Journal of Magnetic Resonance Imaging最新文献

{"title":"Editorial for \"Habitat Radiomics Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Assessing Axillary Lymph Node Burden in Clinical T1-T2 Stage Breast Cancer: A Multicenter and Interpretable Study\".","authors":"Marialena Tsarouchi, Alexandros Vamvakas","doi":"10.1002/jmri.29809","DOIUrl":"https://doi.org/10.1002/jmri.29809","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144022166","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":"Suspicious MRI Safety Statistics.","authors":"Michael Steckner, Shao Jin Ong, Martin J Graves","doi":"10.1002/jmri.29808","DOIUrl":"https://doi.org/10.1002/jmri.29808","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143995927","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":"Design and Implementation of a Multi-Center Trial of ¹²⁹Xe Gas Exchange MRI and MRS to Evaluate Longitudinal Progression of COPD.","authors":"Bastiaan Driehuys, Shuo Zhang, Aryil Bechtel, Andrew D Hahn, Guilhem Collier, Peter J Niedbalski, Yuh-Chin Huang, Zackary I Cleveland, Matthew M Willmering, John P Mugler, Jaime Mata, Yun Michael Shim, Mario Castro, Sarah Svenningsen, Yonni Friedlander, Terence Ho, Sean Fain, Eric A Hoffman, Jim M Wild, Robert P Thomen, Talissa Altes, Ummul Afia Shammi, Will Harris, Yixuan Zou, Alexandre Fernandez Coimbra, Paula Belloni, Laura C Bell, David Mummy","doi":"10.1002/jmri.29769","DOIUrl":"https://doi.org/10.1002/jmri.29769","url":null,"abstract":"<p><p>MR imaging holds the potential to enhance drug development efficiency by de-risking early phase studies and increasing confidence in results. It can improve patient selection, increase repeatability, and provide greater sensitivity to change, thereby enabling smaller, faster clinical trials. For trials in the pulmonary space, hyperpolarized ¹²⁹Xe MRI is appealing because it provides 3-dimensional imaging of pulmonary ventilation and gas exchange in a brief, non-invasive exam. Metrics derived from ¹²⁹Xe MRI may be more sensitive to disease progression than conventional lung function assessments and may thus provide a valuable means to evaluate numerous novel pharmacologic and biologic therapies now in development. However, despite the acute need for better patient selection and for prognostic and monitoring biomarkers, ¹²⁹Xe MR imaging is not yet widely utilized in pulmonary drug development, partly because such trials must be conducted at multiple centers to enroll enough participants. Thus, incorporating ¹²⁹Xe MRI requires broader dissemination, harmonized image acquisition protocols, standardized dose delivery, visualization, and quantification. Multi-site trials must also be able to operate across all major MRI vendor platforms and diverse software/hardware revisions. To this end, the ¹²⁹Xe MRI Clinical trials consortium has published a harmonized protocol describing four recommended acquisitions. Here we report on the first industry-sponsored study to deploy this ¹²⁹Xe MRI/MRS protocol in a multi-center, multi-platform, multi-national study to evaluate longitudinal progression of chronic obstructive pulmonary disease (COPD). We demonstrate the steps necessary to implement standardized ¹²⁹Xe-MRI acquisition techniques across multiple sites and discuss the practices implemented, quality control approaches, and lessons learned for facilitating and accelerating the implementation of future trials that incorporate this technology. Level of Evidence: 5.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144029419","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 \"Prostate Cancer Risk Stratification and Scan Tailoring Using Deep Learning With Abbreviated Prostate MRI\".","authors":"Felipe Sahb Furtado","doi":"10.1002/jmri.29806","DOIUrl":"https://doi.org/10.1002/jmri.29806","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143985540","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":"Prostate Cancer Risk Stratification and Scan Tailoring Using Deep Learning on Abbreviated Prostate MRI.","authors":"Patricia M Johnson, Tarun Dutt, Luke A Ginocchio, Amanpreet Singh Saimbhi, Lavanya Umapathy, Kai Tobias Block, Daniel K Sodickson, Sumit Chopra, Angela Tong, Hersh Chandarana","doi":"10.1002/jmri.29798","DOIUrl":"https://doi.org/10.1002/jmri.29798","url":null,"abstract":"<p><strong>Background: </strong>MRI plays a critical role in prostate cancer (PCa) detection and management. Bi-parametric MRI (bpMRI) offers a faster, contrast-free alternative to multi-parametric MRI (mpMRI). Routine use of mpMRI for all patients may not be necessary, and a tailored imaging approach (bpMRI or mpMRI) based on individual risk might optimize resource utilization.</p><p><strong>Purpose: </strong>To develop and evaluate a deep learning (DL) model for classifying clinically significant PCa (csPCa) using bpMRI and to assess its potential for optimizing MRI protocol selection by recommending the additional sequences of mpMRI only when beneficial.</p><p><strong>Study type: </strong>Retrospective and prospective.</p><p><strong>Population: </strong>The DL model was trained and validated on 26,129 prostate MRI studies. A retrospective cohort of 151 patients (mean age 65 ± 8) with ground-truth verification from biopsy, prostatectomy, or long-term follow-up, alongside a prospective cohort of 142 treatment-naïve patients (mean age 65 ± 9) undergoing bpMRI, was evaluated.</p><p><strong>Field strength/sequence: </strong>3 T, Turbo-spin echo T2-weighted imaging (T2WI) and single shot EPI diffusion-weighted imaging (DWI).</p><p><strong>Assessment: </strong>The DL model, based on a 3D ResNet-50 architecture, classified csPCa using PI-RADS ≥ 3 and Gleason ≥ 7 as outcome measures. The model was evaluated on a prospective cohort labeled by consensus of three radiologists and a retrospective cohort with ground truth verification based on biopsy or long-term follow-up. Real-time inference was tested on an automated MRI workflow, providing classification results directly at the scanner.</p><p><strong>Statistical tests: </strong>AUROC with 95% confidence intervals (CI) was used to evaluate model performance.</p><p><strong>Results: </strong>In the prospective cohort, the model achieved an AUC of 0.83 (95% CI: 0.77-0.89) for PI-RADS ≥ 3 classification, with 93% sensitivity and 54% specificity. In the retrospective cohort, the model achieved an AUC of 0.86 (95% CI: 0.80-0.91) for Gleason ≥ 7 classification, with 93% sensitivity and 62% specificity. Real-time implementation demonstrated a processing latency of 14-16 s for protocol recommendations.</p><p><strong>Data conclusion: </strong>The proposed DL model identifies csPCa using bpMRI and integrates it into clinical workflows.</p><p><strong>Evidence level: </strong>1.</p><p><strong>Technical efficacy: </strong>Stage 2.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144022377","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 \"Feasibility of Qualitative Evaluation and Quantitative T2 Mapping of Peripheral Nerves and Muscles Using GRAPPATINI\".","authors":"Hamidreza Shaterian Mohammadi, Sameer B Shah, Saeed Jerban","doi":"10.1002/jmri.29805","DOIUrl":"https://doi.org/10.1002/jmri.29805","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144011187","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":"Meta-Analysis Informed Functional Connectomes Representations for Depression Identification.","authors":"Xinyi Wang, Li Xue, Zhongpeng Dai, Junneng Shao, Yujie Zhang, Shui Tian, Rui Yan, Zhilu Chen, Zhijian Yao, Qing Lu","doi":"10.1002/jmri.29801","DOIUrl":"https://doi.org/10.1002/jmri.29801","url":null,"abstract":"<p><strong>Background: </strong>Meta-analyses in neuroimaging have gained popularity. However, their clinical utility remains uncertain. Convergent masks, containing repeated clusters from publications, are often focal and small, and voxel-wise features can lead to the curse of dimensionality, limiting discriminative ability in clinical diagnosis.</p><p><strong>Purpose: </strong>To develop a functional connectome representation (FCR) by integrating meta-analytic neuroimaging data and to evaluate its performance in identifying depression.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Subjects: </strong>The principal data set included 151 patients with depression (male/female, 72/79) and 105 healthy controls (male/female, 48/57). An external test data set comprised 109 patients (male/female, 44/65) and 54 healthy controls (male/female, 15/39).</p><p><strong>Field strength/sequence: </strong>3.0 T T1-weighted imaging, resting-state functional MRI with echo-planar sequence.</p><p><strong>Assessment: </strong>We performed the community detection algorithm and principal component analysis to develop the FCR. The model's performance based on the FCR was evaluated in terms of accuracy, specificity, and sensitivity. Effect sizes (Cohen's d) for FCR components were calculated between patients and healthy controls. Model robustness was assessed by analyzing the association between accuracy and the degree of shuffled features in the permutation test.</p><p><strong>Statistical tests: </strong>Chi-squared test, two-sample t-test, effect sizes (Cohen's d), permutation tests for accuracy validation, and correlation analysis. Significance was determined at p < 0.05.</p><p><strong>Results: </strong>Effect sizes (Cohen's d) for each of the 39 principal components to quantify the magnitude of differences between depressed patients and healthy controls, ranged from d = -0.22 to d = 0.84. The FCR-based diagnostic model achieved an accuracy of 89.42% (principal data set) and 83.35% (external data set). Permutation tests (n = 1000) indicated that the model's accuracy was significantly higher than chance level. A significant negative correlation was observed between random noise and accuracy (r = -0.093).</p><p><strong>Data conclusion: </strong>The FCR effectively discriminates between depressed patients and healthy controls, exhibiting strong diagnostic performance, generalization, and robustness, supporting its potential utility in clinical depression identification.</p><p><strong>Evidence level: </strong>Level 3.</p><p><strong>Technical efficacy: </strong>Stage 2.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143997510","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":"Current Landscape of Short-T2 Imaging Techniques in the Musculoskeletal System: The Past, Present and Future.","authors":"Pranjal Rai, Amit Kumar Janu, Nitin Shetty, Suyash Kulkarni","doi":"10.1002/jmri.29776","DOIUrl":"https://doi.org/10.1002/jmri.29776","url":null,"abstract":"<p><p>Conventional MRI is limited in imaging tissues with short T2 relaxation times, such as bone, ligaments, and cartilage, due to their rapid signal decay. This limitation has spurred the development of specialized MRI techniques designed specifically for short-T2 tissue imaging. Traditional pulse sequences, including three-dimensional gradient echo (3D-GRE), susceptibility-weighted imaging (SWI), and Fast Field Echo Resembling a CT using Restricted Echo-Spacing (FRACTURE), initially addressed some of these challenges but often lacked sufficient resolution or contrast differentiation. Recent advancements, such as ultrashort echo time (UTE), zero echo time (ZTE), 3D-Bone, and synthetic computed tomography (sCT), have significantly enhanced the diagnostic capabilities of MRI by providing high-quality, CT-like visualization without exposure to ionizing radiation. These innovations have substantially improved MRI's ability to depict bone morphology, assess joint pathology, identify subtle fractures, and characterize bone tumors with higher accuracy. Beyond musculoskeletal applications, these techniques have demonstrated emerging clinical utility in additional domains, including pulmonary and dental imaging. This review article evaluates conventional pulse sequences alongside emerging MRI innovations, highlighting their clinical applications, current limitations, and technical considerations. Continued optimization of these techniques promises broader clinical adoption, potentially reducing dependence on invasive and radiation-intensive imaging modalities. Evidence Level: N/A Technical Efficacy: Stage 3.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143970129","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":"Habitat Radiomics Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Assessing Axillary Lymph Node Burden in Clinical T1-T2 Stage Breast Cancer: A Multicenter and Interpretable Study.","authors":"Siyi Chen, Yue Zhang, Yuqi Su, Jie Tian, Yongxin Chen, Wenjie Tang, Yaheng Fan, Chen Jin, Yangcheng He, Yongzhou Xu, Hong Hu, Yuan Guo, Junping Li","doi":"10.1002/jmri.29796","DOIUrl":"https://doi.org/10.1002/jmri.29796","url":null,"abstract":"<p><strong>Background: </strong>Axillary lymph node burden(ALNB) is a critical factor in determining treatment strategies for clinical T₁-T₂ (cT₁-T₂) stage breast cancer. However, as ALNB assessment relies on invasive procedures, exploring non-invasive methods is essential.</p><p><strong>Purpose: </strong>To develop and validate a habitat radiomics model for assessing ALNB in cT₁-T₂ breast cancer, incorporating radiogenomic data to improve interpretability.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Population: </strong>468 patients with cT₁-T₂ stage breast cancer from two institutions and The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA)-Breast Invasive Carcinoma (BRCA) were included. The cohort was divided into training (n = 173), internal validation (n = 58), external validation (n = 130), and TCGA-BRCA sets (n = 107). Patients were categorized into high nodal burden (HNB; > 3 positive lymph nodes) and non-HNB (≤ 3 positive lymph nodes) groups.</p><p><strong>Field strength/sequence: </strong>1.5-T MRI and 3.0-T MRI, and three-dimensional dynamic contrast-enhanced T1-weighted gradient-echo sequences.</p><p><strong>Assessment: </strong>Two logistic regression models were developed using habitat-based and clinical features. Model performance was evaluated using the AUC. SHapley Additive exPlanations (SHAP) analysis was employed to identify key features. Radiogenomic analysis, including gene set enrichment and drug sensitivity assessments, was conducted using transcriptomic data from the TCGA-BRCA set.</p><p><strong>Statistical tests: </strong>Pearson correlation, Mann-Whitney U, genetic algorithm, logistic regression, AUC analysis, delong test, and SHAP analysis. A p-value < 0.05 was considered statistically significant.</p><p><strong>Results: </strong>The Habitat model outperformed the Clinical model (AUCs: 0.840-0.932 vs. 0.558-0.673). The SHAP analysis was used to rank feature importance, with subregion 3 showing the highest average SHAP value. Radiogenomic analysis indicated upregulation of the KEGG ribosome pathway in the HNB group and identified differential drug sensitivity profiles among risk groups.</p><p><strong>Data conclusion: </strong>The Habitat model has the potential to assess ALNB in cT₁-T₂ breast cancer and assist radiologists in axillary diagnosis, which may help reduce the need for unnecessary ALN dissection.</p><p><strong>Evidence level: </strong>3. Technical Efficacy: Stage 2.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144015369","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":"MR-Guidance of Gene Therapy for Brain Diseases: Moving From Palliative Treatment to Cures.","authors":"Dalton H Bermudez, Thomas Lilieholm, Walter F Block","doi":"10.1002/jmri.29804","DOIUrl":"https://doi.org/10.1002/jmri.29804","url":null,"abstract":"<p><p>Regulatory bodies in the U.S. and Europe recently approved a gene therapy for aromatic L-amino acid decarboxylase (AADC) deficiency, a rare neurologic disorder where a genetic mutation prevents dopamine production in the brain. Affected children fail to develop normal motor and cognitive functions. MRI-guided intraparenchymal delivery of AADC gene therapy to localized gray matter regions-specifically the substantia nigra and ventral tegmental area-has enabled the brain to produce dopamine, resulting in dramatic improvements in physical and cognitive outcomes. The need to target only a small brain region simplifies the surgical approach. However, gene therapy for broader neurodegenerative conditions has progressed more slowly than expected, despite significant global investment. Clinical efficacy depends heavily on the accurate delivery of gene therapeutics via direct brain infusion, cerebrospinal fluid (CSF) administration, or both. Inadequate image guidance during clinical trials makes it difficult to distinguish between true drug inefficacy and delivery failure. We highlight how increasing use of MRI for pre-surgical simulation and real-time therapy monitoring is accelerating gene therapy development for neurological diseases. This manuscript explores MRI's role in guiding intraparenchymal gene delivery, particularly using Convection Enhanced Delivery (CED). MRI contributes across the treatment timeline-from pre-surgical planning and infusion guidance to validating therapeutic coverage. We describe how MRI supports controlled therapeutic distribution for localized treatments and its potential to enable broader distributions needed for correcting widespread genetic anomalies. We also detail how structural and anatomical MRI sequences (T1, T2, Time of Flight, and Diffusion Tensor Imaging (DTI)) can help model likely infusion distributions. Finally, we provide an outlook on how advanced DTI-based algorithms and poroelastic theory could further improve modeling of infusion dynamics. Current MRI-based technologies can be integrated and enhanced to improve CED effectiveness, especially in very young pediatric patients. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 4.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144014508","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}