metrics and disease severity. A corrected p < 0.05 was considered statistically significant.</p><p><strong>Results: </strong>PD patients showed significantly reduced MWF and increased susceptibility in multiple predefined ROIs. MoCA scores were significantly associated with MWF and T1 in the left thalamus (β = 104.213; β = -0.015), and susceptibility in the left thalamic radiation (β = -107.346). MDS-UPDRS III scores were significantly associated with MWF in the contralateral uncinate fasciculus (β = -130.751) and susceptibility in the internal capsule (β = 267.798). Notably, MWF in the right internal capsule (β = -0.213) and nucleus accumbens (β = -0.302), and T1 in the right internal capsule (β = 0.001) were significantly correlated with susceptibility.</p><p><strong>Data conclusion: </strong>This study may provide quantitative evidence of myelin loss and iron accumulation in PD. The observed associations between MRF, susceptibility, and disease severity could offer insights into PD pathophysiology.</p><p><strong>Level of evidence: 2: </strong>Technical Efficacy: Stage 1.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144199390","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":"Association of MRI Left Atrial Strain With Ischemic Stroke in Myocardial Infarction Patients.","authors":"Lei Chen, Wensu Chen, Jiahua Liu, Xinjia Du, Yuan Lu, Wenliang Che","doi":"10.1002/jmri.29833","DOIUrl":"https://doi.org/10.1002/jmri.29833","url":null,"abstract":"<p><strong>Background: </strong>Patients with myocardial infarction (MI) have a high risk of ischemic stroke (IS). Left atrial (LA) strain is associated with IS, but the relationship between them remains unclear in patients with MI.</p><p><strong>Purpose: </strong>To evaluate the relationship between MRI-derived LA strain and IS in patients with MI.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Population: </strong>Seven hundred eighty patients were diagnosed with acute MI (AMI), including 549 with ST-segment elevation MI (STEMI) and 231 with non-STEMI (NSTEMI).</p><p><strong>Field strength/sequence: </strong>3.0 T, balanced turbo field echo cine sequence.</p><p><strong>Assessment: </strong>Cardiac MRI was performed during hospitalization for AMI. LA reservoir and conduit strains were determined automatically from cine data by feature tracking (FT) software, and their association with IS was determined after correcting for possible confounders, such as new-onset atrial fibrillation (AF) and CHA₂DS₂-VASc score.</p><p><strong>Statistical tests: </strong>Cox regression models to explore the association between LA strain and IS; receiver operating characteristic (ROC) curve analysis to assess the ability of LA strain to identify IS and to determine high/low LA strain cutoff values; Kaplan-Meier curves for long-term IS risk assessment. A p value < 0.05 was considered significant.</p><p><strong>Results: </strong>The MR study was performed with a median of 5 (interquartile range: 4-6) days after admission. During a median follow-up of 29.1 months (interquartile range: 19.4-39.6), 38 (4.9%) patients had a new IS. After adjusting for AF and CHA₂DS₂-VASc, Cox regression analysis showed that both LA reservoir strain (hazard ratio [HR]: 0.933; 95% confidence interval (CI): 0.894-0.973) and LA conduit strain (HR: 0.909; 95% CI: 0.846-0.978) were independently associated with IS. Kaplan-Meier curves showed that patients with low LA strain had a significantly higher long-term risk of IS than patients with high LA strain.</p><p><strong>Data conclusion: </strong>LA strain is significantly associated with the incidence of IS after MI, even after adjusting for AF and CHA₂DS₂-VASc.</p><p><strong>Evidence level: </strong>Level 4.</p><p><strong>Technical efficacy: </strong>Stage 5.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144199389","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 \"Stroke Mechanism Subtypes and Prognosis in Patients With Symptomatic Intracranial Atherosclerosis Based on Multiparametric MRI\".","authors":"Shuang Xia, Huiying Wang","doi":"10.1002/jmri.29831","DOIUrl":"https://doi.org/10.1002/jmri.29831","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144159667","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 \"Federated Learning for Renal Tumor Segmentation and Classification on Multi-Center MRI Dataset\".","authors":"Wentao Yang, Tianyi Xia","doi":"10.1002/jmri.29830","DOIUrl":"https://doi.org/10.1002/jmri.29830","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144159664","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":"Advanced MRI Methods for Diagnosis and Monitoring of Multiple Sclerosis (MS).","authors":"Reza Elahi, Siavash Taremi, Anahita Najafi, Hanie Karimi, Elnaz Asadollahzadeh, Seyed Aidin Sajedi, Hamidreza Saligheh Rad, Mohammad Ali Sahraian","doi":"10.1002/jmri.29817","DOIUrl":"https://doi.org/10.1002/jmri.29817","url":null,"abstract":"<p><p>Multiple sclerosis (MS) is an autoimmune neuroinflammatory disorder affecting the central nervous system (CNS). It is primarily driven by an immune-mediated inflammatory response, leading to the demyelination of neurons. Neuroimaging, particularly magnetic resonance imaging (MRI), plays a crucial role in diagnosing, monitoring, and predicting the progression of MS. Conventional MRI sequences, including T1-weighted (T1w), T2-weighted (T2w), fluid-attenuated inversion recovery (FLAIR), and post-contrast T1 (T1ce) imaging, are commonly employed to visualize MS lesions. However, these standard MRI methods have limitations in clinical practice, such as reliance on the radiologist's expertise, difficulty in detecting heterogeneous patterns of demyelination in normal-appearing white and gray matter, and lack of specificity in differentiating between various clinical subtypes of MS. In recent years, advanced MRI methods have shown promise in overcoming these limitations, offering improved diagnostic accuracy and monitoring capabilities for MS. These methods include magnetic resonance spectroscopy (MRS), magnetization transfer (MT), diffusion tensor imaging (DTI), quantitative susceptibility mapping (QSM), sodium (23Na) MRI, double inversion recovery (DIR), phase-sensitive inversion-recovery (PSIR), M2PRAGE, resting-state functional MRI (Rs-fMRI), diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), myelin water imaging (MWI), magnetic resonance fingerprinting (MRF), chemical exchange saturation transfer (CEST) MRI, and ultrasmall superparamagnetic iron oxide (USPIO). These methods have been extensively studied for their ability to provide novel biomarkers for demyelination, track lesion progression in white and gray matter, and assess neurodegeneration in MS. This review aims to explore the methods, current knowledge, weaknesses, and future prospects of advanced MRI methods, with a particular focus on their capacity to introduce novel diagnostic biomarkers based on the underlying pathophysiology of MS. For a better understanding, we also provide original clinical images from our tertiary MS care center. Additionally, we will discuss how these methods may be used to monitor disease progression across different stages of MS. Finally, we introduce our proposed protocol for imaging MS based on advanced MRI methods. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144159661","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}