Magnetic resonance imaging最新文献

{"title":"Monte Carlo simulations of transverse relaxation for characterization of physicochemical properties of superparamagnetic iron oxide nanoparticles","authors":"Lauritz Klünder ,&nbsp;Bastian Maus ,&nbsp;María Belén Rivas Aiello ,&nbsp;Athina Drakonaki ,&nbsp;Michael Holtkamp ,&nbsp;Uwe Karst ,&nbsp;Christos Gatsogiannis ,&nbsp;Cristian Strassert ,&nbsp;Cornelius Faber","doi":"10.1016/j.mri.2025.110553","DOIUrl":"10.1016/j.mri.2025.110553","url":null,"abstract":"<div><div>Superparamagnetic iron oxide nanoparticles are used in MRI as <span><math><msub><mi>T</mi><mn>2</mn></msub></math></span> or <span><math><msubsup><mi>T</mi><mn>2</mn><mo>∗</mo></msubsup></math></span> contrast agents and, in combination with relaxometry, enable quantitative analysis of physiological and pathological processes. However, the induced changes in relaxation times are influenced by a complex interplay of the contrast agents' physicochemical properties. Here, an open-source Monte Carlo simulation pipeline was implemented, which enables the characterization of these relaxation time changes caused by MRI contrast agents. The simulation tool was validated by showing that simulated relaxation times for iron oxide particles matched the solutions of analytical models of the respective diffusion regimes. For comparison with relaxometry measurements, <span><math><msub><mi>T</mi><mn>2</mn></msub></math></span> and <span><math><msubsup><mi>T</mi><mn>2</mn><mo>∗</mo></msubsup></math></span> of four MRI contrast agents Ferucarbotran, FeraSpin XL, magnetite nanohexagons (MNH@OH) and magnetite nanocubes (MNC@OH) were simulated, using three approaches for modeling contrast agent size and composition: 1) uniform particle sizes using the median hydrodynamic radii; 2) distributed radii corresponding to measured hydrodynamic radius distributions; 3) size-distributed magnetite cores in a coating layer of uniform radius. The simulation accurately reproduced measured relaxation times when appropriate modeling strategies for contrast agent size and composition were used. For FeraSpin XL and MNH@OH, using uniform radii provided good estimates of relaxation times, which was further improved by using the size distributions. For MNC@OH, discrepancies in simulated and measured <span><math><msub><mi>T</mi><mn>2</mn></msub></math></span> for all approaches were attributed to particle aggregation. For Ferucarbotran, coating and size distribution of the core had to be considered to match experimental data.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"125 ","pages":"Article 110553"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145425790","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 quantitative spatiotemporal relationship of whole brain activity of human brains revealed by fMRI","authors":"Jie Huang","doi":"10.1016/j.mri.2025.110552","DOIUrl":"10.1016/j.mri.2025.110552","url":null,"abstract":"<div><div>It is imperative to study individual brain functioning for understanding the neural bases of individual behavioral and clinical traits. BOLD-fMRI measures the four-dimensional (3 spatial and 1 temporal) neural activity across the entire brain at large-scale systems level. All local activities across the entire brain constitute the whole brain activity and each local activity is a part of that whole brain activity. Unlike a local activity that is characterized by its temporal neural activity, the whole brain activity is characterized by its spatial variation across the entire brain. We present a novel data-driven method to analyze the whole brain activity when performing tasks. The method enabled us to analyze the whole brain activity for each task trial and each individual subject with no requirement of a priori knowledge of task-evoked BOLD response. Our study revealed a quantitative spatiotemporal relationship of the whole brain activity with the local activities. The whole brain activity demonstrated a remarkable dynamic activity that varied from trial to trial when performing the same task repeatedly, showing the importance of analyzing the whole brain activity for investigating the neural bases of personal traits.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"125 ","pages":"Article 110552"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145425789","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":"Integration of radiomics, habitat imaging, and deep learning for MRI-based prediction of parametrial invasion in cervical cancer: A dual-center study","authors":"Yuemei Cui ,&nbsp;Ya Li ,&nbsp;Jing Na ,&nbsp;Junling Lu ,&nbsp;Xinyou Wang ,&nbsp;Shichao Han ,&nbsp;Jun Wang","doi":"10.1016/j.mri.2025.110542","DOIUrl":"10.1016/j.mri.2025.110542","url":null,"abstract":"<div><h3>Objectives</h3><div>To assess the diagnostic performance of radiomics, habitat imaging, and 2.5D deep learning models for MRI-based prediction of parametrial invasion in cervical cancer, and to evaluate the clinical utility of a multimodal integrated model.</div></div><div><h3>Methods</h3><div>This dual-center retrospective study included 290 patients with FIGO stage IB1–IIB cervical cancer who underwent preoperative MRI. Patients from Center A (<em>n</em> = 227) were divided into training and validation cohorts, while patients from Center B (<em>n</em> = 63) comprised the external test cohort. Radiomic features were extracted, habitat imaging was performed using k-means clustering, and a 2.5D deep learning model incorporated adjacent slices. Feature selection was conducted using Pearson correlation and LASSO regression. Machine learning models were developed, and an integrated model was constructed. Model performance was evaluated using AUC and accuracy. AUCs were compared with DeLong tests, calibration was assessed with the Hosmer–Lemeshow test, and clinical utility was evaluated with decision curve analysis.</div></div><div><h3>Results</h3><div>The integrated model outperformed all individual models, achieving AUCs of 0.973, 0.901, and 0.906 in the training, validation, and external test cohorts, respectively. Among individual models, the deep-learning model showed the highest AUCs (0.954, 0.803, 0.833), followed by habitat imaging (0.860, 0.811, 0.843). In the external test cohort, the peritumoral radiomics model outperformed the intratumoral model (0.843 vs. 0.719). The clinical model showed the lowest performance. Hosmer–Lemeshow tests indicated good calibration, and decision curve analysis confirmed superior clinical utility of the integrated model.</div></div><div><h3>Conclusion</h3><div>The multimodal integrated model, combining radiomics, habitat imaging, 2.5D deep learning, and clinical features, demonstrated superior predictive performance for parametrial invasion in cervical cancer compared with individual models. This approach may enhance preoperative assessment, guide clinical decision-making, and optimize treatment strategies.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"125 ","pages":"Article 110542"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145422019","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":"Feasibility of MR-tagging to quantify spinal cord motion in degenerative cervical myelopathy","authors":"Constantin von Deuster ,&nbsp;Georg Wilhelm Kajdi ,&nbsp;Shila Pazahr ,&nbsp;Nikolai Pfender ,&nbsp;Markus Hupp ,&nbsp;Armin Curt ,&nbsp;Reto Sutter ,&nbsp;Daniel Nanz","doi":"10.1016/j.mri.2025.110555","DOIUrl":"10.1016/j.mri.2025.110555","url":null,"abstract":"<div><h3>Background</h3><div>The aim of this study was to investigate the feasibility of MR-tagging for direct visualization and quantification of pathologically altered spinal-cord motion in patients with cervical spinal stenosis and compare it to the standard approach of phase contrast (PC) imaging.</div></div><div><h3>Methods</h3><div>In this prospective study, sagittal sections (22 cm field of view) of the cervical spine of nine patients with mono-segmental spinal-canal stenosis were imaged in different heart phases after selective pre-saturation of tissue magnetization in an axially oriented tag-stripe pattern (MR-tagging). Video loops of images acquired in different heart phases were viewed to directly observe and compare head-feet (HF) displacement at the level of the stenosis. The maximum HF displacement of MR-tags in the cord was quantitatively assessed and compared to that derived from integration of PC velocity data.</div></div><div><h3>Results</h3><div>Regional MR-tag displacement in the spinal cord could successfully be observed in all patients (4 females, 5 males, mean age 57 ± 7 years). Maximum displacement data derived from tagging at the stenosis level correlated excellently (R² = 0.84) with matched measurements from PC imaging.</div></div><div><h3>Conclusion</h3><div>Without complex post-processing, MR-tag imaging provides an intuitive direct visualization and quantification of pathologically altered spinal-cord motion at the level of cervical stenosis offering a faster alternative to PC imaging in clinical routine.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"125 ","pages":"Article 110555"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145390825","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":"Impact of RF saturation parameters and magnetization transfer contrast (MTC) signal model selection on amide proton transfer (APT) tumor contrast at 3 T","authors":"Munendra Singh ,&nbsp;Sultan Z. Mahmud ,&nbsp;Kevin Ju ,&nbsp;Jinyuan Zhou ,&nbsp;Hye-Young Heo","doi":"10.1016/j.mri.2025.110561","DOIUrl":"10.1016/j.mri.2025.110561","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the effect of RF saturation parameters and different amide proton transfer (APT) quantification methods on human brain tumor contrast.</div></div><div><h3>Methods</h3><div>Chemical exchange saturation transfer (CEST) images were acquired with varied RF saturation strengths, saturation times, and duty cycles (DC) at 3 T from brain tumor patients with glioblastoma. Different quantification methods, including magnetization transfer ratio asymmetry (MTR_asym), apparent exchange-dependent relaxation (AREX), and extrapolated semisolid MT reference (EMR), were compared for measuring APT. The effects of different MT contrast (MTC) lineshape assumptions, such as Gaussian vs. Lorentzian or super-Lorentzian, and symmetric vs. asymmetric, were also evaluated with respect to APT contrast in human brain tumors.</div></div><div><h3>Results</h3><div>Overall, higher RF saturation strengths (&gt; 1.5 μT) and longer saturation times with 100 % DC resulted in better enhancement of APT tumor contrast. The asymmetric super-Lorentzian MTC lineshape assumption was found to be the most accurate for in vivo brain tissue and provided the best fit for the acquired data. The EMR technique provided highly positive contrast for brain tumors, although still influenced by water relaxation effects.</div></div><div><h3>Conclusions</h3><div>RF saturation parameters and APT quantification methods can greatly influence brain tumor contrast. The choice of an APT quantification metric must be carefully considered according to RF saturation parameter settings. The EMR-based CEST calculation metric demonstrated high contrast for brain tumors, making it a powerful clinical biomarker for tumor detection.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"125 ","pages":"Article 110561"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145495617","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":"PHASE: Personalized Head-based Automatic Simulation for Electromagnetic properties in 7T MRI","authors":"Zhengyi Lu ,&nbsp;Hao Liang ,&nbsp;Ming Lu ,&nbsp;Dann Martin ,&nbsp;Benjamin M. Hardy ,&nbsp;Benoit M. Dawant ,&nbsp;Xiao Wang ,&nbsp;Xinqiang Yan ,&nbsp;Yuankai Huo","doi":"10.1016/j.mri.2025.110532","DOIUrl":"10.1016/j.mri.2025.110532","url":null,"abstract":"<div><div>Accurate and individualized human head models are becoming increasingly important for electromagnetic (EM) simulations. These simulations depend on precise anatomical representations to realistically model electric and magnetic field distributions, particularly when evaluating Specific Absorption Rate (SAR) within safety guidelines. State of the art simulations use the Virtual Population due to limited public resources and the impracticality of manually annotating patient data at scale. This paper introduces Personalized Head-based Automatic Simulation for EM properties (PHASE), an automated open-source toolbox that generates high-resolution, patient-specific head models for EM simulations using paired T1-weighted (T1w) magnetic resonance imaging (MRI) and computed tomography (CT) scans with 14 tissue labels. To evaluate the performance of PHASE models, we conduct semi-automated segmentation and EM simulations on 15 real human patients, serving as the gold standard reference. The PHASE model achieved comparable global SAR and localized SAR averaged over 10 grams of tissue (SAR-10g), demonstrating its potential as a promising tool for generating large-scale human model datasets in the future. The code and models of PHASE toolbox have been made publicly available: <span><span>https://github.com/hrlblab/PHASE</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"124 ","pages":"Article 110532"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145182084","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 radiomics predicts complete responses to initial transcatheter arterial chemoembolization in virus-associated early and intermediate stage HCC","authors":"Xinran Wen ,&nbsp;Zhaoyang Xu ,&nbsp;Hongbo Wang ,&nbsp;Yan Wang ,&nbsp;Qiaoyu Gu ,&nbsp;Xiran Jiang ,&nbsp;Cong Chen ,&nbsp;Yalian Yu","doi":"10.1016/j.mri.2025.110513","DOIUrl":"10.1016/j.mri.2025.110513","url":null,"abstract":"<div><h3>Background</h3><div>To evaluate the value of a multiparametric MRI-based nomogram on predicting response to transcatheter arterial chemoembolization (TACE) in virus-associated hepatocellular carcinoma (HCC) patients;</div></div><div><h3>Methods</h3><div>This study enrolled 235 and 51 patients from Center 1 and 2, respectively. All patients underwent baseline MRI scans before treatment. The least absolute shrinkage and selection operator (LASSO) regression method was used to screen radiomics features from intra- and peri-tumor areas to establish the radiomics signatures (RS). The nomogram model was built by integrating the RS and clinical predictors. Receiver operating characteristics (ROC), calibration and decision curve analyses (DCA) curves were used to assess predictive performances of radiomics models;</div></div><div><h3>Results</h3><div>In the training, internal validation and external validation cohort, the AUCs based on developed RS were 0.848, 0.759 and 0.762, respectively. The clinical model consisted of 4 significant distinct clinical predictors, including HBsAg, AFP, BCLC staging and size. To enhance diagnostic efficiency, we integrated 11 radiomics features and 4 clinical predictors to develop a nomogram model, which showed increased AUCs to 0.892, 0.851 and 0.787 in the training, internal validation and external validation cohort, respectively;</div></div><div><h3>Conclusions</h3><div>This study demonstrates that multiparametric MRI-based radiomics nomogram can preoperatively predict responses to TACE in virus-associated HCC.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"124 ","pages":"Article 110513"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144959602","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":"Correcting unwanted diffusion weighting in diffusion-weighted-STEAM sequence for time-dependent diffusion kurtosis imaging","authors":"Qinfeng Zhu ,&nbsp;Ruicheng Ba ,&nbsp;Zuozhen Cao ,&nbsp;Yao Shen ,&nbsp;Haotian Li ,&nbsp;Yi-Cheng Hsu ,&nbsp;Xu Yan ,&nbsp;Dan Wu","doi":"10.1016/j.mri.2025.110535","DOIUrl":"10.1016/j.mri.2025.110535","url":null,"abstract":"<div><h3>Purpose</h3><div>This study proposes an optimized acquisition protocol to minimize the unwanted diffusion weighting in DW-STEAM and facilitate time-dependent diffusion kurtosis imaging (tDKI).</div></div><div><h3>Methods</h3><div>We first corrected the diffusion-direction-dependent shift by optimizing the diffusion gradient amplitude. We then proposed to use low-b-value in the reference image, instead of the conventional non-diffusion-weighted (b0) acquisition, and removed the crusher gradients. The tDKI measurements from the proposed strategy were compared with conventional DWIs that included crushers in the b0 image, in a water phantom and in healthy adults (<em>n</em> = 8) on 3 T, and the water exchange time (<em>τ</em>_<em>ex</em>) was calculated from the tDKI measurements. The optimal strategy was tested in five <em>ex vivo</em> human brains, and the results were compared with <em>in vivo</em> data.</div></div><div><h3>Results</h3><div>Neglecting the diffusion weighting in b0 images introduced an artificial time dependence in the apparent diffusivity of the water phantom, and resulted in elevated estimates of water exchange time (<em>τ</em>_<em>ex</em>) in in the <em>in vivo</em> data. Additionally, the AIC indicated that, even when diffusion weighting in the b0 images was accounted for, the kurtosis estimation remained less stable in the <em>in vivo</em> than with the crusher-free approach. In contrast, using low-b-value images as reference measurements yielded reasonable tDKI estimates, with <em>τ</em>_<em>ex</em> ranging from 15 to 40 ms for gray matter <em>in vivo</em> and from 20 to 60 ms for gray matter <em>ex vivo</em>.</div></div><div><h3>Conclusions</h3><div>The optimized DW-STEAM acquisition eliminated artificial diffusion-time dependence, enabling the acquisition of accurate tDKI data for mapping structural morphology and transmembrane permeability.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"124 ","pages":"Article 110535"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267178","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":"Combined model based on multimodal imaging and clinical pathology: A new pathway to assess hypoxia status and HIF-1α expression in breast cancer","authors":"Zhiyuan He ,&nbsp;Yiwen Wang ,&nbsp;Yunxi Li ,&nbsp;Laimin Zhu ,&nbsp;Yueqin Chen ,&nbsp;Ning Mao ,&nbsp;Wenwen Zhao ,&nbsp;Xiuzheng Yue ,&nbsp;Yufei Xue ,&nbsp;Shuzhen Wang ,&nbsp;Weiwei Wang ,&nbsp;Zhanguo Sun","doi":"10.1016/j.mri.2025.110537","DOIUrl":"10.1016/j.mri.2025.110537","url":null,"abstract":"<div><h3>Purpose</h3><div>To develop and validate a combined model integrating multimodal magnetic resonance imaging (MRI) and clinical-pathological parameters to assess hypoxia status and hypoxia-inducible factor-1α (HIF-1α) expression in mass-like breast cancer.</div></div><div><h3>Methods</h3><div>This retrospective cohort study included 197 patients with mass-like breast cancer from two medical centers, who were divided into a training set (<em>n</em> = 104), an internal validation set (<em>n</em> = 45), and an external validation set (<em>n</em> = 48). Clinical-pathological and multimodal MRI parameters were analyzed using histopathology as the reference. The combined model was developed through logistic and least absolute shrinkage and selection operator (LASSO) regression analysis to identify features, and visualized using nomograms.</div></div><div><h3>Results</h3><div>Axillary lymph node (ALN) metastasis, time-intensity curve (TIC) type, mean diffusivity (MD), volumetric transfer constant (K^trans), and rate constant (Kep) were selected to construct the combined model. The diagnostic performance of the combined model [area under the curve (AUC) = 0.967, 95 % CI: 0.913–0.992] (training), was significantly better than that of other individual models (<em>P</em> &lt; 0.001). This performance was replicated in internal (AUC= 0.907) and external (AUC = 0.928) validation sets. The nomogram of the combined model showed excellent calibration (Hosmer-Lemeshow <em>P</em> = 0.653) and the highest net benefit across threshold probabilities in the decision curve analysis (DCA). Tumors with high HIF-1α expression exhibited higher ALN metastasis, histological grade, unclear margins, Type 3 TIC, and elevated K^trans, Kep, and MK, but reduced MD values.</div></div><div><h3>Conclusions</h3><div>The combined model (ALN + TIC + MD + K^trans + Kep) shows potential as a reliable tool for predicting HIF-1α expression levels in mass-like breast cancer.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"124 ","pages":"Article 110537"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145232921","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":"Correction of orientation dependence in magnetization transfer measures in the context of tractometry: Challenges, pitfalls and solutions","authors":"Philippe Karan ,&nbsp;Manon Edde ,&nbsp;Guillaume Gilbert ,&nbsp;Muhamed Barakovic ,&nbsp;Stefano Magon ,&nbsp;Maxime Descoteaux","doi":"10.1016/j.mri.2025.110522","DOIUrl":"10.1016/j.mri.2025.110522","url":null,"abstract":"<div><div>In this work we investigate the feasibility of a correction method for removing the orientation dependence of magnetization transfer (MT) measures in the context of tractometry. Following previous work on the track-based characterization of such orientation dependence using diffusion MRI, a correction method was developed. It uses polynomial fits to extrapolate the single-fiber characterizations and allows the MT measures across all white matter tracks to be shifted towards a chosen reference value, effectively removing the bias of fiber orientation with respect to the main magnetic field. Three different references were tested on a dataset of one hundred acquisitions and the performance was accessed by evaluating the removal of the orientation dependence and the reduction of variance between acquisitions, while also exploring the effects on tractometry results. Throughout these experiments, various challenges and pitfalls of an empirical correction method were laid out, like the absence of ground truth or the lack of knowledge about the complex behavior of the phenomenon in crossing-fiber voxels. Nonetheless, a solution was presented, paving the way towards a fully validated correction method for MT measures.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"124 ","pages":"Article 110522"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145065287","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}