Journal of Magnetic Resonance Imaging最新文献

{"title":"High-Fidelity MRI Assessment of Cerebral Perfusion in Healthy Neonates Less Than 1 Week of Age.","authors":"Zhiyi Hu, Dengrong Jiang, Jennifer Shepard, Yuto Uchida, Kenichi Oishi, Wen Shi, Peiying Liu, Doris Lin, Vivek Yedavalli, Aylin Tekes, William Christopher Golden, Hanzhang Lu","doi":"10.1002/jmri.29740","DOIUrl":"https://doi.org/10.1002/jmri.29740","url":null,"abstract":"<p><strong>Background: </strong>Perfusion imaging of the brain has important clinical applications in detecting neurological abnormalities in neonates. However, such tools have not been available to date. Although arterial-spin-labeling (ASL) MRI is a powerful noninvasive tool to measure perfusion, its application in neonates has encountered obstacles related to low signal-to-noise ratio (SNR), large-vessel contaminations, and lack of technical development studies.</p><p><strong>Purpose: </strong>To systematically develop and optimize ASL perfusion MRI in healthy neonates under 1 week of age.</p><p><strong>Study type: </strong>Prospective.</p><p><strong>Subjects: </strong>Thirty-two healthy term neonates (19 female; postnatal age 1.9 ± 0.7 days).</p><p><strong>Field strength/sequence: </strong>3.0 T; T<sub>2</sub>-weighted half-Fourier single-shot turbo-spin-echo (HASTE) imaging, single-delay and multi-delay 3D gradient-and-spin-echo (GRASE) large-vessel-suppression pseudo-continuous ASL (LVS-pCASL).</p><p><strong>Assessment: </strong>Three studies were conducted. First, an LVS-pCASL MRI sequence was developed to suppress large-vessel spurious signals in neonatal pCASL. Second, multiple post-labeling delays (PLDs) LVS-pCASL were employed to simultaneously estimate normative cerebral blood flow (CBF) and arterial transit time (ATT) in neonates. Third, an enhanced background-suppression (BS) scheme was developed to increase the SNR of neonatal pCASL.</p><p><strong>Statistical tests: </strong>Repeated measure analysis-of-variance, paired t-test, spatial intraclass-correlation-coefficient (ICC), and voxel-wise coefficient-of-variation (CoV). P-value <0.05 was considered significant.</p><p><strong>Results: </strong>LVS-pCASL reduced spurious ASL signals, making the CBF images more homogenous and significantly reducing the temporal variation of CBF measurements by 58.0% when compared to the standard pCASL. Multi-PLD ASL yielded ATT and CBF maps showing a longer ATT and lower CBF in the white matter relative to the gray matter. The highest CBF was observed in basal ganglia and thalamus (10.4 ± 1.9 mL/100 g/min). Enhanced BS resulted in significantly higher test-retest reproducibility (ICC = 0.90 ± 0.04, CoV = 8.4 ± 1.2%) when compared to regular BS (ICC = 0.59 ± 0.12, CoV = 23.6 ± 3.8%).</p><p><strong>Data conclusion: </strong>We devised an ASL method that can generate whole-brain CBF images in 4 minutes with a test-retest image ICC of 0.9. This technique holds potential for studying neonatal brain diseases involving perfusion abnormalities.</p><p><strong>Plain language summary: </strong>MR imaging of cerebral blood flow in neonates remains a challenge due to low blood flow rates and confounding factors from large blood vessels. This study systematically developed an advanced MRI technique to enhance the reliability of perfusion measurements in neonates. The proposed method reduced signal artifacts from large blood vessels and improved the signal-to-noi","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143408711","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 \"Diagnosis of Sacroiliitis Through Semi-Supervised Segmentation and Radiomics Feature Analysis of MRI Images\".","authors":"Eros Montin","doi":"10.1002/jmri.29732","DOIUrl":"https://doi.org/10.1002/jmri.29732","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143399027","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":"Myocardial MRI Cine Radiomics: A Novel Approach to Risk-Stratification for Major Adverse Cardiovascular Events in Patients With ST-Elevation Myocardial Infarction.","authors":"Ming-Lei Li, Ruo-Yang Shi, Jin-Yu Zheng, Jin-Yi Xiang, Ward Hedges, Julia Liang, Jiani Hu, Jie Chen, Lei Zhao, Lian-Ming Wu","doi":"10.1002/jmri.29739","DOIUrl":"https://doi.org/10.1002/jmri.29739","url":null,"abstract":"<p><strong>Background: </strong>The incremental prognostic value of integrating myocardial cine radiomics into predictive models for major adverse cardiovascular events (MACE) risk in patients with ST-elevation myocardial infarction (STEMI) is unclear.</p><p><strong>Purpose: </strong>To determine if myocardial cine radiomics can improve risk assessment for MACE when combined with clinical information and cardiac MRI parameters in STEMI patients.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Subjects: </strong>One thousand twenty-four STEMI patients (83% male; mean age 59 ± 11 years) from two centers, divided into training (819 patients) and external testing (205 patients) cohorts.</p><p><strong>Field strength/sequence: </strong>3.0 T/balanced steady-state free precession cine, and phase-sensitive inversion recovery sequences.</p><p><strong>Assessment: </strong>The Rad_score was calculated as a weighted sum of independent radiomic variables derived from the logistic regression model, providing a concise representation of their combined prognostic impact. Six risk models were developed, incorporating varying combinations of MRI parameters, clinical variables, and Rad_score to comprehensively evaluate their prognostic performance. A final risk stratification, integrating left ventricular ejection fraction (LVEF), the extent of late gadolinium enhancement (LGE), and Rad_score, was established and compared with one based on LVEF alone.</p><p><strong>Statistical tests: </strong>The prognostic implications of the Rad_score were evaluated using univariable and multivariable Cox proportional hazards models. A P value <0.05 was considered significant.</p><p><strong>Results: </strong>During a median follow-up of 3.1 years, 139 patients (17%) in the training set and 30 patients (15%) in the testing set experienced MACE. Rad_score was identified as a significant risk factor for MACE, with a hazard ratio of 1.46 (1.38-1.55) (P < 0.01) in univariate Cox analysis. The risk stratification reclassified the risk for 33% of the study population in the training set and 34% in the testing set.</p><p><strong>Data conclusion: </strong>Myocardial cine radiomics are associated with MACE risk in STEMI patients and provide incremental improvement in risk stratification when combined with traditional parameters.</p><p><strong>Plain language summary: </strong>The development of radiomics has introduced new perspectives in both the diagnosis and prognosis of cardiovascular diseases. However, the incremental prognostic value of incorporating myocardial cine radiomics into predictive models for major adverse cardiovascular events (MACE) risk in patients with ST-elevation myocardial infarction (STEMI) remains unclear. This study integrates radiomics with traditional clinical parameters and cardiac magnetic resonance imaging (MRI) to evaluate its added value in assessing MACE risk in STEMI patients. The results demonstrate that radiomics is significantly associat","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143391062","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":"Deep Learning-Based Techniques in Glioma Brain Tumor Segmentation Using Multi-Parametric MRI: A Review on Clinical Applications and Future Outlooks","authors":"Delaram J. Ghadimi MD,&nbsp;Amir M. Vahdani MD,&nbsp;Hanie Karimi MD, MPH,&nbsp;Pouya Ebrahimi MD,&nbsp;Mobina Fathi MD, MPH,&nbsp;Farzan Moodi MD,&nbsp;Adrina Habibzadeh MD,&nbsp;Fereshteh Khodadadi Shoushtari MS,&nbsp;Gelareh Valizadeh PhD,&nbsp;Hanieh Mobarak Salari MS,&nbsp;Hamidreza Saligheh Rad PhD","doi":"10.1002/jmri.29737","DOIUrl":"https://doi.org/10.1002/jmri.29737","url":null,"abstract":"<p>CLINICAL APPLICATIONS OF DEEP LEARNING-BASED SEGMENTATION FOR GLIOMA. MRI IMAGE ADAPTED FROM BRATS 2020. BY GHADIMI ET AL.(1094-1109)\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure>\u0000 </p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":"61 3","pages":"spcone"},"PeriodicalIF":3.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmri.29737","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362936","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":"MRI Assessment of Geometric Microstructural Changes of White Matter in Infants With Periventricular White Matter Injury and Spastic Cerebral Palsy.","authors":"Miaoyan Wang, Hua Zhu, Tingting Huang, Jingjing Qiao, Bo Peng, Ni Shu, Anqi Qiu, Jian Cheng, Haoxiang Jiang","doi":"10.1002/jmri.29730","DOIUrl":"https://doi.org/10.1002/jmri.29730","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Periventricular white matter injury (PWMI) is a high-risk factor for spastic cerebral palsy (SCP).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To investigate the geometric microstructural changes in WM in infants with PWMI-SCP using MRI which may facilitate early identification.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Study type: &lt;/strong&gt;Retrospective cohort study.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Population: &lt;/strong&gt;Twenty-three healthy infants (aged 6.53-36 months), 25 infants with PWMI-SCP (aged 6-33 months), and 32 infants with PWMI-nonSCP (aged 6-36 months).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Field strength/sequence: &lt;/strong&gt;3.0 T, T1-weighted three-dimensional gradient-echo sequence, and diffusion tensor imaging (DTI) with a single-shot gradient echo planar sequence.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Assessment: &lt;/strong&gt;The brain was automatically segmented, parcellated into major regions of interest according to the Desikan-Killiany atlas and volumes extracted. Fractional anisotropy (FA) and mean diffusivity (MD) of regions were extracted from DTI data. Director field analysis (DFA) was used to assess the geometric microstructural properties of WM. Motor dysfunction was graded from l (mild) to 5 (severe) according to the Gross Motor Function Classification System.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Statistical tests: &lt;/strong&gt;Tests included analysis of variance, correlation analysis, mediation analysis, and receiver operating characteristic analysis. Corrected P-values &lt;0.05 were considered significant. Mediation analysis examined whether DFA metrics mediated the relationship between brain morphological and motor dysfunction. Models were constructed to identify PWMI-SCP.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The PWMI-SCP group exhibited significantly elevated all four DFA metrics (splay, bend, twist, and distortion), primarily in the corpus callosum, posterior thalamic radiata, and corona radiata, compared to the PWMI-nonSCP group, and was associated with enlarged lateral ventricles, reduced deep nuclear volumes and motor dysfunction. Mediation analysis indicated that increased splay in the corpus callosum partially mediates (mediating effect ratio: 29.74%, 22.46%) the relationship between the lateral ventricles and motor function. The results showed that DFA achieved a higher area under the curve (AUC) than the FA + MD, especially in distinguishing PWMI-nonSCP from PWMI-SCP (AUC = 0.93).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Data conclusion: &lt;/strong&gt;Monitoring fiber-orientational alterations may provide new insights into early identification of PWMI-SCP.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Plain language summary: &lt;/strong&gt;This study utilized directional field analysis (DFA) to systematically examine white matter microstructural changes in three groups: periventricular white matter injury with spastic cerebral palsy (PWMI-SCP), periventricular white matter injury without spastic cerebral palsy (PWMI-nonSCP), and healthy controls. The results revealed significantly abnormal increases in the white matter geometric structure within the sensorimotor circuit in the PWMI-SCP g","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143364644","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":"Diagnosis of Sacroiliitis Through Semi-Supervised Segmentation and Radiomics Feature Analysis of MRI Images.","authors":"Lei Liu, Ruotao Zhong, Yuzhen Zhang, Haoyang Wan, Shuju Chen, Nanfeng Zhang, JingJing Liu, Wei Mei, Ruibin Huang","doi":"10.1002/jmri.29731","DOIUrl":"https://doi.org/10.1002/jmri.29731","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Sacroiliitis is a hallmark of ankylosing spondylitis (AS), and early detection plays an important role in managing the condition effectively. MRI is commonly used for diagnosing sacroiliitis, traditional methods often depend on subjective interpretation or limited automation which can introduce variability in diagnoses. The integration of semi-supervised segmentation and radiomics features may reduce reliance on expert interpretation and the need for large annotated datasets, potentially enhancing diagnostic workflows.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To develop a diagnostic model for sacroiliitis and bone marrow edema (BME) using semi-supervised segmentation and radiomics analysis of MRI images.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Study type: &lt;/strong&gt;Retrospective cohort study.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Population: &lt;/strong&gt;A total of 257 patients (161 males, 93 females; age 11-74 years), including 155 sacroiliitis and 175 BME patients. A total of 514 sacroiliac joint (SIJ) MRI images are analyzed, with 359 used for training and 155 for testing.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Field strength/sequence: &lt;/strong&gt;3.0 T, spin echo T1-weighted imaging (T1WI) and short-tau inversion recovery (STIR).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Assessment: &lt;/strong&gt;SIJ segmentation is automated using the semi-supervised segmentation-based Unimatch framework. Manual delineation of SIJ regions of interest (ROIs) on T1WI images by an experienced radiologist (W.M., 10-year experience) served as the reference standard for segmentation performance evaluation. Radiomics features from T1WI and STIR are used to train machine learning models, including support vector machine (SVM), logistic regression (LR), and light gradient boosting machine (LightGBM), for sacroiliitis and BME detection. Performance is assessed using area under the curve (AUC), sensitivity, specificity, and accuracy. The Dice coefficient is used to assess the performance of the semi-supervised segmentation model on SIJ segmentation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Statistical tests: &lt;/strong&gt;Performance is evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Result: &lt;/strong&gt;The Unimatch model achieves an average Dice coefficient of 0.859 for SIJ segmentation. AUCs for sacroiliitis detection are 0.84 (LR), 0.86 (SVM), and 0.78 (LightGBM), while for BME detection, AUCs are 0.73 (LR), 0.76 (SVM), and 0.70 (LightGBM).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Data conclusion: &lt;/strong&gt;This study demonstrates that semi-supervised segmentation combined with radiomics features and machine learning models provides a promising approach for diagnosis of sacroiliitis and BME.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Plain language summary: &lt;/strong&gt;This study aimed to improve the diagnosis of sacroiliitis and bone marrow edema in patients with ankylosing spondylitis. The researchers used a method that automatically segments MRI images and analyzes features from those images. By applying machine learning, they created models to help detect sacroiliitis and bone marrow","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143255863","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 \"Associations of Central Arterial Stiffness With Brain White Matter Integrity and Gray Matter Volume in MRI Across the Adult Lifespan\".","authors":"Harald E Möller","doi":"10.1002/jmri.29733","DOIUrl":"https://doi.org/10.1002/jmri.29733","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143189580","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 \"Incorporating Radiologist Knowledge Into MRI Quality Metrics for Machine Learning Using Rank-Based Ratings\".","authors":"Yao Lu, Ninghao Chen","doi":"10.1002/jmri.29728","DOIUrl":"https://doi.org/10.1002/jmri.29728","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143189581","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":"Associations of Postencephalitic Epilepsy Using Multi-Contrast Whole Brain MRI: A Large Self-Supervised Vision Foundation Model Strategy.","authors":"Ronghui Gao, Anjiao Peng, Yifei Duan, Mengyao Chen, Tao Zheng, Meng Zhang, Lei Chen, Huaiqiang Sun","doi":"10.1002/jmri.29734","DOIUrl":"https://doi.org/10.1002/jmri.29734","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Postencephalitic epilepsy (PEE) is a severe neurological complication following encephalitis. Early identification of individuals at high risk for PEE is important for timely intervention.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To develop a large self-supervised vision foundation model using a big dataset of multi-contrast head MRI scans, followed by fine-tuning with MRI data and follow-up outcomes from patients with PEE to develop a PEE association model.&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;Fifty-seven thousand six hundred twenty-one contrast-enhanced head MRI scans from 34,871 patients for foundation model construction, and head MRI scans from 144 patients with encephalitis (64 PEE, 80 N-PEE) for the PEE association model.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Field strength/sequence: &lt;/strong&gt;1.5-T, 3-T, T1-weighted imaging, T2-weighted imaging, fluid attenuated inversion recovery, T1-weighted contrast-enhanced imaging.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Assessment: &lt;/strong&gt;The foundation model was developed using self-supervised learning and cross-contrast context recovery. Patients with encephalitis were monitored for a median of 3.7 years (range 0.7-7.5 years), with epilepsy diagnosed according to International League Against Epilepsy. Occlusion sensitivity mapping highlighted brain regions involved in PEE classifications. Model performance was compared with DenseNet without pre-trained weights.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Statistical tests: &lt;/strong&gt;Performance was assessed via confusion matrices, accuracy, sensitivity, specificity, precision, F1 score, and area under the receiver operating characteristic curve (AUC). The DeLong test evaluated AUC between the two models (P &lt; 0.05 for statistical significance).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The PEE association model achieved accuracy, sensitivity, specificity, precision, F1 score, and AUC of 79.3% (95% CI: 0.71-0.92), 92.3% (95% CI: 0.80-1.00), 68.8% (95% CI: 0.55-0.87), 70.6% (95% CI: 0.61-0.90), 80.0% (95% CI: 0.71-0.93), and 81.0% (95% CI: 0.68-0.92), respectively. A significant AUC improvement was found compared to DenseNet (Delong test, P = 0.03). The association model focused on brain regions affected by encephalitis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Data conclusion: &lt;/strong&gt;Using extensive unlabeled data via self-supervised learning addressed the limitations of supervised tasks with limited data. The fine-tuned foundation model outperformed DenseNet, which was trained exclusively on task data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Plain language summary: &lt;/strong&gt;This research develops a model to assess the occurrence epilepsy after encephalitis, a severe brain inflammation condition. By using over 57,000 brain scans, the study trains a computer program to recognize patterns in brain images. The model analyzes whole-brain scans to identify areas commonly affected by the disease, such as the temporal and frontal lobes. It was tested on data from patients with encephalitis and showed better performance than o","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143080140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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