Magnetic resonance imaging最新文献

{"title":"Comparing single-shot EPI and 2D-navigated, multi-shot EPI diffusion tensor imaging acquisitions in the lumbar spinal cord at 3T","authors":"Alicia E. Cronin ,&nbsp;Anna Combes ,&nbsp;Lipika Narisetti ,&nbsp;Grace Sweeney ,&nbsp;Logan Prock ,&nbsp;Delaney Houston ,&nbsp;Caroline Seehorn ,&nbsp;Kurt G. Schilling ,&nbsp;Ryan K. Robison ,&nbsp;Seth A. Smith ,&nbsp;Kristin P. O'Grady","doi":"10.1016/j.mri.2025.110445","DOIUrl":"10.1016/j.mri.2025.110445","url":null,"abstract":"<div><div>Diffusion tensor imaging (DTI) can provide insights into spinal cord microstructure in health and disease; however, its application has been largely limited to cervical spinal segments using single-shot echo-planar imaging (EPI) diffusion-weighted MRI acquisitions. In this work, we evaluate a multi-shot EPI diffusion-weighted acquisition with reduced field-of-view (FOV) and 2D-navigated motion correction applied in the lumbar spinal cord of healthy volunteers, and compare image quality, geometric distortions, and quantitative DTI indices to those obtained with conventional, single-shot EPI diffusion-weighted MRI in a distinct, age/sex-matched healthy cohort. At 3 Tesla, 25 and 27 healthy participants were imaged using the single-shot and multi-shot EPI readouts with diffusion weighting, respectively, with matching resolution and comparable scan time. Seven participants underwent both diffusion acquisitions and were included in both cohorts. DTI indices were compared between the multi-shot and single-shot cohorts. Image signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) between gray and white matter, geometric distortions, and within-subject bias between the acquisitions were also assessed. The lumbar spinal cord diffusion indices derived from both cohorts were comparable to those in previous studies using single-shot EPI, though within-subject analysis demonstrated a systematic bias between the acquisitions in gray and white matter DTI measures, indicating these acquisitions are not interchangeable within a study. The multi-shot quantitative DTI maps demonstrated a significant reduction in image artifacts (i.e., distortions and blurring) and higher SNR and CNR compared to single-shot images. Overall, the reduced FOV, 2D-navigated, motion-corrected multi-shot acquisition demonstrated improved DTI quality metrics compared to single-shot, supporting its application for the lumbar spinal cord region.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"122 ","pages":"Article 110445"},"PeriodicalIF":2.1,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144239984","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":"Rapid 1 mm isotropic diffusion tensor imaging with denoising and improved parameter estimation for detecting focal hippocampal lesions in temporal lobe epilepsy","authors":"Pablo Stack-Sanchez ,&nbsp;Christian Beaulieu ,&nbsp;Donald W. Gross","doi":"10.1016/j.mri.2025.110443","DOIUrl":"10.1016/j.mri.2025.110443","url":null,"abstract":"<div><div>While high resolution diffusion tensor imaging (DTI) at 1 mm isotropic can detect focal lesions of the hippocampus in temporal lobe epilepsy (TLE), faster acquisition times would facilitate potential clinical implementation. The purpose here is to assess different published denoising algorithms to overcome the low signal-to-noise ratio and accelerate 1 mm isotropic DTI of the human hippocampus at 3 T while maintaining diffusivity metric accuracy and image quality for focal lesion detection in TLE. The previously published 5.5 min protocol of 110 diffusion images per slice (10 directions × 10 averages and 10 b = 0 s/mm²) was assessed for subsets of 1–10 averages (same 10 directions) that were denoised using four algorithms that have been applied to other diffusion MRI datasets. In healthy controls, the variance-stabilizing transformation and optimal singular-value manipulation (VST) and Non-Local Spatial and Angular Matching (NLSAM) denoising greatly improved image quality while minimizing voxels with spurious extremes of fractional anisotropy (FA) or mean diffusivity (MD) down to 4 averages (i.e. 40 diffusion images and 4 b = 0 s/mm²) in healthy controls. The identification of focal lesions indicated by elevated MD and alterations of internal micro-architecture with only 4 averages were comparable to the full data set of 10 averages. Therefore, denoising of 1 mm isotropic DTI of the hippocampus enables a clinically feasible scan time of 2.2 min at 3 T that can be used for the detection of focal hippocampal lesions in TLE, as well as other neurological disorders such as multiple sclerosis, dementia and Alzheimer's disease.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"122 ","pages":"Article 110443"},"PeriodicalIF":2.1,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212067","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":"Prediction of HER-2 expression status in breast cancer based on multi-parameter MRI intratumoral and peritumoral radiomics","authors":"Mingtai Cao ,&nbsp;Xinyi Liu ,&nbsp;Airu Yang ,&nbsp;Yuan Xu ,&nbsp;Qian Zhang ,&nbsp;Yuntai Cao","doi":"10.1016/j.mri.2025.110434","DOIUrl":"10.1016/j.mri.2025.110434","url":null,"abstract":"<div><h3>Background</h3><div>This study aims to explore the value of multiparametric magnetic resonance imaging (MRI) techniques—dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging (DWI), and T2-weighted fat-suppressed imaging (T2WI)—in predicting human epidermal growth factor receptor 2 (HER-2) status in breast cancer by integrating intratumoral and peritumoral radiomics features to establish a multiparametric MRI intratumoral and peritumoral radiomics model.</div></div><div><h3>Methods</h3><div>A retrospective cohort of 266 female breast cancer patients was analyzed. Patients from Center 1 (<em>n</em> = 199) were divided into a training set (<em>n</em> = 140) and internal validation set (<em>n</em> = 59; 7:3 ratio), while Center 2 (<em>n</em> = 67) provided the external test set. Using 3D Slicer, tumor boundaries were manually segmented on T2WI, DWI, and DCE-MRI to define intratumoral volumes of interest (VOIs). These VOIs were expanded by 3 mm to generate peritumoral regions (VOI_Peri3mm). Radiomics features were extracted from both regions, optimized via feature selection, and used to train eight random forest (RF) models. Performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).</div></div><div><h3>Results</h3><div>The multiparametric MRI intratumoral and peritumoral radiomics model (DWI_Peri3 + T2WI_Peri3 + DCE_Peri3_RF) demonstrated optimal HER-2 prediction, achieving area under the curve (AUC) values of 0.822 (95 % CI:0.755–0.889), 0.823 (0.714–0.932), and 0.813 (0.712–0.914) in the training, internal validation, and external test sets, respectively. It significantly outperformed single-parameter or single-region models and maintained cross-cohort consistency.</div></div><div><h3>Conclusion</h3><div>The intratumoral-peritumoral radiomics fusion model integrating DWI, T2WI, and DCE-MRI provides high diagnostic accuracy for HER-2 assessment, offering non-invasive biomarkers and enhancing precision in breast cancer management.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"122 ","pages":"Article 110434"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144216261","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":"Noise-robust foreground segmentation of multispectral imaging calibration volume in the presence of metallic implants for spectral range estimation in phantom and in-vivo data","authors":"Venkata Veerendranadh Chebrolu ,&nbsp;Mathias Nittka ,&nbsp;Constantin von Deuster ,&nbsp;Azadeh Sharafi ,&nbsp;Andrew Nencka ,&nbsp;Hollis G. Potter ,&nbsp;Kevin M. Koch","doi":"10.1016/j.mri.2025.110432","DOIUrl":"10.1016/j.mri.2025.110432","url":null,"abstract":"<div><h3>Purpose</h3><div>Multispectral Imaging (MSI) methods can use a calibration scan to estimate an off-resonance field-map to determine the spectral range required to cover off-resonant signal in the presence of metallic implants of various shape and composition. Background signal noise can corrupt the field-map estimation in this calibration process. Previous work on foreground segmentation used a cumulative distribution function (CDF) to remove signal extrema, which can remove regions of true off-resonance signal from the calibration analysis.</div><div>The purpose of this work is to develop a foreground segmentation method robust to background noise in both phantom and in-vivo data to support calibrating the spectral range needed for MSI acquisitions.</div></div><div><h3>Methods</h3><div>The proposed method uses information from individual spectral bins, rather than a composite bin-combined image, for segmentation. Ten phantom (seven with metal) and ten in-vivo (six with metal) data were acquired using a prototype MSI spectral calibration sequence. Field-maps were estimated and spectral range estimates from the unmasked field-map and the proposed method were computed and compared using a paired sample Wilcoxon signed-rank test.</div></div><div><h3>Results</h3><div>The proposed method achieved a noise-robust foreground segmentation in both phantom and in-vivo data, in the presence or absence of metal devices. The Wilcoxon test showed a statistically significant difference between the spectral range estimates from the unmasked field-map and proposed method for both the phantom and in-vivo data (<em>p</em>-value: 0.002).</div></div><div><h3>Conclusion</h3><div>Noise-robust foreground segmentation achieved by the proposed method can improve the accuracy and robustness of spectral range estimates for time-efficient and reduced artifact multispectral imaging.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"122 ","pages":"Article 110432"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213197","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":"Longitudinal quantitative MRI in prostate cancer after radiation therapy with and without androgen deprivation therapy","authors":"Yu-Feng Wang ,&nbsp;Sirisha Tadimalla ,&nbsp;Niluja Thiruthaneeswaran ,&nbsp;Lois Holloway ,&nbsp;Sandra Turner ,&nbsp;Amy Hayden ,&nbsp;Mark Sidhom ,&nbsp;Jarad Martin ,&nbsp;Annette Haworth","doi":"10.1016/j.mri.2025.110431","DOIUrl":"10.1016/j.mri.2025.110431","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Early detection of locally recurring prostate cancer (PCa) after definitive radiation therapy (RT) offers the opportunity to deliver targeted salvage therapies, thereby reducing the risk of disease progression. Quantitative MRI (qMRI) parameters show promise as imaging biomarkers for early detection of local recurrence. However, the feasibility of using qMRI for response monitoring in patients undergoing RT combined with androgen deprivation therapy (ADT) remains uncertain. Here, we identified the qMRI parameters with potential to reliably detect post-RT response in PCa and compared the response in patients receiving RT combined with ADT versus those receiving RT alone.</div></div><div><h3>Materials and methods</h3><div>qMRI scans were acquired before and at 6-, 12-, and 18-months after standard definitive RT in sixteen patients with localised PCa. Patients undergoing neoadjuvant ADT were also scanned pre-ADT. Mean value of ADC, D, f, HS, R2*, T1, K^trans, v_e within the tumour were calculated at each imaging timepoint. Statistical significance of treatment-related changes was assessed using rANOVA and post hoc two-tailed <em>t</em>-test. Changes relative to the baseline scan exceeding the parameter uncertainty were classified as “detectable”.</div></div><div><h3>Results</h3><div>K^trans and HS measured at 18-months post-RT were found to be most reliable for detecting treatment response regardless of ADT use. Significant post-treatment changes were observed in other qMRI parameters but were unreliable due to large measurement uncertainties.</div></div><div><h3>Conclusions</h3><div>Quantitative MRI show promise for reliably detecting treatment response within 18-months post-RT. Future clinical trials should validate the potential of K^trans and HS by correlating these parameters with treatment outcomes.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"122 ","pages":"Article 110431"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205713","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":"Comprehensive characterization of tumor therapeutic response via simultaneous mapping of cell size, density, and transcytolemmal water exchange","authors":"Diwei Shi ,&nbsp;Xiaoxia Wang ,&nbsp;Sisi Li ,&nbsp;Fan Liu ,&nbsp;Xiaoyu Jiang ,&nbsp;Li Chen ,&nbsp;Jiuquan Zhang ,&nbsp;Hua Guo ,&nbsp;Junzhong Xu","doi":"10.1016/j.mri.2025.110433","DOIUrl":"10.1016/j.mri.2025.110433","url":null,"abstract":"<div><div>The evaluation of tumor response to neoadjuvant chemotherapy is critical for the personalized management of cancer patients, aiming to minimize unnecessary toxicity, costs, and treatment delays. Current imaging techniques primarily depend on detecting tumor volume changes, which reflect downstream effects. In contrast, advanced microstructural diffusion MRI (dMRI) methods offer cellular-level insights but are limited by biased estimates of cell density due to oversimplified biophysical models. We present a novel dMRI-based approach, EXCHANGE, which incorporates transcytolemmal water exchange into a quantitative multi-compartmental biophysical model. This method enables simultaneous mapping of cell size, density, and transcytolemmal water exchange, providing a comprehensive characterization of tumor microstructure. Validation through computer simulations and in vitro studies demonstrated the good accuracy of EXCHANGE-derived metrics. In a proof-of-concept study, EXCHANGE was applied to animal models and patients with triple-negative breast cancer, showcasing its potential to evaluate tumor therapeutic response to neoadjuvant chemotherapy. EXCHANGE offers a unique capability to characterize tumor microstructural properties at the cellular level, paving the way for improved monitoring of treatment response in clinical settings.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"122 ","pages":"Article 110433"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144216260","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":"Correlation of pathologic features and prognostic factors in rectal adenocarcinoma based on APT imaging and IVIM-DWI histogram","authors":"Shiji Kan ,&nbsp;Yongwen Sun ,&nbsp;Kai Ai ,&nbsp;Yong Xia ,&nbsp;Bo Gao","doi":"10.1016/j.mri.2025.110430","DOIUrl":"10.1016/j.mri.2025.110430","url":null,"abstract":"<div><h3>Objective</h3><div>This study aimed to evaluate the effectiveness of amide proton transfer (APT) imaging and intravoxel incoherent motion (IVIM) histogram parameters in predicting pathologic prognostic factors (lymph node metastasis and vascular invasion) in rectal adenocarcinoma. Additionally, the study compared the diagnostic performance of these parameters through combined models.</div></div><div><h3>Methods</h3><div>This study enrolled 42 patients with rectal adenocarcinoma proved by pathology. The APT signal intensity (APTSI) of primary rectal cancer was measured. The IVIM images were postprocessed to generate quantitative parameter maps of pure diffusioncoefficient(D), pseudo-diffusion coefficient(D*), perfusion fraction(f). The histogram analysis was performed to obtain the minimum, maximum, mean, standard deviation, variance, median, 10th and 90th percentiles (10th and 90th henceforth), skewness, kurtosis, and entropy of each parameter.The postoperative pathologic results included T stage, lymph node N stage, and peripheral nerve and lymphovascular invasion.</div></div><div><h3>Results</h3><div>The histogram of D and D* were statistically significant between with and without lymph node metastasis (LNM) (<em>P</em> &lt; .05). The histogram of D value was significant between with lymphovascular invasion or not (<em>P</em> &lt; .05). No clear difference was noted between APTSI and prognostic factors of rectal adenocarcinoma (<em>P</em> &gt; .05). The area under the curve (AUC) of the combined model combining 90th, kurtosis, entropy, and D* maximum value for diagnosing LNM of rectal adenocarcinoma was 0.796. The AUC value of the combined model combining the mean, median, 10th and 90th, skewness, kurtosis, and entropy of D value in diagnosing the presence or absence of lymphovascular invasion of rectal adenocarcinoma was 0.821.</div></div><div><h3>Conclusions</h3><div>IVIM histogram parameters (e.g., 90th percentile of <em>D</em>, kurtosis, and entropy) displayed significant diagnostic value for detecting LNM and vascular invasion in rectal adenocarcinoma. In contrast, APTSI showed no significant correlation with these prognostic factors. These findings underscore the potential of IVIM imaging as a noninvasive tool for preoperative risk stratification in patients with rectal adenocarcinoma.</div></div><div><h3>Key points</h3><div>IVIM histogram parameters help distinguish LNM and lymphovascular invasion in rectal adenocarcinoma. No obvious difference was observed in APTSI between patients with rectal adenocarcinoma with various TN stages, peripheral nerve invasion, and vascular invasion.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"122 ","pages":"Article 110430"},"PeriodicalIF":2.1,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144208883","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":"Fully automated measurement of aortic pulse wave velocity from routine cardiac MRI studies","authors":"Yue Jiang ,&nbsp;Tina Yao ,&nbsp;Nikhil Paliwal ,&nbsp;Daniel Knight ,&nbsp;Karan Punjabi ,&nbsp;Jennifer Steeden ,&nbsp;Alun D. Hughes ,&nbsp;Vivek Muthurangu ,&nbsp;Rhodri Davies","doi":"10.1016/j.mri.2025.110442","DOIUrl":"10.1016/j.mri.2025.110442","url":null,"abstract":"<div><h3>Introduction</h3><div>Aortic pulse wave velocity (PWV) is a prognostic biomarker for cardiovascular disease, which can be measured by dividing the aortic path length by the pulse transit time. However, current MRI techniques require special sequences and time-consuming manual analysis. We aimed to fully automate the process using deep learning to measure PWV from standard sequences, facilitating PWV measurement in routine clinical and research scans.</div></div><div><h3>Methods</h3><div>A deep learning (DL) model was developed to generate high-resolution 3D aortic segmentations from routine 2D trans-axial SSFP localizer images, and the centerlines of the resulting segmentations were used to estimate the aortic path length. A further DL model was built to automatically segment the ascending and descending aorta in phase contrast images, and pulse transit time was estimated from the sampled flow curves. Quantitative comparison with trained observers was performed for path length, aortic flow segmentation and transit time, either using an external clinical dataset with both localizers and paired 3D images acquired or on a sample of UK Biobank subjects. Potential application to clinical research scans was evaluated on 1053 subjects from the UK Biobank.</div></div><div><h3>Results</h3><div>Aortic path length measurement was accurate with no major difference between the proposed method (125 ± 19 mm) and manual measurement by a trained observer (124 ± 19 mm) (<em>P</em> = 0.88). Automated phase contrast image segmentation was similar to that of a trained observer for both the ascending (Dice vs manual: 0.96) and descending (Dice 0.89) aorta with no major difference in transit time estimation (proposed method = 21 ± 9 ms, manual = 22 ± 9 ms; <em>P</em> = 0.15). 966 of 1053 (92 %) UK Biobank subjects were successfully analyzed, with a median PWV of 6.8 m/s, increasing 27 % per decade of age and 6.5 % higher per 10 mmHg higher systolic blood pressure.</div></div><div><h3>Conclusions</h3><div>We describe a fully automated method for measuring PWV from standard cardiac MRI localizers and a single phase contrast imaging plane. The method is robust and can be applied to routine clinical scans, and could unlock the potential of measuring PWV in large-scale clinical and population studies. All models and deployment codes are available online.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"122 ","pages":"Article 110442"},"PeriodicalIF":2.1,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144199526","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":"MATI: A GPU-accelerated toolbox for microstructural diffusion MRI simulation and data fitting with a graphical user interface","authors":"Junzhong Xu ,&nbsp;Sean P. Devan ,&nbsp;Diwei Shi ,&nbsp;Adithya Pamulaparthi ,&nbsp;Nicholas Yan ,&nbsp;Zhongliang Zu ,&nbsp;David S. Smith ,&nbsp;Kevin D. Harkins ,&nbsp;John C. Gore ,&nbsp;Xiaoyu Jiang","doi":"10.1016/j.mri.2025.110428","DOIUrl":"10.1016/j.mri.2025.110428","url":null,"abstract":"<div><h3>Purpose</h3><div>To introduce MATI (Microstructural Analysis Toolbox for Imaging), a versatile MATLAB-based toolbox that combines both simulation and data fitting capabilities for microstructural dMRI research.</div></div><div><h3>Methods</h3><div>MATI provides a user-friendly, graphical user interface that enables researchers, including those without much programming experience, to perform advanced simulations and data analyses for microstructural MRI research. For simulation, MATI supports arbitrary microstructural tissues and pulse sequences. For data fitting, MATI supports a range of fitting methods, including traditional non-linear least squares, Bayesian approaches, machine learning, and dictionary matching methods, allowing users to tailor analyses based on specific research needs.</div></div><div><h3>Results</h3><div>Optimized with vectorized matrix operations and high-performance numerical libraries, MATI achieves high computational efficiency, enabling rapid simulations and data fitting on CPU and GPU hardware. While designed for microstructural dMRI, MATI's generalized framework can be extended to other imaging methods, making it a flexible and scalable tool for quantitative MRI research.</div></div><div><h3>Conclusion</h3><div>MATI offers a significant step toward translating advanced microstructural MRI techniques into clinical applications.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"122 ","pages":"Article 110428"},"PeriodicalIF":2.1,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144151125","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":"MRI-based habitat analysis for Intratumoral heterogeneity quantification combined with deep learning for HER2 status prediction in breast cancer","authors":"Qing-Yu Li,&nbsp;Yue Liang,&nbsp;Lan Zhang,&nbsp;Jia-Hao Li,&nbsp;Bin-Jie Wang,&nbsp;Chang-Fu Wang","doi":"10.1016/j.mri.2025.110429","DOIUrl":"10.1016/j.mri.2025.110429","url":null,"abstract":"<div><h3>Background</h3><div>Human epidermal growth factor receptor 2 (HER2) is a crucial determinant of breast cancer prognosis and treatment options. The study aimed to establish an MRI-based habitat model to quantify intratumoral heterogeneity (ITH) and evaluate its potential in predicting HER2 expression status.</div></div><div><h3>Methods</h3><div>Data from 340 patients with pathologically confirmed invasive breast cancer were retrospectively analyzed. Two tasks were designed for this study: Task 1 distinguished between HER2-positive and HER2-negative breast cancer. Task 2 distinguished between HER2-low and HER2-zero breast cancer. We developed the ITH, deep learning (DL), and radiomics signatures based on the features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Clinical independent predictors were determined by multivariable logistic regression. Finally, a combined model was constructed by integrating the clinical independent predictors, ITH signature, and DL signature. The area under the receiver operating characteristic curve (AUC) served as the standard for assessing the performance of models.</div></div><div><h3>Results</h3><div>In task 1, the ITH signature performed well in the training set (AUC = 0.855) and the validation set (AUC = 0.842). In task 2, the AUCs of the ITH signature were 0.844 and 0.840, respectively, which still showed good prediction performance. In the validation sets of both tasks, the combined model exhibited the best prediction performance, with AUCs of 0.912 and 0.917 respectively, making it the optimal model.</div></div><div><h3>Conclusion</h3><div>A combined model integrating clinical independent predictors, ITH signature, and DL signature can predict HER2 expression status preoperatively and noninvasively.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"122 ","pages":"Article 110429"},"PeriodicalIF":2.1,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144143172","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}