Magnetic resonance imaging最新文献

{"title":"CvTFuse: An unsupervised medical image fusion method of gliomas T1-DWI mode","authors":"Qianjia Huang ,&nbsp;Wei Chen ,&nbsp;Jiahui Zeng ,&nbsp;Jiangyi Ding ,&nbsp;Kai Xie ,&nbsp;Nannan Cao ,&nbsp;Kangkang Sun ,&nbsp;Zhuqing Jiao ,&nbsp;Jing Cai ,&nbsp;Xinye Ni","doi":"10.1016/j.mri.2026.110618","DOIUrl":"10.1016/j.mri.2026.110618","url":null,"abstract":"<div><h3>Background</h3><div>DWI can provide microscopic information on the diffusion of water molecules, whereas T1WI can provide high-resolution anatomical and histological information.</div></div><div><h3>Purpose</h3><div>Accurately and effectively fusing different MRI modalities can precisely localize lesion areas and provide rich information for analyzing the nature of lesions.</div></div><div><h3>Methods</h3><div>We propose a dual-branch medical image fusion network that combines convolutional neural network (CNN) and vision transformer (CvTFuse). CvTFuse consists of three parts: encoder, fusion layer, and decoder. The encoder is divided into a CNN module and a transformer module, which are used to extract local and global features of the source image. To completely capture the contextual information of the image, a global context aggregation module (GCAM) is proposed, which aggregates multi-scale features extracted from the transformer branch to improve the quality of the fused image. The fusion layer employs an energy-aware and gradient-enhanced fusion strategy to help retain the details in the source images for feature fusion of different MRI modalities. The decoder consists of five convolutional layers and two skip connections to reconstruct the fused features.</div></div><div><h3>Results</h3><div>Qualitative results showed that this method presented clear texture details and sharp boundaries, preserving the salient information of the source images to the greatest extent. Quantitative results indicated that the method achieved average gradient, information entropy, mutual information, and visual saliency of 4.5975, 4.9073, 2.5181, and 0.77, respectively. Qualitative and quantitative results demonstrated that compared with deep learning fusion methods such as DenseFuse, RFN-Nest, MSDNet, IFCNN, CDDFuse, and SwinFusion, this method maintained gradient information, texture information, and edge details very well, while also minimizing information loss and reducing distortion.</div></div><div><h3>Conclusion</h3><div>This method can combine information from different modalities of MR images, allowing for accurate localization of lesion areas. It also utilizes rich clinical information to aid in the precise diagnosis and formulation of treatment plans.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"128 ","pages":"Article 110618"},"PeriodicalIF":2.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145994312","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":"Imaging evidence of diabetic pancreatopathy: An exploratory IVIM MRI study","authors":"Boyu Liu ,&nbsp;Feng Huang ,&nbsp;Peng Liu ,&nbsp;Xi Wu ,&nbsp;Dandan Zhou ,&nbsp;Siyou Yi ,&nbsp;Qianbiao Gu","doi":"10.1016/j.mri.2026.110610","DOIUrl":"10.1016/j.mri.2026.110610","url":null,"abstract":"<div><h3>Background</h3><div>To investigate pancreatic microstructural alterations in type 2 diabetes mellitus (T2DM) using intravoxel incoherent motion (IVIM) MRI.</div></div><div><h3>Methods</h3><div>This cross-sectional study enrolled 152 participants (72 T2DM, 80 controls). IVIM-derived parameters (perfusion fraction F, diffusion coefficient D) were quantified in pancreatic head, body, and tail. Group comparisons utilized multivariable logistic regression adjusted for confounders; disease duration correlation was assessed via Pearson's test.</div></div><div><h3>Results</h3><div>T2DM patients exhibited significantly elevated perfusion fraction F in the pancreatic head (adjusted OR = 1.06, <em>P</em> = 0.015). Conversely, the pancreatic tail demonstrated a nominally reduced pure diffusion coefficient (D_p) across models (adjusted OR = 0.99, <em>P</em> = 0.028), indicating microstructural damage. Longitudinal analysis revealed that tail F positively correlated with T2DM duration (<em>r</em> = 0.39, <em>P</em> &lt; 0.01), while tail D_p negatively correlated (<em>r</em> = −0.25, <em>P</em> = 0.05), suggesting progressive tail-specific pathology.</div></div><div><h3>Conclusion</h3><div>This exploratory IVIM MRI study suggests a spatial heterogeneity in diabetic pancreatopathy, characterized by compensatory perfusion changes in the pancreatic head alongside progressive microstructural alterations in the tail. Pancreatic-tail IVIM parameters, particularly perfusion fraction F which correlated with disease duration, may represent candidate imaging biomarkers for diabetic pancreatic injury, warranting further validation in larger cohorts.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"128 ","pages":"Article 110610"},"PeriodicalIF":2.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145900705","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":"The effect of prolonged glucose infusion on resting-state fMRI signal fluctuations at 7 T","authors":"N. Ahmadian ,&nbsp;Evita C. Wiegers ,&nbsp;Wybe van der Kemp ,&nbsp;Ellen van Hulst ,&nbsp;Corin Miller ,&nbsp;Sarah Hsieh ,&nbsp;An Bautmans ,&nbsp;Kristien Van Dyck ,&nbsp;Shawn Kim ,&nbsp;Ilias Triantafyllou ,&nbsp;Pieter van Eijsden ,&nbsp;Dennis W. Klomp ,&nbsp;Natalia Petridou ,&nbsp;Alex A. Bhogal","doi":"10.1016/j.mri.2026.110611","DOIUrl":"10.1016/j.mri.2026.110611","url":null,"abstract":"<div><div>The aim of this study was to examine the effect of 2-h glucose infusion after an overnight fast on relative resting-state fMRI signal fluctuations. Our hypothesis was that neuronal glucose metabolism would be affected, and that this would lead to changes in signal power at specific frequency bands. Resting state fMRI (rsfMRI) scans were acquired in 9 subjects pre- and post-glucose ∼2 h infusion. Fourier spectral analysis was performed using signals localized in the prefrontal cortex. Power spectra were calculated and statistical analysis was performed targeting the full spectral range using both a cluster-based and Wilcoxon signed rank test. For both tests, post-infusion signal power was significantly higher between the 0.015–0.1 Hz range. Moreover we observed differences at higher frequencies, normally attributed to cardiac or respiratory related signals. Our findings suggest that prolonged glucose infusion post-fasting may significantly modulate rsfMRI signal fluctuations in the prefrontal cortex that may be related to metabolic responses to glucose infusion. Future studies should investigate the dynamic effects of sustained glucose infusion using continuous fMRI, exploring implications for brain function and metabolic disorders like diabetes.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"128 ","pages":"Article 110611"},"PeriodicalIF":2.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145952401","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":"Tumor microstructural biomarkers from time-dependent diffusion MRI for predicting IDH mutation status in non-enhancing gliomas","authors":"Yu Han ,&nbsp;Zhuomin Lyu ,&nbsp;Yibin Xi ,&nbsp;Yuyao Wang ,&nbsp;Jin Zhang","doi":"10.1016/j.mri.2026.110620","DOIUrl":"10.1016/j.mri.2026.110620","url":null,"abstract":"<div><h3>Purpose</h3><div>To explore the value of microstructural parameters derived from time-dependent diffusion MRI (TDD-MRI) in predicting isocitrate dehydrogenase (IDH) mutation status in non-enhancing gliomas.</div></div><div><h3>Methods</h3><div>A total of 112 patients with non-enhancing gliomas, comprising 90 IDH-mutant and 22 IDH-wildtype gliomas, were enrolled retrospectively. Seven microstructural features were calculated from TDD-MRI. After selecting features via least absolute shrinkage and selection operator regression and Boruta algorithm, Firth's logistic regression was applied to develop three predictive models, including quantitative model, clinical model, and integrative model. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. A 5-fold cross-validation and bootstrap were conducted to provide a stable estimate of model performance.</div></div><div><h3>Results</h3><div>Among all microstructural parameters, cellularity exhibited the most favorable diagnostic efficacy, achieving an AUC of 0.758 and an accuracy of 0.830. The quantitative model outperformed single microstructural parameters with an AUC of 0.828 and an accuracy of 0.857, whereas the clinical model exhibited an AUC of 0.864 and an accuracy of 0.866. The integrative model exhibited the highest diagnostic efficacy, with an AUC of 0.903 and an accuracy of 0.884. For stability, the quantitative, clinical and integrative models yielded 5-fold cross-validation AUCs of 0.836, 0.883 and 0.908, and bootstrap AUCs of 0.821, 0.862 and 0.893, respectively.</div></div><div><h3>Conclusion</h3><div>TDD-MRI-derived cellularity is the most potent microstructural predictor of IDH status in non-enhancing gliomas. The integrative model achieved superior diagnostic performance, providing a robust and clinically translatable framework for non-invasive glioma genotyping.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"128 ","pages":"Article 110620"},"PeriodicalIF":2.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146030097","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":"Assessment of a truncation-based R2* fitting technique for quantifying high liver iron concentration (LIC)","authors":"Marshall S. Sussman ,&nbsp;Bryan Buckley ,&nbsp;Stephan A.R. Kannengiesser ,&nbsp;Kartik S. Jhaveri","doi":"10.1016/j.mri.2026.110609","DOIUrl":"10.1016/j.mri.2026.110609","url":null,"abstract":"<div><h3>Background</h3><div>R2*-based MRI liver iron concentration (LIC) quantification has several advantages. However, lower accuracies can impact utility in high iron overload states.</div></div><div><h3>Purpose</h3><div>To develop and characterize an R2* fitting technique based on truncation. Unlike other truncation methods, this approach maximizes the accuracy of LIC estimates at the possible cost of precision, rather than the converse. The hypothesis of this study is that the truncation-based method can provide accurate LIC estimates over the full range of LICs characterized by FerriScan.</div></div><div><h3>Study type: Retrospective subjects</h3><div>170 patients undergoing chelation treatment for iron overload had undergone FerriScan and R2* MRI during the same exam.</div></div><div><h3>Field strength/sequence</h3><div>Field strength was 1.5 T. Pulse sequences included 2D multi-slice spin-echo for FerriScan, and 3D 5-echo gradient echo for R2* mapping.</div></div><div><h3>Assessment</h3><div>LIC was calculated for both non- and truncated R2* fits using a previously-derived calibration curve. The resulting LIC values were compared to those from the reference FerriScan technique. Precision was also assessed for LIC estimates.</div></div><div><h3>Statistical tests</h3><div>LIC values were compared using a Wilcoxon signed rank test. Statistical significance was set at the 95 % confidence level.</div></div><div><h3>Results</h3><div>LIC estimates from truncated R2* fits were equal to FerriScan at all LIC values, while those from non-truncated fits deviated for LIC &gt; 28 mg Fe / g dw liver. The precision of the truncated fits was better than the non-truncated fits.</div></div><div><h3>Data conclusion</h3><div>The truncation R2*-fitting method developed in this study permits accurate characterization of LIC over the full range of LICs characterized by FerriScan.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"128 ","pages":"Article 110609"},"PeriodicalIF":2.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145906311","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":"DeepVBM: A fully automatic and efficient voxel-based morphometry via deep learning-based segmentation and registration methods","authors":"Pei-Mao Sun ,&nbsp;Teng-Yi Huang ,&nbsp;Tzu-Chao Chuang ,&nbsp;Yi-Ru Lin ,&nbsp;Hsiao-Wen Chung ,&nbsp;for the Alzheimer's Disease Neuroimaging Initiative","doi":"10.1016/j.mri.2026.110637","DOIUrl":"10.1016/j.mri.2026.110637","url":null,"abstract":"<div><div>Voxel-based morphometry (VBM) using T1-weighted magnetic resonance imaging is a pivotal tool for assessing brain structure and identifying subtle morphological changes associated with various neurological conditions. Conventional VBM workflows, however, face significant computational challenges, particularly during the nonlinear deformable registration stage, which impedes analysis of large-scale neuroimaging databases. In this study, we introduce FuseMorph, a deep learning-based registration method that refines initial zero-shot predictions from a pretrained model via iterative inference and targeted parameter search. By eliminating the need for full backpropagation and additional model retraining, FuseMorph significantly reduces computational demands, achieving registration accuracy comparable to state-of-the-art methods even in CPU-only environments. FuseMorph is integrated into DeepVBM, a fully automated VBM pipeline streamlines the processing of high-resolution MRI datasets and substantially reduces computation time compared to traditional pipelines, thereby facilitating the efficient analysis of large multi-center studies. The proposed approach was validated on multiple datasets, including an Alzheimer's disease cohort where DeepVBM successfully detected characteristic patterns of gray matter atrophy in regions such as the hippocampus, entorhinal cortex, and amygdala. These findings not only underscore the clinical relevance of the method but also demonstrate its potential for early detection and monitoring of neurodegenerative changes. This work contributes an accessible, efficient, and scalable solution for neuroimaging research, with potential applications extending to various neurodegenerative disease studies.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"128 ","pages":"Article 110637"},"PeriodicalIF":2.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146119378","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":"MR cytometry: More effective than conventional diffusion MRI in differentiating benign and malignant musculoskeletal tumors","authors":"Zhanxing Yan ,&nbsp;Jian Zhao ,&nbsp;Jianling Cui ,&nbsp;Lisha Duan ,&nbsp;Xiaohan Feng ,&nbsp;Xinying Zhang ,&nbsp;Shengnan Zhang ,&nbsp;Wenhua Liang ,&nbsp;Xiaohui Cao ,&nbsp;Hong Yu","doi":"10.1016/j.mri.2025.110603","DOIUrl":"10.1016/j.mri.2025.110603","url":null,"abstract":"<div><h3>Objectives</h3><div>To investigate the clinical significance of microstructure imaging based on MR cytometry in the qualitative diagnosis of musculoskeletal tumors (MSTs).</div></div><div><h3>Materials and methods</h3><div>Participants with clinically suspected MSTs between March 2024 and March 2025 were prospectively enrolled. Conventional apparent diffusion coefficient (ADC) value and four microstructural parameters, including cell diameter (<em>d</em>), intracellular volume fraction (<em>v</em>_in), Cellularity index, and extracellular diffusivity (<em>D</em>_ex), were estimated using the IMPULSED method (a form of MR cytometry). The ADC_PGSE, ADC_{25 Hz} and ADC_{40 Hz} were measured at three different effective diffusion times, and the change ADC (cADC) and relative change ADC (rcADC) were measured. The performance was evaluated using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test.</div></div><div><h3>Results</h3><div>A total of 62 participants with benign and malignant MSTs (mean age, 47.7 ± 20.9 [SD] years; 21 women, 41men) were enrolled. Among these ADC parameters, rcADC had the highest AUC (AUC = 0.737; 95 %Cl: 0.600, 0.874; <em>p</em> = 0.002). Among the four microstructure parameters derived from MR cytometry, Cellularity index had the highest AUC (AUC = 0.735; 95 %Cl: 0.601, 0.868; <em>p</em> = 0.002). Among all parameters, the AUC of rcADC and Cellularity index is higher than that of conventional ADC (AUC = 0.728; 95 %Cl: 0.594, 0.863; <em>p</em> = 0.003). The combination of the four microstructure parameters of MR cytometry further improved the diagnostic performance (AUC = 0.758; 95 %Cl: 0.626, 0.890; <em>p</em> = 0.001).</div></div><div><h3>Conclusion</h3><div>MR cytometry was an effective method for helping to predict the benign and malignant MSTs and is superior to conventional ADC values.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110603"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145844082","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":"Microscopic Propagator Imaging with diffusion MRI","authors":"Tommaso Zajac ,&nbsp;Gloria Menegaz ,&nbsp;Marco Pizzolato","doi":"10.1016/j.mri.2025.110607","DOIUrl":"10.1016/j.mri.2025.110607","url":null,"abstract":"<div><div>Microscopic Propagator Imaging (MPI) is a novel diffusion MRI technique that estimates properties, referred to as indices, of the microscopic propagator. This is the probability distribution of water displacements within tissue microstructures. Unlike conventional mean apparent propagator methods, MPI is designed to minimize the sensitivity of indices to mesoscopic confounds such as axonal orientation dispersion, yielding diagnostic maps that more directly reflect presence, integrity, and shape of microstructures rather than their directional arrangement within the voxel. The method is implemented as a machine learning framework that exploits zonal relationships among spherical harmonic coefficients of multi-shell diffusion data to map such data to the microscopic propagator indices. Applied to human brain data, MPI yields reliable voxelwise estimates, with resulting maps exhibiting expected spatial patterns and systematic differences relative to the corresponding mean apparent propagator indices. These findings suggest that MPI provides microscopic-specific and complementary information beyond classical propagator methods, with potential to improve the characterization of brain tissue microstructure.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110607"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145878672","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":"APTWI-differential analysis for breast cancer: Association with histopathologic characteristics and early prediction of neoadjuvant chemotherapy response","authors":"Jie Fang ,&nbsp;Xiaoxia Wang ,&nbsp;Lu Wang ,&nbsp;Ying Cao ,&nbsp;Yao Huang ,&nbsp;Shuling Liu ,&nbsp;Huifang Chen ,&nbsp;Zhechuan Dai ,&nbsp;Tao Yu ,&nbsp;Sun Tang ,&nbsp;Meng Lin ,&nbsp;Yi Zhang ,&nbsp;Jiuquan Zhang","doi":"10.1016/j.mri.2025.110590","DOIUrl":"10.1016/j.mri.2025.110590","url":null,"abstract":"<div><div>The purpose of the study is to investigate the value of Amide proton transfer imaging(APTWI)-differential analysis in association with histopathologic characteristics, and the performance to early predict pathologic complete response (pCR) in participants with breast cancer (BC). Participants with BC who underwent pretreatment APTWI between November 2022 and April 2024 were prospectively enrolled. APT-specific signal quantification was achieved through differential analysis between model-fitted and experimentally acquired <em>Z</em>-spectrum at +3.5 ppm. Univariate analysis was used to identify APT^# values associated with histopathologic characteristics and pCR. The area under the receiver operating characteristic curve (AUC) analysis was performed to evaluate the diagnostic value of APTWI-DIGITAL on histopathologic characteristics and assess the predictive performance for pCR. The analysis ultimately included 123 participants with BC (mean age, 52 years±9 [SD]), 43 participants of whom received neoadjuvant chemotherapy (NAC) and 15 participants who achieved pCR. In the pre-treatment group, the APT^# values showed reasonable performance in identifying the positive status of KI67 proliferation index (<em>P</em> = 0.01, AUC = 0.69), and PR (<em>P</em> = 0.045, AUC = 0.60). In the NAC group, the APT^# values in the pCR participants showed a significant downward trend at the T1 (<em>P</em> = 0.01, AUC = 0.80), and was not significant between pCR and non-pCR at other timepoints. The findings suggest APTWI-differential analysis may be useful imaging biomarkers to characterize the immunohistochemical biomarkers and predict pCR to NAC in BC patients.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110590"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145756795","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":"Accuracy and precision of random walk with barrier model fitting: Simulations and applications in head and neck cancers","authors":"Jiaren Zou ,&nbsp;Yue Cao","doi":"10.1016/j.mri.2025.110597","DOIUrl":"10.1016/j.mri.2025.110597","url":null,"abstract":"<div><div>Random Walk with Barrier Model (RWBM) can quantify microstructural parameters (free diffusivity, cell size, and membrane permeability) in head and neck cancers (HNCs) using time-dependent diffusion MRI. However, model fitting remains challenging due to limited number of measurements, low signal-to-noise ratio (SNR) and complex nonlinear biophysical models. In this work, we numerically evaluated RWBM fitting performance under a clinically feasible imaging protocol by comparing fits to the short-time-limit expression (STL-RWBM) and to the general RWBM across a broad range of SNRs. We also examined the model fitting degeneracy and fitting landscape for both fitting methods to elucidate their fitting behaviors. Numerical findings were further compared with model-fitting results from HNC patient data. We observed that under clinically relevant SNRs, fitting the STL-RWBM provided low-variance estimates of free diffusivity and membrane permeability, with biases comparable to those obtained from fitting the general RWBM. However, the general RWBM yielded low-bias estimates for cell size. The flat and tube-like fitting landscapes of the general RWBM led to high variance, frequent convergence to boundary constraints and spurious correlation among fitted parameters in vivo. The more restricted fitting landscape of the STL-RWBM substantially reduced variance. Both fitting methods produced cell size estimates in HNCs that were consistent with prior pathological findings. In conclusion, this work provided a comprehensive analysis of RWBM fitting in clinical settings and may guide optimizations of data acquisition and model fitting methods.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110597"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145781614","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}