Magnetic resonance imaging最新文献

{"title":"Comparison of 3D BFFE and 3D TRANCE on pulmonary artery imaging with two T-SLIP placement strategies","authors":"Li Zhu ,&nbsp;Shuai Liu ,&nbsp;Xiaoying Tang ,&nbsp;Fei Shang ,&nbsp;Hong Yu ,&nbsp;Yuan Qu ,&nbsp;Yan Wang","doi":"10.1016/j.mri.2024.110274","DOIUrl":"10.1016/j.mri.2024.110274","url":null,"abstract":"<div><div>3D BFFE and TRANCE can provide a visualization of pulmonary vessels without injection of contrast agent. T-SLIP can observe a large area of vessels using an arterial spin labeling technique. 3D BFFE and TRANCE with two T-SLIP placement strategies were compared on pulmonary artery imaging. Ten male and thirteen female healthy volunteers were recruited in the present study. Each subject underwent non-contrast-enhanced pulmonary MRA. Four protocols were involved in the present study as follows: 3D BFFE with T-SLIP covering vena cava (VC) and right atrium and ventricle (RV), respectively; 3D TRANCE with T-SLIP covering VC and RV, respectively. The SNR were measured at eight ROIs, while CNR were measured at five ROIs. The number of visualized branch and image quality were analyzed by two radiologists. The differences of metrics among different imaging strategies were compared. SNR and CNR were compared between two T-SLIP placement strategies using a paired <em>t</em>-test. The branches number and image quality were compared between two T-SLIP placement strategies using a rank-sum test. The agreement of subjective assessment was conducted using a Kappa test. There were significant differences at all ROIs between TRANCE and BFEE with T-SLIP on RV (all <em>P</em> values &lt;0.01). TRANCE with T-SLIP on RV exhibited a higher CNR at all five ROIs compared with BFFE with T-SLIP on RV. TRANCE with T-SLIP on RV performed better on the number of observed branches and image quality compared with BFFE with T-SLIP on RV. 3D TRANCE with T-SLIP on RV can provide a higher SNR and image quality among four MR protocols, and thus may be a potential non-contrast enhanced technique in the visualization of pulmonary artery.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110274"},"PeriodicalIF":2.1,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142965540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of MRI-based tumor heterogeneity analysis for identification and pathologic staging of breast phyllodes tumors","authors":"Yue Liang,&nbsp;Qing-Yu Li,&nbsp;Jia-Hao Li,&nbsp;Lan Zhang,&nbsp;Ying Wang,&nbsp;Bin-Jie Wang,&nbsp;Chang-Fu Wang","doi":"10.1016/j.mri.2025.110325","DOIUrl":"10.1016/j.mri.2025.110325","url":null,"abstract":"<div><h3>Objective</h3><div>To explore the application value of MRI-based imaging histology and deep learning model in the identification and classification of breast phyllodes tumors.</div></div><div><h3>Methods</h3><div>Seventy-seven patients diagnosed as breast phyllodes tumors and fibroadenomas by pathological examination were retrospectively analyzed, and traditional radiomics features, subregion radiomics features, and deep learning features were extracted from MRI images, respectively. The features were screened and modeled using variance selection method, statistical test, random forest importance ranking method, Spearman correlation analysis, least absolute shrinkage and selection operator (LASSO). The efficacy of each model was assessed using the subject operating characteristic (ROC) curve, The DeLong test was used to assess the differences in the AUC values of the different models, and the clinical benefit of each model was assessed using the decision curve (DCA), and the predictive accuracy of the model was assessed using the calibration curve (CCA).</div></div><div><h3>Results</h3><div>Among the constructed models for classification of breast phyllodes tumors, the fusion model (AUC: 0.97) had the best diagnostic efficacy and highest clinical benefit. The traditional radiomics model (AUC: 0.81) had better diagnostic efficacy compared with subregion radiomics model (AUC: 0.70). De-Long test, there is a statistical difference between the fusion model traditional radiomics model, and subregion radiomics model in the training group. Among the models constructed to distinguish phyllodes tumors from fibroadenomas in the breast, the TDT_CIDL model (AUC: 0.974) had the best predictive efficacy and the highest clinical benefit. De-Long test, the TDT_CI combination model was statistically different from the remaining five models in the training group.</div></div><div><h3>Conclusion</h3><div>Traditional radiomics models, subregion radiomics models and deep learning models based on MRI sequences can help to differentiate benign from junctional phyllodes tumors, phyllodes tumors from fibroadenomas, and provide personalized treatment for patients.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110325"},"PeriodicalIF":2.1,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142950847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High correlation between Ki-67 expression and a novel perfusion MRI biomarker diffusion-derived vessel density (DDVD) in endometrial carcinoma","authors":"Fang Wang ,&nbsp;Yafei Wang ,&nbsp;Lisha Qi ,&nbsp;Jing Liang ,&nbsp;Ben-Heng Xiao ,&nbsp;Chen Zhang ,&nbsp;Yì Xiáng J. Wáng ,&nbsp;Zhaoxiang Ye","doi":"10.1016/j.mri.2025.110324","DOIUrl":"10.1016/j.mri.2025.110324","url":null,"abstract":"<div><h3>Objective</h3><div>This study aimed to investigate the feasibility of diffusion-derived vessel density (DDVD) in characterizing tumor microvasculature in endometrial carcinoma (EC), and to explore the correlations with Ki-67 proliferation status and histological type based on DDVD values.</div></div><div><h3>Methods</h3><div>There were in total 81 EC patients. There were 64 cases of non-aggressive histological type, and 17 cases of aggressive histological type. Ki-67 labeling index was low (&lt;50 %) in 35 cases and high (≥50 %) in 46 cases. DDVD_(b0b20) is calculated according to: DDVD_(b0b20) = S_b0/ROI_area0 – S_b20/ROI_area20, where S_b0 and S_b20 refer to the tissue signal when <em>b</em> is 0 or 20 s/mm². Intraclass correlation coefficient (ICC); two-tailed independent samples <em>t</em>-test and Mann-Whitney <em>U</em> test, and Receiver operating characteristic area under the curve (AUC) were applied for statistical analysis.</div></div><div><h3>Results</h3><div>Endometrial carcinoma showed lower DDVD_(b0b20) values (34.9 ± 21.2, au/pixel) compared with myometrium (65.3 ± 37.4, <em>P</em> &lt; 0.001). Tumors with Ki-67 high-proliferation or aggressive histological type had higher DDVD values than those with Ki-67 low-proliferation (44.17 (median) vs. 16.08, <em>P</em> &lt; 0.001]] or non-aggressive histological type (47.92 vs. 30.77, <em>P</em> = 0.002). DDVD_(b0b20) ROC curve analysis shows AUC of 0.842 for distinguishing between Ki-67 low- and high-expression, and AUC of 0.771 for distinguishing between non-aggressive and aggressive histological types. DDVD_(b0b20) &gt; 32.9 and DDVD_(b0b20) &gt; 50.1 provided a specificity of 85 % for identifying Ki67 high expression (sensitivity 78.3 %) and histological aggressive type (sensitivity 47.1 %), respectively.</div></div><div><h3>Conclusion</h3><div>DDVD can act as an imaging marker reflecting Ki-67 proliferation and histological aggressiveness of EC, thus helping pretreatment risk assessment in EC.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110324"},"PeriodicalIF":2.1,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142950843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time-dependent diffusion MRI and kinetic heterogeneity as potential imaging biomarkers for diagnosing suspicious breast lesions with 3.0-T breast MRI","authors":"Xue Li ,&nbsp;Chunmei Li ,&nbsp;Bin Hua ,&nbsp;Lei Jiang ,&nbsp;Min Chen","doi":"10.1016/j.mri.2025.110323","DOIUrl":"10.1016/j.mri.2025.110323","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aimed to evaluate the diagnostic efficacy of time-dependent diffusion magnetic resonance imaging (td-dMRI) and dynamic contrast-enhanced MRI (DCE-MRI)-based kinetic heterogeneity in differentiating suspicious breast lesions (categorised as Breast Imaging Reporting and Data System 4 or 5).</div></div><div><h3>Methods</h3><div>This prospective study included 51 females with suspicious breast lesions who underwent preoperative breast MRI, including DCE-MRI and td-dMRI. Six kinetic parameters, namely peak, persistent, plateau, washout component, predominant curve type, and heterogeneity, were extracted from the DCE series using MATLAB and SPM software. The td-dMRI data were analysed using the JOINT model to obtain five microstructural parameters and apparent diffusion coefficient at 50 ms (ADC_50ms). Chi-square or Fisher's exact test and the Mann–Whitney <em>U</em> test were used to compare these parameters between benign and malignant breast lesions. Univariate and multivariate logistic regression analyses with forward stepwise covariate selection were performed to identify significant clinical and radiologic variables. Differential diagnostic performance was evaluated using receiver operating characteristic curves and logistic regression analyses.</div></div><div><h3>Results</h3><div>For td-dMRI-derived parameters, the values of <em>f</em>_in and cellularity were significantly higher in malignant breast lesions compared to benign lesions (<em>P</em> = 0.001 and <em>P</em>&lt;0.001, respectively), while ADC_50ms was significantly lower in malignant lesions (<em>P</em> = 0.001). In the kinetic heterogeneity analysis, the washout component was higher in malignant lesions compared to benign lesions (<em>P</em> = 0.003). When combining significant td-dMRI and kinetic heterogeneity parameters, the area under the curve (AUC) value was 0.875, with an accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 82.69 %, 86.11 %, 75.00 %, 88.57 %, and 70.59 %, respectively. Notably, margin and kinetic pattern emerged as independent predictors of malignant breast lesions (<em>P</em> = 0.019 and 0.006, respectively). Furthermore, incorporating these two clinical-radiologic characteristics further enhanced diagnostic accuracy, yielding an AUC of 0.969, with accuracy, sensitivity, specificity, PPV, and NPV improving to 90.38 %, 86.11 %, 100 %, 100 %, and 76.19 %, respectively.</div></div><div><h3>Conclusions</h3><div>Kinetic heterogeneity- and td-dMRI-derived parameters are potentially non-invasive biomarkers for distinguishing suspicious breast lesions.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110323"},"PeriodicalIF":2.1,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143008250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison and calibration of MP2RAGE quantitative T1 values to multi-TI inversion recovery T1 values","authors":"Adam M. Saunders ,&nbsp;Michael E. Kim ,&nbsp;Chenyu Gao ,&nbsp;Lucas W. Remedios ,&nbsp;Aravind R. Krishnan ,&nbsp;Kurt G. Schilling ,&nbsp;Kristin P. O'Grady ,&nbsp;Seth A. Smith ,&nbsp;Bennett A. Landman","doi":"10.1016/j.mri.2025.110322","DOIUrl":"10.1016/j.mri.2025.110322","url":null,"abstract":"<div><div>While typical qualitative T1-weighted magnetic resonance images reflect scanner and protocol differences, quantitative T1 mapping aims to measure T1 independent of these effects. Changes in T1 in the brain reflect structural changes in brain tissue. Magnetization-prepared two rapid acquisition gradient echo (MP2RAGE) is an acquisition protocol that allows for efficient T1 mapping with a much lower scan time per slab compared to multi-TI inversion recovery (IR) protocols. We collect and register B1-corrected MP2RAGE acquisitions with an additional inversion time (MP3RAGE) alongside multi-TI selective inversion recovery acquisitions for four subjects. We use a maximum a posteriori (MAP) T1 estimation method for both MP2RAGE and compare to typical point estimate MP2RAGE T1 mapping, finding no bias from MAP MP2RAGE but a sensitivity to <span><math><msubsup><mi>B</mi><mn>1</mn><mo>+</mo></msubsup></math></span> inhomogeneities with MAP MP3RAGE. We demonstrate a tissue-dependent bias between MAP MP2RAGE T1 estimates and the multi-TI inversion recovery T1 values. To correct this bias, we train a patch-based ResNet-18 to calibrate the MAP MP2RAGE T1 estimates to the multi-TI IR T1 values. Across four folds, our network reduces the RMSE significantly (white matter: from 0.30 ± 0.01 s to 0.11 ± 0.02 s, subcortical gray matter: from 0.26 ± 0.02 s to 0.10 ± 0.02 s, cortical gray matter: from 0.36 ± 0.02 s to 0.17 ± 0.03 s). Using limited paired training data from both sequences, we can reduce the error between quantitative imaging methods and calibrate to one of the protocols with a neural network.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110322"},"PeriodicalIF":2.1,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142931936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of a diagnostic nomogram model for osteoporosis in the elderly using 3D multi-echo Dixon sequence combined with magnetization transfer imaging","authors":"Qiuju Fan ,&nbsp;Hui Tan ,&nbsp;Zhongxu Zhang ,&nbsp;Xingui Feng ,&nbsp;Nan Yu ,&nbsp;Yong Yu ,&nbsp;Shaoyu Wang ,&nbsp;Guangming Ma","doi":"10.1016/j.mri.2025.110320","DOIUrl":"10.1016/j.mri.2025.110320","url":null,"abstract":"<div><h3>Objective</h3><div>To develop a novel combined nomogram based on 3D multi-echo Dixon (qDixon), magnetization transfer imaging (MTI) and clinical risk factors for the diagnosis of osteoporosis.</div></div><div><h3>Materials and methods</h3><div>A total of 287 subjects who underwent MR examination with qDixon and MTI sequences participated in this study. These participants were randomly assigned to a training cohort and a validation cohort at a ratio of 7:3. We extracted and analyzed the bone marrow fat fraction (FF) and magnetization transfer ratio (MTR) of L1 ∼ 3 vertebrae, along with clinical data. Univariate and multivariate logistic regression analyses was used to assess independent predictors of OP in the training cohort. We established a diagnostic nomogram and evaluated its performance in terms of discrimination, calibration, and clinical value using the receiver operating characteristic curve (ROC) and calibration curve. Decision curve analysis (DCA) was performed to determine the clinical validity of the nomogram by measuring the net benefits at different threshold probabilities.</div></div><div><h3>Results</h3><div>Gender, age, FF, and MTR (all <em>P</em>﹤0.05) emerged as independent indicators for diagnosing osteoporosis. The AUCs for the FF, MTR, FF + MTR, and nomogram models were 0.842, 0.903, 0.923, and 0.941, respectively, in the training cohort and 0.779, 0.872, 0.901, and 0.929, respectively, in the validation cohort. The nomogram model exhibited good calibration and discrimination. DCA revealed that the nomogram model yielded a higher net benefit than the FF and MTR models.</div></div><div><h3>Conclusion</h3><div>The nomogram model, integrating qDixon, MTI, and clinical parameters, could serve as a reliable tool for diagnosing the individual risk for the osteoporosis in the elderly.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110320"},"PeriodicalIF":2.1,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142931943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of the glymphatic system in relapsing remitting multiple sclerosis by measuring the diffusion along the perivascular space","authors":"Máté Csomós ,&nbsp;Dániel Veréb ,&nbsp;Krisztián Kocsis ,&nbsp;Péter Faragó ,&nbsp;Eszter Tóth ,&nbsp;Szabolcs István Antal ,&nbsp;Bence Bozsik ,&nbsp;Bernadett Tuka ,&nbsp;András Király ,&nbsp;Nikoletta Szabó ,&nbsp;Zsigmond Tamás Kincses","doi":"10.1016/j.mri.2025.110319","DOIUrl":"10.1016/j.mri.2025.110319","url":null,"abstract":"<div><h3>Background</h3><div>In the inflammatory process of multiple sclerosis (MS) several toxic waste products are generated. The clearance of these products might depend on the glymphatic system; however, it's preserved function in MS is uncertain. Recently, it was suggested that this ‘waste clearance’ system can be examined by measuring the diffusion along the perivascular space (ALPS) index.</div></div><div><h3>Methods</h3><div>Reproducibility of the ALPS index was tested with intraclass correlation on two open-source datasets with two methods: calculating ALPS indices from the skeleton map (sk-ALPS) and via registration to the common space (ro-ALPS).</div><div>ALPS indices of 66 MS patient were calculated via the reorientation method. Spearman's correlation and partial least squares regression were applied to reveal the connection between the ALPS indices and the radiological (lesion count) and clinical parameters (SDMT, BVMT, CVLT, EDSS, disease duration) of the patients.</div></div><div><h3>Results</h3><div>Repeatability of the ALPS index calculated by the ro-ALPS method is the most reliable (ICC: 0.961). Significant correlation was found between the left ALPS index and SDMT. On the right side, significant correlation was found between the ALPS index and the number of periventricular lesions and black holes. The most important predictors of EDSS are disease duration, age, SDMT and infratentorial lesion count.</div></div><div><h3>Conclusion</h3><div>Reproducibility of the ALPS index ranges from ‘good’ to ‘excellent’. No relationship was found between the ALPS index and clinical disability. A lateralization was observed with cognitive characteristics on the left sided ALPS index and radiological characteristics on the right sided ALPS index.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110319"},"PeriodicalIF":2.1,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142932012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating glioma genetics through perfusion MRI: rCBV and rCBF as predictive biomarkers","authors":"Paulina Śledzińska-Bebyn ,&nbsp;Jacek Furtak ,&nbsp;Marek Bebyn ,&nbsp;Alicja Bartoszewska-Kubiak ,&nbsp;Zbigniew Serafin","doi":"10.1016/j.mri.2024.110318","DOIUrl":"10.1016/j.mri.2024.110318","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Brain tumors exhibit diverse genetic landscapes and hemodynamic properties, influencing diagnosis and treatment outcomes.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Purpose&lt;/h3&gt;&lt;div&gt;To explore the relationship between MRI perfusion metrics (rCBV, rCBF), genetic markers, and contrast enhancement patterns in gliomas, aiming to enhance diagnostic accuracy and inform personalized therapeutic strategies. Additionally, other radiological features, such as the T2/FLAIR mismatch sign, are evaluated for their predictive utility in &lt;em&gt;IDH&lt;/em&gt; mutations.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Study type&lt;/h3&gt;&lt;div&gt;Retrospective cohort study.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Population&lt;/h3&gt;&lt;div&gt;67 patients with brain tumors (including glioblastoma, astrocytoma, oligodendroglioma) undergoing surgical resection.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Field strength&lt;/h3&gt;&lt;div&gt;1.5 Tesla MRI, including T1 pre- and post-contrast, FLAIR, DWI, and DSC sequences.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Assessment&lt;/h3&gt;&lt;div&gt;Semiquantitative perfusion metrics (rCBV, rCBF) were evaluated against genetic markers (&lt;em&gt;IDH1&lt;/em&gt;, &lt;em&gt;EGFR&lt;/em&gt;, &lt;em&gt;CDKN2A&lt;/em&gt;, &lt;em&gt;PDGFRA&lt;/em&gt;, &lt;em&gt;MGMT&lt;/em&gt;, &lt;em&gt;TERT&lt;/em&gt;, 1p19q, &lt;em&gt;PTEN&lt;/em&gt;, &lt;em&gt;TP53&lt;/em&gt;, &lt;em&gt;H3F3A&lt;/em&gt;) through advanced MRI techniques. Contrast enhancement was assessed, and genetic alterations were confirmed via histopathological and molecular analyses.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Statistical tests&lt;/h3&gt;&lt;div&gt;Chi-square test, sensitivity, specificity, and ROC analysis for predictive modeling; significance level set at &lt;em&gt;p&lt;/em&gt; &lt; 0.05.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;Statistically significant differences in perfusion metrics were observed among tumors with distinct genetic profiles, with primary tumors and those harboring specific mutations (&lt;em&gt;IDH1&lt;/em&gt; wildtype, &lt;em&gt;EGFR&lt;/em&gt; amplification, &lt;em&gt;CDKN2A&lt;/em&gt; homozygous deletion, &lt;em&gt;PDGFRA&lt;/em&gt; amplification) showing higher perfusion values. A cut-off value of &lt;4 for rCBV in predicting &lt;em&gt;IDH1&lt;/em&gt; mutation yielded a sensitivity of 61.5 % and specificity of 82.1 %. For &lt;em&gt;CDKN2A&lt;/em&gt; deletion, a cut-off of &gt;5 resulted in a sensitivity of 75 % and specificity of 74.6 %, with an ROC value of 0.78.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Data conclusion&lt;/h3&gt;&lt;div&gt;Integrating perfusion MRI with genetic analysis offers a promising approach to improving the diagnostic and therapeutic landscape for brain tumors, indicating a substantial step toward personalized neuro-oncology. Additionally, findings like the T2/FLAIR mismatch sign highlight the potential for preoperative molecular predictions when biopsy is not feasible. These findings support further validation in larger, multi-institutional studies to solidify their role in clinical practice.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Data conclusion&lt;/h3&gt;&lt;div&gt;Integrating perfusion MRI with genetic analysis offers a promising approach to improving the diagnostic and therapeutic landscape for brain tumors, indicating a substantial step toward personalized neuro-oncology. These findings support further validation in larger, m","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110318"},"PeriodicalIF":2.1,"publicationDate":"2024-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142909852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of T1-weighted and augmented T1-weighted images of multi-parametric MR sequence in detecting neonatal punctate white matter lesions","authors":"Liangyu Ji ,&nbsp;Lingnan Kong ,&nbsp;Xuan Zhang ,&nbsp;Xiangxun Chen ,&nbsp;Chao Lu ,&nbsp;Feiyun Wu ,&nbsp;Ran Tang ,&nbsp;Meng Zhao","doi":"10.1016/j.mri.2024.110317","DOIUrl":"10.1016/j.mri.2024.110317","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Punctate White Matter Lesion (PWML) is common in neonates. Multi-parametric MR imaging with flexible design (MULTIPLEX, MTP) generates multiple contrasts requires only about 6 min for full-head coverage. This study aimed to evaluate the value of T1WI and aT1WI contrasts of MTP in detecting neonatal punctate white matter lesions.</div></div><div><h3>Materials and methods</h3><div>Twenty-one neonates with punctate white matter damage underwent multi-parametric MR imaging between November 2022 to July 2024. For subjective image quality, two pediatric neuroradiologists assessed overall image quality, and visualization of structures using a 4-point assessment scale. To analyze objective image quality, the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), contrast, number and sharpness of lesions were quantified.</div></div><div><h3>Results</h3><div>With regard to sharpness of the lesion, MTP T1WI and aT1WI are comparable to conventional T1W. For subjective assessment, MTP-T1WI exhibited superior overall image quality and anatomical structure display compared to conventional T1WI (<em>P</em> &lt; 0.01). Regarding objective assessment, MTP-T1WI had significantly higher SNR values for gray matter, white matter and lesions than the other two groups. The CNR values of MTP-T1WI and MTP-aT1WI of the white matter to lesion (WM-Lesion) were higher than conventional T1WI. The contrast of aT1WI surpassed that of the other two groups in WM-Lesion contrast. MTP-aT1W can detect more white matter lesions than conventional T1WI (conventional T1WI vs MTP-T1WI vs MTP-aT1WI,123 vs 165 vs 161).</div></div><div><h3>Conclusions</h3><div>The MTP-T1W and aT1W images can enhance lesion contrast and precisely delineate the extent and boundaries of the lesions, and could be more sensitive to PWML than conventional T1WI.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110317"},"PeriodicalIF":2.1,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142895752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of conventional diffusion-weighted imaging and multiplexed sensitivity-encoding combined with deep learning-based reconstruction in breast magnetic resonance imaging","authors":"Yitian Xiao ,&nbsp;Fan Yang ,&nbsp;Qiao Deng ,&nbsp;Yue Ming ,&nbsp;Lu Tang ,&nbsp;Shuting Yue ,&nbsp;Zheng Li ,&nbsp;Bo Zhang ,&nbsp;Huilou Liang ,&nbsp;Juan Huang ,&nbsp;Jiayu Sun","doi":"10.1016/j.mri.2024.110316","DOIUrl":"10.1016/j.mri.2024.110316","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the feasibility of multiplexed sensitivity-encoding (MUSE) with deep learning-based reconstruction (DLR) for breast imaging in comparison with conventional diffusion-weighted imaging (DWI) and MUSE alone.</div></div><div><h3>Methods</h3><div>This study was conducted using conventional single-shot DWI and MUSE data of female participants who underwent breast magnetic resonance imaging (MRI) from June to December 2023. The k-space data in MUSE were reconstructed using both conventional reconstruction and DLR. Two experienced radiologists conducted quantitative analyses of DWI, MUSE, and MUSE-DLR images by obtaining the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) of lesions and normal tissue and qualitative analyses by using a 5-point Likert scale to assess the image quality. Inter-reader agreement was assessed using the intraclass correlation coefficient (ICC). Image scores, SNR, CNR, and apparent diffusion coefficient (ADC) measurements among the three sequences were compared using the Friedman test, with significance defined at <em>P</em> &lt; 0.05.</div></div><div><h3>Results</h3><div>In evaluations of the images of 51 female participants using the three sequences, the two radiologists exhibited good agreement (ICC = 0.540–1.000, <em>P</em> &lt; 0.05). MUSE-DLR showed significantly better SNR than MUSE (<em>P</em> &lt; 0.001), while the ADC values within lesions and tissues did not differ significantly among the three sequences (<em>P</em> = 0.924, <em>P</em> = 0.636, respectively). In the subjective assessments, MUSE and MUSE-DLR scored significantly higher than conventional DWI in overall image quality, geometric distortion and axillary lymph node (<em>P</em> &lt; 0.001).</div></div><div><h3>Conclusion</h3><div>In comparison with conventional DWI, MUSE-DLR yielded improved image quality with only a slightly longer acquisition time.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110316"},"PeriodicalIF":2.1,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142882415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}