Magnetic resonance imaging最新文献

{"title":"Stratifying non-alcoholic fatty liver disease using diffusion-weighted imaging–based habitat imaging: A rabbit model study","authors":"Jiale Hang ,&nbsp;Wenjian Wang ,&nbsp;Furong Wan ,&nbsp;Zhaorong Wang ,&nbsp;Jun Sun ,&nbsp;Dejuan Shen ,&nbsp;Jing Ye ,&nbsp;Xianfu Luo","doi":"10.1016/j.mri.2026.110614","DOIUrl":"10.1016/j.mri.2026.110614","url":null,"abstract":"<div><h3>Purpose</h3><div>Habitat imaging integrated multiparametric biomarkers, enabling precise quantification and visualization of hepatic tissue characteristics. This work evaluated DWI-based habitat imaging for stratifying Non-alcoholic Fatty Liver Disease (NAFLD) stages to enable early detection of Nonalcoholic Steatohepatitis (NASH).</div></div><div><h3>Methods</h3><div>Using existing DWI data from 32 male New Zealand rabbits randomized to high-fat/cholesterol or standard diets (NAFLD model), we applied a novel habitat imaging framework integrating mono-exponential diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), stretched exponential model (SEM), and diffusion kurtosis imaging (DKI) models. This identified four hepatic habitats reflecting perfusion, diffusion heterogeneity, and microstructural complexity. Unlike prior diffusion analyses, habitat fractions (H₁ - H₄) and heterogeneity (HH) indices were derived <em>via</em> spatial multiparametric pattern recognition. Diagnostic performance for NASH was assessed by AUC.</div></div><div><h3>Results</h3><div>NASH was characterized by a significantly lower fraction of habitat 1 (H₁) and higher fraction of habitat 4 (H₄) (<em>p</em> &lt; 0.05). Habitat heterogeneity (HH) was markedly reduced in NASH <em>versus</em> normal histology and simple steatosis (<em>p</em> &lt; 0.05). For distinguishing NASH, H₁ demonstrated a numerically higher AUC (0.931) compared to H₁ + H₄ (AUC = 0.922) and perfusion fraction (<em>f</em>, AUC = 0.909). However, the DeLong test confirmed no statistically significant differences among these AUC values.</div></div><div><h3>Conclusion</h3><div>DWI habitat imaging is a promising tool for NAFLD monitoring, detecting tissue heterogeneity with diagnostic performance comparable to conventional diffusion metrics.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"128 ","pages":"Article 110614"},"PeriodicalIF":2.0,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145952394","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":"2026-01-09","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":"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":"2026-01-03","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":"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":"2026-01-02","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":"Lenz effect in conductive nonmagnetic objects moved in MRI environments","authors":"Alessandro Arduino ,&nbsp;Oriano Bottauscio ,&nbsp;Michael Steckner ,&nbsp;Umberto Zanovello ,&nbsp;Luca Zilberti","doi":"10.1016/j.mri.2025.110605","DOIUrl":"10.1016/j.mri.2025.110605","url":null,"abstract":"<div><h3>Purpose:</h3><div>To model and predict the dynamics of conductive nonmagnetic objects moved within the MRI room under the influence of Lenz effect. High frequency motions, like vibrations induced by gradient eddy currents are not taken into account.</div></div><div><h3>Methods:</h3><div>The dynamics are described by an ordinary differential equation and the Lenz effect approximated under the assumption of negligible skin effect. This allows to separate the Lenz effect dependency on the object position and velocity, leading to a simple numerical procedure for objects of any shape.</div></div><div><h3>Results:</h3><div>The proposed model and numerical procedure were validated with experimental data recording the rotation of an aluminium plate falling inside a 1.5 T MRI scanner. The model was also applied for studying the translation of an aluminium plate pushed with constant force towards the MRI bore through the fringe field.</div></div><div><h3>Conclusion:</h3><div>The collected results showed that it is possible to obtain accurate predictions of motion in the presence of Lenz effect by neglecting the skin effect while determining the motional eddy currents induced in the metallic object.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110605"},"PeriodicalIF":2.0,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880744","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":"2025-12-29","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":"Development and validation of a prognostic prediction nomogram incorporating MRI and clinicopathological features in breast cancer patients after neoadjuvant chemotherapy","authors":"Shuluan Chen ,&nbsp;Shunan Che ,&nbsp;Mengying Yang ,&nbsp;Yufei Chen ,&nbsp;Yuan Tian ,&nbsp;Kun Ma ,&nbsp;Sicong Wang ,&nbsp;Jing Li","doi":"10.1016/j.mri.2025.110606","DOIUrl":"10.1016/j.mri.2025.110606","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aimed to develop and validate a prognostic nomogram integrating baseline MRI and clinicopathological features to predict disease-free survival (DFS) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC).</div></div><div><h3>Materials and methods</h3><div>A retrospective cohort of 402 invasive breast cancer patients who underwent pre-treatment MRI, NAC, and surgery between January 2014 and December 2021 was analyzed. Patients were randomly assigned to a training group (<em>n</em> = 280) and a validation group (<em>n</em> = 122). Variables were selected via univariate Cox regression and Lasso-Cox analyses, with significant factors used to construct nomogram models. The clinicopathological, baseline MRI and combined models were constructed. Model performance was assessed using the area under the curve (AUC), concordance index (C-index), and calibration curves. A risk score derived from the combined model facilitated stratification into high- and low-risk groups, with log-rank test used for survival comparison.</div></div><div><h3>Results</h3><div>Key predictors in the clinicopathological model included clinical T stage, pathological complete response (pCR) in primary tumor, pCR in axillary lymph nodes, and lymphovascular invasion. MRI-based predictors included multifocal or multicentric lesions, subcutaneous edema, and ipsilateral suspicious internal mammary lymph nodes. The combined model outperformed the clinicopathological (training C-index = 0.67, validation C-index = 0.754) and baseline MRI models (training C-index = 0.665, validation C-index = 0.605), achieving C-indices of 0.706 and 0.719 in the training and validation groups, respectively. A risk score cut-off of −0.35 effectively stratified patients into high- and low-risk groups.</div></div><div><h3>Conclusion</h3><div>This combined nomogram integrating clinicopathological and MRI features offers improved predictive accuracy for DFS in breast cancer patients after NAC, enabling enhanced risk stratification and individualized follow-up strategies.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110606"},"PeriodicalIF":2.0,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145878664","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":"NLMap-ATVR: A novel combination of nonlinear mapping network and adaptive total variation regularization for MRI denoising","authors":"Yu Weng ,&nbsp;Jufeng Zhao ,&nbsp;Christakis Damianou ,&nbsp;Kun Luo ,&nbsp;Guangmang Cui","doi":"10.1016/j.mri.2025.110608","DOIUrl":"10.1016/j.mri.2025.110608","url":null,"abstract":"<div><div>Magnetic resonance imaging (MRI) is an advanced imaging technique that is used to aid in medical diagnosis. However, common noises such as Gaussian and Rician noise can blur details and structures, affect contrast and reduce signal-to-noise ratio (SNR), so MRI denoising technique becomes an critical step to get noise-free MRI images. Traditional methods still have limitations in effectively balancing noise removal and the preservation of image details and structural information. To address the challenge, this paper proposes an MRI image denoising model that combines Nonlinear Mapping Network (NLMap) and Attention Mechanism-guided Adaptive Total Variation Regularization (ATVR). The model includes a NLMap-ATVR network, a crafted joint loss function and a Bayesian optimization framework. Firstly, the network uses an encoder-decoder architecture, combined with ATVR to ensure noise removal. Secondly, the joint loss function includes mean square error (MSE) loss, perceptual loss and ATVR loss, which are used to consider pixel-level and feature-level spatial structural errors to preserve details and structures. Thirdly, a Bayesian optimization framework is applied to automatically tune the hyperparameters to obtain optimal parameters. Compared with State-of-the-art methods, both subjective and objective evaluations based on experimental results demonstrate that the proposed method not only effectively removes noise but also significantly preserves details and structural information, which greatly improves SNR.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110608"},"PeriodicalIF":2.0,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145878677","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-weighted MRI discriminates hepatocellular carcinoma from intrahepatic cholangiocarcinoma: A prospective animal model study","authors":"Yong-Mei Huang ,&nbsp;Yu-Chen Wei ,&nbsp;Xing-Qing Qin ,&nbsp;Meng-Na Lan ,&nbsp;Jin-Yuan Liao","doi":"10.1016/j.mri.2025.110601","DOIUrl":"10.1016/j.mri.2025.110601","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate the diagnostic potential of microstructural parameters derived from time-dependent diffusion magnetic resonance imaging (Td-dMRI) for distinguishing hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC).</div></div><div><h3>Methods</h3><div>We established nude mouse models bearing subcutaneous xenografts of HCC (MHCC97H, HepG2 cell lines) and ICC (QBC939 cell line) (<em>n</em> = 30). All models underwent Td-dMRI scanning. Microstructural parameters, including cell diameter (<em>d</em>), extracellular diffusion coefficient (<em>D</em>_ex), intracellular volume fraction (<em>V</em>_in), and cellularity, were calculated based on the IMPULSED model. Intergroup differences were assessed using independent samples <em>t</em>-test or Mann-Whitney <em>U</em> test (significance threshold: <em>P</em> &lt; 0.05). The diagnostic performance of each parameter was evaluated by receiver operating characteristic (ROC) curve analysis. Post-operative liver tissue specimens were subjected to <em>β</em>-catenin immunohistochemical staining to validate the correlation between imaging parameters and pathological findings.</div></div><div><h3>Results</h3><div>The ICC group exhibited significantly higher <em>D</em>_ex values compared to the HCC group (<em>P</em> &lt; 0.05), whereas <em>d</em>, <em>V</em>_in, and cellularity were significantly lower in the ICC group (<em>P</em> &lt; 0.05). The areas under the ROC curve (AUCs) for differentiating HCC from ICC were 0.838 for <em>D</em>_ex, 0.779 for <em>d</em>, 0.833 for <em>V</em>_in, and 0.733 for cellularity. The <em>d</em> value measured by Td-dMRI showed a significant positive correlation with pathological results (<em>r</em> = 0.634, <em>P</em> &lt; 0.05). Notably, combining <em>V</em>_in and cellularity parameters enhanced the AUC to 0.95, outperforming any single parameter.</div><div>The ICC group exhibited a significantly higher extracellular diffusivity (<em>D</em>_ex) compared to the HCC group, whereas the cell diameter (<em>d</em>), intracellular volume fraction (<em>V</em>_in), and cellularity were significantly lower (all <em>P</em> &lt; 0.05). The area under the.</div></div><div><h3>Conclusion</h3><div>Td-dMRI enables non-invasive differentiation between HCC and ICC by quantifying distinct tumor microstructural environments. The parameters derived from this technique show promise as potential imaging biomarkers for subtyping liver cancers.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110601"},"PeriodicalIF":2.0,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145857030","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":"Nesterov accelerated spectral conjugate gradient algorithm for magnetic resonance imaging with TV regularisation","authors":"YueHong Ding ,&nbsp;ZhiBin Zhu ,&nbsp;Shuo Wang ,&nbsp;BenXin Zhang","doi":"10.1016/j.mri.2025.110604","DOIUrl":"10.1016/j.mri.2025.110604","url":null,"abstract":"<div><div>Magnetic Resonance Imaging (MRI) as a representative of noninvasive medical imaging, it has excellent soft tissue resolution, multi-parameter imaging capability and no radiation, thus it has been widely used in the fields of disease diagnosis, treatment and drug development. However, MRI technology still has problems such as long time consuming, sensitive to motion artefacts, and difficult to balance the imaging effect and speed. In this paper, a Nesterov accelerated spectral conjugate gradient algorithm (TV_NASCG) for MRI with TV regularisation is established from optimising reconstruction algorithms based on Total Variation (TV) regularised MRI model. The algorithm is based on a set of conjugate and spectral parameters proposed in this paper, and inspired by Nesterov acceleration technique, a set of Nesterov acceleration extrapolation step and acceleration parameter are proposed. In this paper, we also use step size acceleration technique and Powell restart strategy to further improve performance of the algorithm. In addition, we give a convergence analysis of TV_NASCG algorithm, which proves that it is sufficiently descent and globally convergent. Finally, in order to verify MRI performance of TV_NASCG algorithm, it was tested in MRI experiment and compared with other algorithms, and experimental results show that TV_NASCG algorithm can improve MRI quality and shorten imaging time.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110604"},"PeriodicalIF":2.0,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145850274","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}