Magnetic resonance imaging最新文献

{"title":"A new type of MCA pulses combining constant and offset-independent adiabaticity for magnetic resonance","authors":"Alexander V. Snadin ,&nbsp;Alexey S. Kiryutin ,&nbsp;Natalya N. Fishman ,&nbsp;Nikita N. Lukzen","doi":"10.1016/j.mri.2025.110385","DOIUrl":"10.1016/j.mri.2025.110385","url":null,"abstract":"<div><div>Adiabatic pulses are widely used in magnetic resonance techniques, and their development and refinement remain very relevant. Adiabatic inverting pulses are highly robust for radiofrequency or microwave magnetic field inhomogeneities and enable manipulation of spins over a large frequency range. In this work, new inverting pulses for spin 1/2 are proposed which combine the adiabaticity remaining constant for the single isochromat throughout the pulse and the same adiabaticity for all isochromats in a given bandwidth, but only at the single instant of time when the frequency of the pulse coincides with the frequency of the isochromat. The dependence of inversion performance of these pulses on peak amplitude of RF field, while preserving the pulse shape, is studied. These pulses may be useful for a number of MRI techniques where inverting pulses are an integral part. A comparison with other widely used adiabatic inverting pulses reveals performance improvements, achieving up to 30–40 % enhancement in inversion efficiency.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"120 ","pages":"Article 110385"},"PeriodicalIF":2.1,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143700931","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":"Deformable image registration with strategic integration pyramid framework for brain MRI","authors":"Yaoxin Zhang ,&nbsp;Qing Zhu ,&nbsp;Bowen Xie ,&nbsp;Tianxing Li","doi":"10.1016/j.mri.2025.110386","DOIUrl":"10.1016/j.mri.2025.110386","url":null,"abstract":"<div><div>Medical image registration plays a crucial role in medical imaging, with a wide range of clinical applications. In this context, brain MRI registration is commonly used in clinical practice for accurate diagnosis and treatment planning. In recent years, deep learning-based deformable registration methods have achieved remarkable results. However, existing methods have not been flexible and efficient in handling the feature relationships of anatomical structures at different levels when dealing with large deformations. To address this limitation, we propose a novel strategic integration registration network based on the pyramid structure. Our strategy mainly includes two aspects of integration: fusion of features at different scales, and integration of different neural network structures. Specifically, we design a CNN encoder and a Transformer decoder to efficiently extract and enhance both global and local features. Moreover, to overcome the error accumulation issue inherent in pyramid structures, we introduce progressive optimization iterations at the lowest scale for deformation field generation. This approach more efficiently handles the spatial relationships of images while improving accuracy. We conduct extensive evaluations across multiple brain MRI datasets, and experimental results show that our method outperforms other deep learning-based methods in terms of registration accuracy and robustness.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"120 ","pages":"Article 110386"},"PeriodicalIF":2.1,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143692525","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":"Radiomics-based MRI model to predict hypoperfusion in lacunar infarction.","authors":"Chia-Peng Chang, Yen-Chu Huang, Yuan-Hsiung Tsai, Leng-Chieh Lin, Jen-Tsung Yang, Kai-Hsiang Wu, Po-Han Wu, Syu-Jyun Peng","doi":"10.1016/j.mri.2025.110366","DOIUrl":"https://doi.org/10.1016/j.mri.2025.110366","url":null,"abstract":"<p><strong>Background: </strong>Approximately 20-30 % of patients with acute ischemic stroke due to lacunar infarction experience early neurological deterioration (END) within the first three days after onset, leading to disability or more severe sequelae. Hemodynamic perfusion deficits may play a crucial role in END, causing growth in the infarcted area and functional impairments, and even poor long-term prognosis. Therefore, it is vitally important to predict which patients may be at risk of perfusion deficits to initiate treatment and close monitoring early, preparing for potential reperfusion. Our goal is to utilize radiomic features from magnetic resonance imaging (MRI) and machine learning techniques to develop a predictive model for hypoperfusion.</p><p><strong>Method: </strong>During January 2011 to December 2020, a retrospective collection of 92 patients with lacunar stroke was conducted, who underwent MRI within 48 h, had clinical laboratory values, follow-up prognosis records, and advanced perfusion image to confirm the presence of hypoperfusion. Using the initial MRI of these patients, radiomics features were extracted and selected from Diffusion Weighted Imaging (DWI), Apparent Diffusion Coefficient (ADC), and Fluid Attenuated Inversion Recovery (FLAIR) sequences. The data was divided into an 80 % training set and a 20 % testing set, and a hypoperfusion prediction model was developed using machine learning.</p><p><strong>Result: </strong>Tthe model trained on DWI + FLAIR sequence showed superior performance with an accuracy of 84.1 %, AUC 0.92, recall 79.5 %, specificity 87.8 %, precision 83.8 %, and F1 score 81.2. Statistically significant clinical factors between patients with and without hypoperfusion included the NIHSS scores and the size of the lacunar infarction. Combining these two features with the top seven weighted radiomics features from DWI + FLAIR sequence, a total of nine features were used to develop a new prediction model through machine learning. This model in test set achieved an accuracy of 88.9 %, AUC 0.91, recall 87.5 %, specificity 90.0 %, precision 87.5 %, and F1 score 87.5.</p><p><strong>Conclusion: </strong>Utilizing radiomics techniques on DWI and FLAIR sequences from MRI of patients with lacunar stroke, it is possible to predict the presence of hypoperfusion, necessitating close monitoring to prevent the deterioration of clinical symptoms. Incorporating stroke volume and NIHSS scores into the prediction model enhances its performance. Future studies of a larger scale are required to validate these findings.</p>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":" ","pages":"110366"},"PeriodicalIF":2.1,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143692526","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":"DCE-MRI based deep learning analysis of intratumoral subregion for predicting Ki-67 expression level in breast cancer","authors":"Zhimin Ding ,&nbsp;Chengmeng Zhang ,&nbsp;Cong Xia ,&nbsp;Qi Yao ,&nbsp;Yi Wei ,&nbsp;Xia Zhang ,&nbsp;Nannan Zhao ,&nbsp;Xiaoming Wang ,&nbsp;Suhua Shi","doi":"10.1016/j.mri.2025.110370","DOIUrl":"10.1016/j.mri.2025.110370","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate whether deep learning (DL) analysis of intratumor subregion based on dynamic contrast-enhanced MRI (DCE-MRI) can help predict Ki-67 expression level in breast cancer.</div></div><div><h3>Materials and methods</h3><div>A total of 290 breast cancer patients from two hospitals were retrospectively collected. A k-means clustering algorithm confirmed subregions of tumor. DL features of whole tumor and subregions were extracted from DCE-MRI images based on 3D ResNet18 pre-trained model. The logistic regression model was constructed after dimension reduction. Model performance was assessed using the area under the curve (AUC), and clinical value was demonstrated through decision curve analysis (DCA).</div></div><div><h3>Results</h3><div>The k-means clustering method clustered the tumor into two subregions (habitat 1 and habitat 2) based on voxel values. Both the habitat 1 model (validation set: AUC = 0.771, 95 %CI: 0.642–0.900 and external test set: AUC = 0.794, 95 %CI: 0.696–0.891) and the habitat 2 model (AUC = 0.734, 95 %CI: 0.605–0.862 and AUC = 0.756, 95 %CI: 0.646–0.866) showed better predictive capabilities for Ki-67 expression level than the whole tumor model (AUC = 0.686, 95 %CI: 0.550–0.823 and AUC = 0.680, 95 %CI: 0.555–0.804). The combined model based on the two subregions further enhanced the predictive capability (AUC = 0.808, 95 %CI: 0.696–0.921 and AUC = 0.842, 95 %CI: 0.758–0.926), and it demonstrated higher clinical value than other models in DCA.</div></div><div><h3>Conclusions</h3><div>The deep learning model derived from subregion of tumor showed better performance for predicting Ki-67 expression level in breast cancer patients. Additionally, the model that integrated two subregions further enhanced the predictive performance.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"119 ","pages":"Article 110370"},"PeriodicalIF":2.1,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143634247","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":"Characterization of the evaluation method for hepatobiliary phase image by liver magnetic resonance imaging using Gd-EOB-DTPA.","authors":"Yasuo Takatsu, Shohei Harada, Yuya Yamatani, Kei Fukuzawa, Masafumi Nakamura, Kazuki Takano, Tosiaki Miyati","doi":"10.1016/j.mri.2025.110382","DOIUrl":"https://doi.org/10.1016/j.mri.2025.110382","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to characterize quantitative liver-spleen contrast (Q-LSC) and hepatocellular uptake index (HUI) for evaluating hepatobiliary phase (HBP) images using gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) in liver magnetic resonance imaging and to identify differences in the results obtain from these two measurement methods.</p><p><strong>Methods: </strong>Twenty-nine consecutive randomly selected patients were assessed using the 3.0 T MR system. Three regions of interest (ROI) were set for the liver parenchyma and spleen, and signal intensity (SI) was averaged. Q-LSC (SI of the liver divided by the SI of the spleen) and HUI [(Q-LSC-1) × liver volume] were calculated. Moreover, the volume and mean SI of the whole liver and spleen, left lateral segment (LLS), and the other segments were calculated. Subsequently, ROI-based and volume-based values for Q-LSC (R-LSC and V-LSC) and HUI (R-HUI and V-HUI), and the whole and each segment were compared.</p><p><strong>Results: </strong>R-LSC and V-LSC for the whole and each segment were not significantly different. Conversely, all combinations of HUI, except between R-HUI and V-HUI were significantly different (P < 0.01), for the whole liver. Correlations between R-LSC, R-HUI, and volume-based LLS were lower than the others.</p><p><strong>Conclusion: </strong>Q-LSC and HUI were characterized through an imaging evaluation of HBP with Gd-EOB-DTPA. R-LSC and R-HUI, or V-HUI, of the whole liver were strongly correlated, but the LLS affected the data, and HUI depends on liver volume. R-LSC is simple and easy to use for partial image evaluation.</p>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":" ","pages":"110382"},"PeriodicalIF":2.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630599","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":"Optimizing knee joint imaging: A comparative study of 7T MRI sequences","authors":"Xiaoqi Yi ,&nbsp;Zhiming Zhen ,&nbsp;Xiaoli Gou ,&nbsp;Wei Chen ,&nbsp;Wei Chen","doi":"10.1016/j.mri.2025.110384","DOIUrl":"10.1016/j.mri.2025.110384","url":null,"abstract":"<div><h3>Purpose</h3><div>To reduce long scan durations and improve patient comfort while maintaining image quality by assessing varying 7 T MRI sequences to optimize knee joint imaging.</div></div><div><h3>Materials and methods</h3><div>In this prospective study, healthy participants underwent knee joint scans using 7 T proton density fat-saturated (PD-FS), 3-dimensional double-echo steady-state (3D-DESS), and susceptibility-weighted imaging (SWI) sequences. We evaluated the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of cartilage, meniscus, ligaments, synovial fluid, and adipose tissue and the imaging quality of cartilage, meniscus, and ligaments. Also, we assessed the concurrence reached by two independent evaluators using the intraclass correlation coefficient (ICC).</div></div><div><h3>Results</h3><div>Twenty participants [mean age, 25.6 years ±8.4 (SD); 13 women] were evaluated. The 3D-DESS sequence demonstrated the highest SNR for cartilage, ligament, joint fluid, and meniscus structures (<em>P</em> &lt; .001). It performed similarly to the PD sequence for fat but outperformed the SWI sequence. The CNR analysis revealed that 3D-DESS produced the highest contrast between joint fluid and other structures (<em>P</em> &lt; .001), followed by PD-FS, whereas SWI exhibited the lowest contrast. The SWI sequence demonstrated superior CNR between ligament and fat (<em>P</em> &lt; .001). The PD-FS sequence exhibited the highest CNR between cartilage and meniscus (<em>P</em> &lt; .001). Both observers reported substantial concordance in their evaluations (ICC &gt; 0.7). The cartilage visualization was excellent in all sequences, with the SWI sequence displaying slight superiority (<em>P</em> &lt; .05). The ligament and meniscus images were of the highest quality when using PD-FS (<em>P</em> &lt; .001).</div></div><div><h3>Conclusion</h3><div>A combination of PD-FS and 3D DESS sequences is recommended for comprehensive and comfortable 7 T MRI assessments of knee joints, ensuring detailed visualization of various vital knee structures.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"119 ","pages":"Article 110384"},"PeriodicalIF":2.1,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143597261","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":"Precipitation of gadolinium from magnetic resonance imaging contrast agents may be the Brass tacks of toxicity","authors":"Ian M. Henderson ,&nbsp;Angelica D. Benevidez ,&nbsp;Curtis D. Mowry ,&nbsp;John Watt ,&nbsp;George D. Bachand ,&nbsp;Martin L. Kirk ,&nbsp;Karol Dokładny ,&nbsp;Joshua DeAguero ,&nbsp;G. Patricia Escobar ,&nbsp;Brent Wagner","doi":"10.1016/j.mri.2025.110383","DOIUrl":"10.1016/j.mri.2025.110383","url":null,"abstract":"<div><div>The formation of gadolinium-rich nanoparticles in multiple tissues from intravenous magnetic resonance imaging contrast agents may be the initial step in rare earth metallosis. The mechanism of gadolinium-induced diseases is poorly understood, as is how these characteristic nanoparticles are formed. Gadolinium deposition has been observed with all magnetic resonance imaging contrast agent brands. Aside from endogenous metals and acidic conditions, little attention has been paid to the role of the biological milieu in the degradation of magnetic resonance imaging contrast agents into nanoparticles. Herein, we describe the decomposition of the commercial magnetic resonance imaging contrast agents Omniscan and Dotarem in the presence of oxalic acid, a well-known endogenous compound. Omniscan dechelated rapidly and preluded measurement by the means available, while Dotarem underwent a two-step decomposition process. The decomposition of both magnetic resonance imaging contrast agents by oxalic acid formed gadolinium oxalate (Gd₂[C₂O₄]₃, Gd₂Ox₃). Furthermore, both observed steps of the Dotarem reaction involved the associative addition of oxalic acid. Adding protein (bovine serum albumin) increased the rate of dechelation. Displacement reactions could occur at lysosomal pH. Through these studies, we have demonstrated that magnetic resonance imaging contrast agents can be dissociated by endogenous molecules, thus illustrating a metric by which gadolinium-based contrast agents (GBCAs) might be destabilized in vivo.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"119 ","pages":"Article 110383"},"PeriodicalIF":2.1,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143597262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DGEDDGAN: A dual-domain generator and edge-enhanced dual discriminator generative adversarial network for MRI reconstruction","authors":"Qiaohong Liu ,&nbsp;Weikun Zhang ,&nbsp;Yuting Zhang ,&nbsp;Xiaoxiang Han ,&nbsp;Yuanjie Lin ,&nbsp;Xinyu Li ,&nbsp;Keyan Chen","doi":"10.1016/j.mri.2025.110381","DOIUrl":"10.1016/j.mri.2025.110381","url":null,"abstract":"<div><div>Magnetic resonance imaging (MRI) as a critical clinical tool in medical imaging, requires a long scan time for producing high-quality MRI images. To accelerate the speed of MRI while reconstructing high-quality images with sharper edges and fewer aliases, a novel dual-domain generator and edge-enhancement dual discriminator generative adversarial network structure named DGEDDGAN for MRI reconstruction is proposed, in which one discriminator is responsible for holistic image reconstruction, whereas the other is adopted to enhance the edge preservation. A dual-domain U-Net structure that cascades the frequency domain and image domain is designed for the generator. The densely connected residual block is used to replace the traditional U-Net convolution block to improve the feature reuse capability while overcoming the gradient vanishing problem. The coordinate attention mechanism in each skip connection is employed to effectively reduce the loss of spatial information and enforce the feature selection capability. Extensive experiments on two publicly available datasets i.e., IXI dataset and CC-359, demonstrate that the proposed method can reconstruct the high-quality MRI images with more edge details and fewer artifacts, outperforming several state-of-the-art methods under various sampling rates and masks. The time of single-image reconstruction is below 13 ms, which meets the demand of faster processing.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"119 ","pages":"Article 110381"},"PeriodicalIF":2.1,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594146","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":"Quantitative analysis of ureteral jets with dynamic magnetic resonance imaging and a deep-learning approach","authors":"Mingyan Wu ,&nbsp;Wanning Zeng ,&nbsp;Yanbin Li ,&nbsp;Chang Ni ,&nbsp;Jiaying Zhang ,&nbsp;Xiangwei Kong ,&nbsp;Jeff L. Zhang","doi":"10.1016/j.mri.2025.110380","DOIUrl":"10.1016/j.mri.2025.110380","url":null,"abstract":"<div><h3>Objective</h3><div>To develop dynamic MRU protocol that focuses on the bladder to capture ureteral jets and to automatically estimate frequency and duration of ureteral jets from the dynamic images.</div></div><div><h3>Methods</h3><div>Between February and July 2023, we collected 51 sets of dynamic MRU data from 5 healthy subjects. To capture the entire longitudinal trajectory of ureteral jets, we optimized orientation and thickness of the imaging slice for dynamic MRU, and developed a deep-learning method to automatically estimate frequency and duration of ureteral jets from the dynamic images.</div></div><div><h3>Results</h3><div>Among the 15 sets of images with different slice positioning, the positioning with slice thickness of 25 mm and orientation of 30° was optimal. Of the 36 sets of dynamic images acquired with the optimal protocol, 27 sets or 2529 images were used to train a U-Net model for automatically detecting the presence of ureteral jets. On the other 9 sets or 760 images, accuracy of the trained model was found to be 84.9 %. Based on the results of automatic detection, frequency of ureteral jet in each set of dynamic images was estimated as 8.0 ± 1.4 min⁻¹, deviating from reference by −3.3 % ± 10.0 %; duration of each individual ureteral jet was estimated as 7.3 ± 2.8 s, deviating from reference by 2.4 % ± 32.2 %. The accumulative duration of ureteral jets estimated by the method correlated well (with coefficient of 0.936) with the bladder expansion recorded in the dynamic images.</div></div><div><h3>Conclusions</h3><div>The proposed method was capable of quantitatively characterizing ureteral jets, potentially providing valuable information on functional status of ureteral peristalsis.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"119 ","pages":"Article 110380"},"PeriodicalIF":2.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143586128","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":"Denoising of 3D magnetic resonance images via edge-enhanced low-rank tensor decomposition","authors":"Li Wang ,&nbsp;Chong Zeng ,&nbsp;Xingtuo Zhang ,&nbsp;Liang Zhao","doi":"10.1016/j.mri.2025.110365","DOIUrl":"10.1016/j.mri.2025.110365","url":null,"abstract":"<div><div>Magnetic Resonance images (MRI) denoising is to obtain high quality image from infectant version. Recently, low-rank tensor (LRT) methods have been developed and attained resounding success in MRI denoising. However, these pure LRT models are incapable of utilizing the comprehensive inherent information of clean MRI. To overcome these drawbacks, we design a novel edge-enhanced low-rank tensor approximation (EELRTA) framework for Rician noise removal. The tensor gradient L0 norm regularization with describing the local structure information is incorporated into the weighted core tensor rank model for improving texture edge preservation. The application of weights can further preserve the potentially useful information distributed on the different core tensor coefficients with different physical meanings. What's more, non-local self-similarity tactic is employed for low-rank sparsity-encourage and enhancing anti-noise capability of EELRTA model. The proposed EELRTA method is tackled by an efficient alternating direction method of multipliers (ADMM). The Experiment results on simulation and multiple sclerosis lesion (MSL) data illustrate that the proposed method can effectively remove noise while reasonably retaining pathological structure information.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"119 ","pages":"Article 110365"},"PeriodicalIF":2.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143586125","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}