Journal of Magnetic Resonance Imaging最新文献

{"title":"Evaluating the Potential of Quantitative Susceptibility Mapping for Detecting Iron Deposition of Renal Fibrosis in a Rabbit Model.","authors":"Tingting Zha, Zhiping Zhang, Liang Pan, Lei Peng, Yanan Du, Peng Wu, Jie Chen, Wei Xing","doi":"10.1002/jmri.29722","DOIUrl":"https://doi.org/10.1002/jmri.29722","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Purpose: </strong>To investigate the potential of QSM to detect iron deposition in RF.</p><p><strong>Study type: </strong>Animal model.</p><p><strong>Animal model: </strong>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.</p><p><strong>Field strength/sequence: </strong>3 T, QSM with gradient echo, arterial spin labeling with gradient spin echo.</p><p><strong>Assessment: </strong>Bilateral kidney QSM values (χ) in the cortex (χ_CO) and outer medulla (χ_OM) were evaluated with histopathology.</p><p><strong>Statistical tests: </strong>Analysis of variance, Kruskal-Wallis, Spearman's correlation, and the area under the receiver operating characteristic curve (AUC). P < 0.05 was significant.</p><p><strong>Results: </strong>In fibrotic kidneys, χ_CO decreased at 7 days ([-6.69 ± 0.98] × 10^-2 ppm) and increased during 14-28 days ([-1.85 ± 2.11], [0.14 ± 0.58], and [1.99 ± 0.60] × 10^-2 ppm, respectively), while the χ_OM had the opposite trend. Both significantly correlated with histopathology (|r| = 0.674-0.849). AUC of QSM for distinguishing RF degrees was 0.692-0.993. In contralateral kidneys, the χ_CO initially decreased ([-6.67 ± 0.84] × 10^-2 ppm) then recovered to baseline ([-4.81 ± 0.89] × 10^-2 ppm), while the χ_OM at 7-28 days ([2.58 ± 1.40], [2.25 ± 1.83], [2.49 ± 2.11], [2.43 ± 1.32] × 10^-2 ppm, respectively) were significantly higher than baseline ([0.54 ± 0.18] × 10^-2 ppm).</p><p><strong>Data conclusion: </strong>Different iron deposition patterns were observed in RF with QSM values, suggesting the potential of QSM for iron deposition monitoring in RF.</p><p><strong>Plain language summary: </strong>Renal fibrosis (RF) is a common outcome in most kidney diseases, leading to scarring and loss of kidney function. Increasing evidence suggests that abnormal iron metabolism plays an important role in RF. This study used a technique called quantitative susceptibility mapping (QSM) to measure kidney iron levels in rabbits with RF. Specifically, rabbits with advanced RF exhibited higher kidney iron concentrations, and moderate to strong correlations between QSM values and histopathology demonstrated that QSM could accurately detect changes in iron levels and assess RF severity. Overall, QSM shows promise as a tool for monitoring iron deposition in RF progression.</p><p><strong>Evidence level: </strong>2 TECHNICAL EFFICACY: Stage 3.</p>","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":"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}

{"title":"Editorial for \"MRI Radiomics Analysis in the Diagnostic Differentiation of Malignant Soft Tissue Myxoid Sarcomas and Benign Soft Tissue Myxomas of the Musculoskeletal System\".","authors":"Martin Schwartz","doi":"10.1002/jmri.29696","DOIUrl":"https://doi.org/10.1002/jmri.29696","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143046930","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":"Reference Values for Water-Specific T1, Intermuscular and Intramuscular Fat Content in Skeletal Muscle at 2.89 T.","authors":"Stephen J Foulkes, Mark J Haykowsky, Rachel Sherrington, Amy A Kirkham, Justin Grenier, Peter Seres, David I Paterson, Richard B Thompson","doi":"10.1002/jmri.29718","DOIUrl":"https://doi.org/10.1002/jmri.29718","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;MRI offers quantification of proton density fat fraction (PDFF) and tissue characteristics with T1 mapping. The influence of age, sex, and the potential confounding effects of fat on T1 values in skeletal muscle in healthy adults are insufficiently known.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To determine the accuracy and repeatability of a saturation-recovery chemical-shift encoded multiparametric approach (SR-CSE) for quantification of T1&lt;sub&gt;Water&lt;/sub&gt; and muscle fat content, and establish normative values (age, sex) from a healthy cohort.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Study type: &lt;/strong&gt;Prospective observational; phantoms (NiCL&lt;sub&gt;2&lt;/sub&gt;-agarose T1 phantoms with no fat content; gadolinium T1 phantoms with mixed fat-water content).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Populations: &lt;/strong&gt;A total of 130 healthy community-dwelling adults (63 male, 18-76 years) free of chronic health conditions that require regular prescription medication, and with no contraindications to MRI.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Field strength/sequence: &lt;/strong&gt;2.89 T; gradient echo sequences including saturation-recovery chemical-shift encoded T1 mapping (SR-CSE); MOLLI; SASHA; CSE; and single voxel spectroscopy.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Assessment: &lt;/strong&gt;SR-CSE provided T1&lt;sub&gt;Water&lt;/sub&gt; and PDFF maps for assessment of intramuscular (MF&lt;sub&gt;Intra&lt;/sub&gt;), intermuscular (MF&lt;sub&gt;Inter&lt;/sub&gt;), and subcutaneous fat and muscle volumes (thigh, paraspinal muscles). Comparison with MOLLI/SASHA T1 mapping.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Statistical tests: &lt;/strong&gt;Univariable and multivariable linear regression, general linear models, Bland and Altman, coefficient of variation (CV). P-value &lt;0.05 was considered statistically significant.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Phantom and in vivo validation studies showed excellent accuracy of SR-CSE T1&lt;sub&gt;Water&lt;/sub&gt; and PDFF vs. values from reference standards and repeatability CVs between 0.2% and 2.6% for T1&lt;sub&gt;Water&lt;/sub&gt;, R2*, MF&lt;sub&gt;Inter&lt;/sub&gt;, MF&lt;sub&gt;Intra&lt;/sub&gt;, subcutaneous fat and muscle volumes. Mean T1&lt;sub&gt;Water&lt;/sub&gt; was 36 msec significantly higher in females (1445 ± 23 msec vs. 1409 ± 22 msec), with no age-effect (P = 0.35). Females had significantly higher values for MF&lt;sub&gt;Inter&lt;/sub&gt; (10.4% ± 4.8% vs. 7.1% ± 2.9%) and MF&lt;sub&gt;Intra&lt;/sub&gt; (2.6% ± 1.0% vs. 2.3% ± 0.8%), both of which increased with age, secondary to lower muscle volume. MOLLI and SASHA T1 values had a fat-related bias of 21.7/35.0 msec per 1% increase in fat fraction (MFF&lt;sub&gt;Intra&lt;/sub&gt;), in vivo, and a constant bias of -319.8/+35.6 msec, respectively.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Data conclusion: &lt;/strong&gt;SR-CSE provides accurate (vs. phantoms) and repeatable assessment of water-specific T1 values and muscle and fat volumes. Conventional methods (SASHA, MOLLI) have a significant fat-modulated T1-bias. T1&lt;sub&gt;Water&lt;/sub&gt; values are higher in females with no significant age dependence.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Plain language summary: &lt;/strong&gt;We developed and tested the accuracy of a new MRI approach to measure tissue ","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143033237","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":"Breast MRI to Screen Women With Extremely Dense Breasts.","authors":"Carla Sitges, Ritse M Mann","doi":"10.1002/jmri.29716","DOIUrl":"https://doi.org/10.1002/jmri.29716","url":null,"abstract":"<p><p>Women with extremely dense breasts are at a higher risk of breast cancer, and the sensitivity of mammography in this group is reduced due to the masking effect of overlapping tissue. This review examines supplemental screening methods to improve detection in this population, with a focus on MRI. Morphologic techniques offer limited benefits, digital breast tomosynthesis (DBT) shows inconsistent results, and ultrasound (US), while improving cancer detection rates (CDR), results in a higher rate of false positives. Functional imaging techniques show better performance, molecular breast imaging increases CDR but is limited in availability, and contrast-enhanced mammography is promising, with good results and as an accessible technique, but requires further validation. MRI, with sensitivity ranging from 81% to 100%, is the most supported modality. Despite strong evidence for MRI in this population, high costs, use of contrast, and longer scan times hinder widespread use. Abbreviated MRI protocols aim to overcome these barriers by reducing costs and scan duration. As personalized screening becomes a future focus, MRI remains the most effective option for women with extremely dense breasts. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 5.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143033233","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":"Left Ventricular Hemodynamic Forces Changes in Fabry Disease: A Cardiac Magnetic Resonance Study.","authors":"Jialin Li, Shichu Liang, Ziqian Xu, Ke Wan, Lutong Pu, Jie Wang, Yuchi Han, Yucheng Chen","doi":"10.1002/jmri.29700","DOIUrl":"https://doi.org/10.1002/jmri.29700","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Hemodynamic force (HDF) from cardiac MRI can indicate subclinical myocardial dysfunction, and help identify early cardiac changes in patients with Fabry disease (FD). The hemodynamic change in FD patients remains unclear.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To explore HDF changes in FD and the potential of HDF measurements as diagnostic markers indicating early cardiac changes in FD.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Study type: &lt;/strong&gt;Single-center, prospective, observational study.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Population: &lt;/strong&gt;Forty-six FD patients (age: 38 ± 12, females: 45.65%) and 46 sex- and age-matched healthy controls (HCs).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Field strength/sequence: &lt;/strong&gt;3 T, cardiac MRI including steady-state free precession cine imaging (during multiple breath-holds), phase-sensitive inversion recovery sequence for late gadolinium enhancement (LGE) imaging, and motion-corrected modified Look-Locker inversion recovery sequence for T1 mapping.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Assessment: &lt;/strong&gt;Analysis of strains and HDF were performed on the cine imaging. HDF parameters includes apical-basal force, systolic impulse, systolic peak, systolic-diastolic transition, diastolic deceleration, and atrial thrust. Moreover, FD patients were categorized with left ventricular hypertrophy (LVH+) (the maximal wall thickness &gt;12 mm) or without LVH (LVH-). Mainz Severity Score Index (MSSI) score was calculated to measure the progression of FD.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Statistical tests: &lt;/strong&gt;Group comparison tests, logistic regression, and receiver operating characteristic curve (ROC) were performed. A P-value &lt;0.05 was considered statistically significant.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;FD patients showed significantly lower native T1 (1161.1 ± 55.4 vs. 1202.8 ± 42.0 msec) and higher systolic impulse (33.8 ± 9.9 vs. 24.8 ± 9.5%). The systolic impulse in HDF analysis increased even in the pre-hypertrophic stage. The increased myocardial global longitudinal strain (r = 0.419) and systolic impulse (r = 0.333) showed positive correlations with a higher MSSI score. The AUC of systolic impulse and global native T1 showed no significant difference (0.764 vs. 0.790, P = 0.784).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Data conclusion: &lt;/strong&gt;Increased systolic impulse and systolic peak can be observed in FD patients. Systolic impulse showed potential ability for screening pre-LVH FD patients and correlated with disease severity in FD patients.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Plain language summary: &lt;/strong&gt;This study explored hemodynamic changes in patients with Fabry disease (FD) using hemodynamic force (HDF) analysis based on cardiac MRI. 46 FD patients were included and analysis of cardiac function, native T1, strains, and hemodynamic changes on cardiac MRI images were performed. The results showed that systolic impulse and systolic peak of HDF analysis were increased in FD patients, and systolic impulse may increase even in the pre-hypertrophic stage. Systolic impulse was correlated with disease severity in patients wit","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143023810","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":"Magnetic Resonance Acoustic Radiation Force Imaging (MR-ARFI).","authors":"Henrik Odéen, Allison H Payne, Dennis L Parker","doi":"10.1002/jmri.29712","DOIUrl":"https://doi.org/10.1002/jmri.29712","url":null,"abstract":"<p><p>This review covers the theoretical background, pulse sequence considerations, practical implementations, and multitudes of applications of magnetic resonance acoustic radiation force imaging (MR-ARFI) described to date. MR-ARFI is an approach to encode tissue displacement caused by the acoustic radiation force of a focused ultrasound field into the phase of a MR image. The displacement encoding is done with motion encoding gradients (MEG) which have traditionally been added to spin echo-type and gradient recalled echo-type pulse sequences. Many different types of MEG (monopolar, bipolar, tripolar etc.) have been described and pros and cons are discussed. We further review studies investigating the safety of MR-ARFI, as well as approaches to simulate the MR-ARFI displacement. Lastly, MR-ARFI applications such as for focal spot localization, tissue stiffness interrogation following thermal ablation, trans-skull aberration correction, and simultaneous MR-ARFI and MR thermometry are discussed. EVIDENCE LEVEL: N/A TECHNICAL EFFICACY: Stage 1.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143023814","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":"MRI Radiomics Analysis in the Diagnostic Differentiation of Malignant Soft Tissue Myxoid Sarcomas From Benign Soft Tissue Musculoskeletal Myxomas.","authors":"Hadas Benhabib, Daniel Brandenberger, Katherine Lajkosz, Elizabeth G Demicco, Kim M Tsoi, Jay S Wunder, Peter C Ferguson, Anthony M Griffin, Ali Naraghi, Masoom A Haider, Lawrence M White","doi":"10.1002/jmri.29691","DOIUrl":"https://doi.org/10.1002/jmri.29691","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Differentiation of benign myxomas and malignant myxoid sarcomas can be difficult with an overlapping spectrum of morphologic MR findings.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To assess the diagnostic utility of MRI radiomics in the differentiation of musculoskeletal myxomas and myxoid sarcomas.&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 523 patients were included; histologically proven myxomas (N = 201) and myxoid sarcomas (N = 322), randomly divided (70:30) into training:test subsets.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Sequence/field strength: &lt;/strong&gt;T1-weighted (T1W), T2-weighted fat-suppressed (fluid-sensitive), and T1-weighted post-contrast (T1W + C) sequences at 1.0 T, 1.5 T, or 3.0 T.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Assessment: &lt;/strong&gt;Seven semantic (qualitative) tumor features were assessed in each case. Manual 3D tumor segmentations performed with radiomics features extracted from T1W, fluid-sensitive, and T1W + C acquisitions. Models were constructed based on radiomic features from individual sequences and from their combination, both with and without the addition of qualitative tumor features.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Statistical tests: &lt;/strong&gt;Intraclass correlation evaluated in 60 cases segmented by three readers. Features with intraclass correlation &lt;0.7 excluded from further analysis. Boruta feature selection and Random Forest modeling performed using the training-dataset, with resultant models used to assess class discrimination (myxoma vs. myxoid sarcoma) in the test dataset. Radiomics score defined as probability class = myxoma. Logistic regression modeling employed to estimate performance of the radiomics score. Area under the receiver operating characteristic curve (AUC) was used to assess diagnostic performance, and DeLong's test to assess performance between constructed models. A P-value &lt;0.05 was considered significant.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Four qualitative semantic features showed significant predictive power in class discrimination. Radiomic models demonstrated excellent differentiation of myxomas from myxoid sarcomas: AUC of 0.9271 (T1W), 0.9049 (fluid-sensitive), and 0.9179 (T1W + C). Incorporation of multiparametric data or semantic features did not significantly improve model performance (P ≥ 0.08) compared to radiomic models derived from any individual MRI sequence alone.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Data conclusion: &lt;/strong&gt;MRI radiomics appears to be accurate in the differentiation of myxomas from myxoid sarcomas. Classification performance did not improve when incorporating qualitative features or multiparametric imaging data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Plain language summary: &lt;/strong&gt;Accurately distinguishing between benign soft tissue myxomas and malignant myxoid sarcomas is essential for guiding appropriate management but remains challenging with conventional MRI interpretation. This study utilized radiomics, a method that extracts quantitative mathematically derived features from images, to ","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143023817","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":"Improving Image Quality and Decreasing SAR With High Dielectric Constant Pads in 3 T Fetal MRI.","authors":"Zhengyang Zhu, Xunwen Xue, Tang Tang, Chao Luo, Ye Li, Jing Chen, Biyun Xu, Zengping Lin, Xin Zhang, Zhengge Wang, Jun Chen, Jiaming Lu, Wen Zhang, Xin Li, Qian Chen, Zhuoru Jiang, Junxia Wang, Qing Hu, Sven Haller, Ming Li, Chenchen Yan, Bing Zhang","doi":"10.1002/jmri.29677","DOIUrl":"https://doi.org/10.1002/jmri.29677","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;At high magnetic fields, degraded image quality due to dielectric artifacts and elevated specific absorption rate (SAR) are two technical challenges in fetal MRI.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To assess the potential of high dielectric constant (HDC) pad in increasing image quality and decreasing SAR for 3 T fetal MRI.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Study type: &lt;/strong&gt;Prospective.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Field strength/sequence: &lt;/strong&gt;3 T. Balanced steady-state free precession (bSSFP) and single-shot fast spin-echo (SSFSE).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Population: &lt;/strong&gt;One hundred twenty-eight participants (maternal-age 29.0 ± 3.6, range 20-40; gestational-age 30.3 ± 3.5 weeks, range 22-37 weeks) undertook bSSFP and 40 participants (maternal-age 29.5 ± 3.8, range 19-40; gestational-age 30.4 ± 3.5 weeks, range 23-37 weeks) undertook SSFSE.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Assessment: &lt;/strong&gt;Patient clinical characteristics were recorded, such as gestational-age, amniotic-fluid-index, abdominal-circumference, body-mass-index, and fetal-presentation. Quantitative Image-quality analysis included signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Qualitative analysis was performed by three radiologists with four-point scale to evaluate overall image quality, dielectric artifact, and diagnostic confidence. Whole-body total SAR was obtained from the vendor workstation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Statistical testing: &lt;/strong&gt;Paired rank sum test was used to analyze the differences in SNR, CNR, overall image quality, dielectric artifact, diagnostic confidence, and SAR with and without HDC pad. Spearman correlation test was used to detect correlations between image quality variable changes and patient clinical characteristics. P values &lt;0.05 were set as statistical significance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;With HDC pad, SNR and CNR was significantly higher (41.45% increase in SNR, 54.05% increase in CNR on bSSFP; 258.76% increase in SNR, 459.55% increase in CNR on SSFSE). Overall qualitative image quality, dielectric artifact and diagnostic confidence improved significantly. Adding HDC pad significantly reduced Whole-body total SAR (32.60% on bSSFP; 15.40% on SSFSE). There was no significant correlation between image quality variable changes and participant clinical characteristics (P-values ranging from 0.072 to 0.992).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Data conclusion: &lt;/strong&gt;In the clinical setting, adding a HDC pad might increase image quality while reducing dielectric artifact and SAR.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Plan language summary: &lt;/strong&gt;Dielectric artifacts and elevated SAR are two technical problems in 3T fetal MRI. In a prospective analysis of 168 pregnant participants undertaking 3.0T fetal MRI scanning, high dielectric constant (HDC) pad increased SNR by 41.45%, CNR by 54.05% on bSSFP, and SNR by 258.76%, CNR by 459.55% on SSFSE. Overall image quality, dielectric artifact reduction, and diagnostic confidence assessed by three radiologists was improved. Whole-body total SAR decreased","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143006250","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}