Magnetic resonance imaging最新文献

{"title":"Spatial-frequency aware zero-centric residual unfolding network for MRI reconstruction","authors":"Yupeng Lian ,&nbsp;Zhiwei Liu ,&nbsp;Jin Wang ,&nbsp;Shuai Lu","doi":"10.1016/j.mri.2025.110334","DOIUrl":"10.1016/j.mri.2025.110334","url":null,"abstract":"<div><div>Magnetic Resonance Imaging is a cornerstone of medical diagnostics, providing high-quality soft tissue contrast through non-invasive methods. However, MRI technology faces critical limitations in imaging speed and resolution. Prolonged scan times not only increase patient discomfort but also contribute to motion artifacts, further compromising image quality. Compressed Sensing (CS) theory has enabled the acquisition of partial k-space data, which can then be effectively reconstructed to recover the original image using advanced reconstruction algorithms. Recently, deep learning has been widely applied to MRI reconstruction, aiming to reduce the artifacts in the image domain caused by undersampling in k-space and enhance image quality. As deep learning continues to evolve, the undersampling factors in k-space have gradually increased in recent years. However, these layers are limited in compensating for reconstruction errors in the unsampled areas, impeding further performance improvements. To address this, we propose a learnable spatial-frequency difference-aware module that complements the learnable data consistency layer, mapping k-space domain differences to the spatial image domain for perceptual compensation. Additionally, inspired by wavelet decomposition, we introduce explicit priors by decomposing images into mean and residual components, enforcing a refined zero-mean constraint on the residuals while maintaining computational efficiency. Comparative experiments on the FastMRI and Calgary-Campinas datasets demonstrate that our method achieves superior reconstruction performance against seven state-of-the-art techniques. Ablation studies further confirm the efficacy of our model's architecture, establishing a new pathway for enhanced MRI reconstruction.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110334"},"PeriodicalIF":2.1,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143039641","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":"Improving MRI turbulence quantification by addressing the measurement errors caused by the derivatives of the turbulent velocity field – Sequence development and in-vitro validation","authors":"Swantje Romig ,&nbsp;Kristine John ,&nbsp;Simon Schmidt ,&nbsp;Sebastian Schmitter ,&nbsp;Sven Grundmann ,&nbsp;Martin Bruschewski","doi":"10.1016/j.mri.2025.110333","DOIUrl":"10.1016/j.mri.2025.110333","url":null,"abstract":"<div><h3>Purpose</h3><div>To improve the current method for MRI turbulence quantification which is the intravoxel phase dispersion (IVPD) method. Turbulence is commonly characterized by the Reynolds stress tensor (RST) which describes the velocity covariance matrix. A major source for systematic errors in MRI is the sequence's sensitivity to the variance of the derivatives of velocity, such as the acceleration variance, which can lead to a substantial measurement bias.</div></div><div><h3>Methods</h3><div>We developed a Cartesian phase contrast sequence with FAST velocity encoding and two separately measured partial echoes with opposite readout directions. This design aims to reduce the high-order gradient moments that are responsible for the described measurement error. Velocity encoding directions follow the ICOSA6 scheme to capture the full RST. Turbulence data is reconstructed using the intra-voxel phase dispersion (IVPD) technique. We validated this sequence in vitro using a periodic hill flow benchmark with highly anisotropic turbulence. MRI data underwent extensive averaging, with multiple velocity encoding values employed to reduce noise and isolate systematic effects.</div></div><div><h3>Results</h3><div>The RST data obtained from the new sequence agree well with the ground truth. Compared to a state-of-the-art sequence, the maximum errors were reduced by factor five.</div></div><div><h3>Conclusion</h3><div>Simple adjustments to current MRI protocols can greatly enhance turbulence measurement accuracy through the reduction of high-order gradient moments. The proposed measures include applying FAST velocity encoding, high readout bandwidth, and a highly asymmetric readout. Ringing artifacts due to the asymmetric readout can be removed via a second, inverted readout.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110333"},"PeriodicalIF":2.1,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143039640","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":"The relative contrast ratio of malignant bone lesion is highest on relative Fat Fraction maps compared with T1-weighted and b900 DWI images","authors":"Francesca Castagnoli ,&nbsp;Alina Dragan ,&nbsp;Antonio Candito ,&nbsp;Nina Tunariu ,&nbsp;Christina Messiou ,&nbsp;Dow-Mu Koh","doi":"10.1016/j.mri.2025.110331","DOIUrl":"10.1016/j.mri.2025.110331","url":null,"abstract":"<div><h3>Introduction</h3><div>Whole-body MRI (WB-MRI) is increasingly used in clinical practice for detection of malignant bone disease. A high relative contrast ratio (RCR) of malignant bone lesions compared with normal bone can improve disease detection in breast, prostate and myeloma malignancies. However, the RCR of malignant bone lesions on T1w, DWI and relative Fat Fraction (rFF) maps derived from Dixon T1w have not been compared.</div></div><div><h3>Methods</h3><div>110 baseline WB-MRI of patients with suspected malignant bone lesions were reviewed retrospectively. On each scan, up to four active bone lesions were identified, one each at the cervicothoracic spine, lumbosacral spine, pelvis and extremity, and their ROI signal intensity measured on rFF, T1w and DWI b = 900. The signal intensity of background bone was measured by placing an ROI on the nearest normal-appearing bone to each lesion, for each sequence. The mean lesion signal-to-background ratio (taken as RCR) for each lesion was calculated. We compared the RCR of bone lesions on rFF, T1w and DWI (Mann-Whitney test).</div></div><div><h3>Results</h3><div>The median rFF RCR of malignant bone lesions was highest compared with normal bone marrow than that of T1w (<em>p</em> &lt; 0.0001) and DWI (p &lt; 0.0001). There was no significant difference in the median rFF RCR of malignant bone lesions from breast cancer, myeloma and prostate cancer (<em>p</em> &gt; 0.017, Bonferroni correction) or according to their anatomical locations (<em>p</em> &gt; 0.012, Bonferroni correction).</div></div><div><h3>Conclusions</h3><div>Malignant bone lesion RCR measured by lesion/background signal intensity was higher on rFF than on T1w and DWI b = 900 in patients with prostate, breast and myeloma malignancies, indicating its value for disease detection.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110331"},"PeriodicalIF":2.1,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143039643","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":"Prognosticating WHO/ISUP grade in clear cell renal cell carcinoma: Insights from amide proton transfer-weighted MRI","authors":"Qingxuan Wan ,&nbsp;Yun Xu ,&nbsp;Yutao Jiang ,&nbsp;Jinglu Li ,&nbsp;Yulan Li ,&nbsp;Xihui Ren ,&nbsp;Fang Wang ,&nbsp;Xuyou Zhu ,&nbsp;Gang Wu ,&nbsp;Peng WU ,&nbsp;Peijun Wang ,&nbsp;Aijun Shen","doi":"10.1016/j.mri.2025.110332","DOIUrl":"10.1016/j.mri.2025.110332","url":null,"abstract":"<div><h3>Background</h3><div>Preoperative prediction of clear cell renal cell carcinoma (ccRCC) grade can support optimal selection of surgical resection strategies. Currently, there is no effective preoperative method for accurately assessing the histologic grade of ccRCC. More precise, non-invasive prediction methods are needed.</div></div><div><h3>Purpose</h3><div>To investigate the parameter of amide proton transfer-weighted (APTw) MRI for prediction of pathological ccRCC grade and whether the combination of the APTw, Diffusion-Weighted Imaging (DWI), and mDixon MRI techniques can improve the prediction performance.</div></div><div><h3>Materials and methods</h3><div>This prospective study included ccRCC patients who underwent multiparametric MRI examinations (including APTw, DWI, and mDixon) along with contrast-enhanced CT or MRI examinations from March 2021 to June 2024. The mean, maximum, minimum, and standard deviation (SD) values from APTw and apparent diffusion coefficient (ADC) imaging of the solid tumor portions and the fat fraction (FF) values from mDixon imaging were measured. The area under the receiver operating characteristic curve (AUC) values for the parameters were calculated to evaluate the diagnostic performance.</div></div><div><h3>Results</h3><div>A total of 48 ccRCC patients (mean age, 65 years; 32 males) were included, of whom 10 had high-grade ccRCC and 38 had low-grade ccRCC. Compared with low-grade ccRCC, high-grade ccRCC exhibited higher APTw_mean (2.28 % vs. 4.17 %; <em>P</em> &lt; .05), APTw_max (4.97 % vs. 8.48 %; <em>P</em> &lt; .01), and APTwSD (1.10 vs. 1.75; <em>P</em> &lt; .01) values as well as lower ADC_min values (1.23 × 10⁻³ mm²/s vs. 0.94 × 10⁻³ mm²/s; <em>P</em> &lt; .05). The AUC values for the ability of APTw_mean, APTw_max, APTwSD, ADC_min, and these factors combined to distinguish between low-grade and high-grade ccRCC were 0.728, 0.786, 0.832, 0.741, and 0.858, respectively.</div></div><div><h3>Conclusion</h3><div>APTw_mean, APTw_max, APTwSD, and ADC_min values are potential indicators for preoperative prediction of ccRCC pathologic grade. APTw parameters offer certain advantages for use alone or in combination with other parameters.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110332"},"PeriodicalIF":2.1,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143039642","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":"Evaluating the added value of multiparametric functional MRI in predicting renal function response to renal artery stenting in severe atherosclerotic renal artery stenosis","authors":"Jia Fu ,&nbsp;Zhiyong Lin ,&nbsp;Bihui Zhang ,&nbsp;Xiaoyan Meng ,&nbsp;Jianxing Qiu ,&nbsp;Min Yang ,&nbsp;Yinghua Zou","doi":"10.1016/j.mri.2025.110329","DOIUrl":"10.1016/j.mri.2025.110329","url":null,"abstract":"<div><h3>Objective</h3><div>To explore the potential of Intravoxel Incoherent Motion Diffusion (IVIM) and Arterial Spin Labeling (ASL) in predicting the short-term effectiveness of post-revascularization for severe atherosclerotic renal artery stenosis.</div></div><div><h3>Material and methods</h3><div>A retrospective analysis of 88 cases from October 2018 to February 2023 was conducted. Patients were divided into Responder and Non-Responder groups based on renal function outcomes at their last follow-up. Clinical data were compared between the groups, and preoperative functional MRI images were analyzed. ROIs were outlined for the affected and both kidneys. Measurements included ASL-derived renal blood flow (RBF), IVIM's pseudo-diffusion coefficient (D*), perfusion fraction (f), true diffusion coefficient (D), and the conventional apparent diffusion coefficient (ADC).Multivariate logistic regression identified independent clinical predictors of benefit, and a clinical prediction model was developed. Model performance was assessed using Receiver Operating Characteristic (ROC) curves and Decision Curve Analysis(DCA) curves.</div></div><div><h3>Results</h3><div>In the training cohort of 54 non-responders and 34 responders, no quantitative parameters of bilateral kidneys showed statistical significance in predicting Responders (all <em>p</em> &gt; 0.05). Pre-treatment eGFR, presence of diabetes, and the D value of the affected kidney were identified as independent factors for predicting short-term treatment effectiveness. The combined clinical-functional imaging model yielded a higher AUC at 0.796 (95 % CI: 0.690–0.897). Decision curve analysis further confirmed the better net benefit of combined model.</div></div><div><h3>Conclusion</h3><div>Beyond clinical characteristics, functional MRI had the potential to predict response of stenting for severe atherosclerotic renal artery stenosis.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110329"},"PeriodicalIF":2.1,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143039639","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":"Magnetic field probe-based co-simulation method for irregular volume-type inductively coupled wireless MRI radiofrequency coils","authors":"Ming Lu ,&nbsp;Hao Liang ,&nbsp;Haoqin Zhu ,&nbsp;Xinqiang Yan","doi":"10.1016/j.mri.2025.110330","DOIUrl":"10.1016/j.mri.2025.110330","url":null,"abstract":"<div><h3>Background</h3><div>Inductively coupled wireless coils are increasingly used in MRI due to their cost-effectiveness and simplicity, eliminating the need for expensive components like preamplifiers, baluns, coil plugs, and coil ID circuits. Existing tools for predicting component values and electromagnetic (EM) fields are primarily designed for cylindrical volume coils, making them inadequate for irregular volume-type wireless coils.</div></div><div><h3>Purpose</h3><div>The aim of this study is to introduce and validate a novel magnetic (H-) field probe-based co-simulation method to accurately predict capacitance values and EM fields for irregular volume-type wireless coils, thereby addressing the limitations of current prediction tools.</div></div><div><h3>Methods</h3><div>The proposed method involves several key steps: modeling the coil in EM simulation software, replacing lumped components with 50-Ω ports, placing well-decoupled double pick-up sniffer probes within the wireless coil, conducting full-wave EM simulations, and exporting the S-parameter matrix to an RF circuit simulation tool for optimization. The RF circuit simulation optimizes component values by maximizing the average magnitude of the root square of Sxys (mean_√Sxy) of double probes and minimizing the normalized standard deviation of √Sxy (normStd_√Sxy). The optimized capacitance values are validated through re-performing EM simulations, and hardware prototypes are fabricated and tested in MRI experiments.</div></div><div><h3>Results</h3><div>The method was validated using bottle-shaped and dome-shaped Litzcage coils designed for 1.5 T MRI. Consistent resonant peaks and magnetic field distributions were observed across different coil designs. The optimized capacitance values obtained from circuit-level simulations were confirmed through EM simulations. Significant SNR enhancements were observed in MRI experiments, with the wireless hand and wrist/head coil showing an overall SNR enhancement of 12.8/3.4-fold in EM simulation and 13.4/3.8-fold in MRI experiments, compared to the body coil alone.</div></div><div><h3>Conclusions</h3><div>The H-field probe-based co-simulation method provides an efficient and accurate solution for designing and optimizing irregular wireless RF coils in MRI. By integrating EM simulation, H-field probes, and RF circuit optimization, this method reduces the need for extensive full-wave EM simulations and accurately predicts capacitance values and EM fields. The validation using irregular Litzcage coils demonstrated the method's efficacy, contributing to improved imaging quality in MRI applications. This approach offers a valuable tool for coil developers and researchers, facilitating the development of high-quality irregular wireless coils for enhanced MRI performance.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110330"},"PeriodicalIF":2.1,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143029138","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":"Deep learning-based free-water correction for single-shell diffusion MRI","authors":"Tianyuan Yao ,&nbsp;Derek B. Archer ,&nbsp;Praitayini Kanakaraj ,&nbsp;Nancy Newlin ,&nbsp;Shunxing Bao ,&nbsp;Daniel Moyer ,&nbsp;Kurt Schilling ,&nbsp;Bennett A. Landman ,&nbsp;Yuankai Huo","doi":"10.1016/j.mri.2025.110326","DOIUrl":"10.1016/j.mri.2025.110326","url":null,"abstract":"<div><div>Free-water elimination (FWE) modeling in diffusion magnetic resonance imaging (dMRI) is crucial for accurate estimation of diffusion properties by mitigating the partial volume effects caused by free water, particularly at the interface between white matter and cerebrospinal fluid. The presence of free water partial volume effects leads to biases in estimating diffusion properties. Additionally, the existing mathematical FWE model is a two-compartment model, which can be well posed for multi-shell data. However, single-shell acquisitions are more common in clinical cohorts due to time constraints. To overcome these problems, we proposed a deep-learning framework that focuses on mapping and correcting free-water partial volume contamination in DWI. It utilizes data-driven techniques to infer plausible free-water volumes across different diffusion MRI acquisition schemes, including single-shell acquisitions. In this work, we study the Human Connectome Project Young Adults (HCP-ya), the HCP Aging dataset (HCP-a) as well as Brain Tumor Connectomics Data (BTC). The evaluation demonstrates that it produces more plausible results compared to previous single-shell free water estimation approaches. The proposed method is generalizable through model fine-tuning and b-value re-mapping when dealing with new data. The results have demonstrated improved consistency of properties estimation between scan/rescan data and accuracy in identifying neural pathways, as well as enhanced clarity in the visualization of white matter tracts.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110326"},"PeriodicalIF":2.1,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143007617","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":"Log subtracted inversion recovery","authors":"Mark Bydder ,&nbsp;Daniel M. Cornfeld ,&nbsp;Tracy R. Melzer ,&nbsp;Paul Condron ,&nbsp;Gil Newburn ,&nbsp;Eryn E. Kwon ,&nbsp;Maryam Tayebi ,&nbsp;Miriam Scadeng ,&nbsp;Samantha J. Holdsworth ,&nbsp;Graeme M. Bydder","doi":"10.1016/j.mri.2025.110328","DOIUrl":"10.1016/j.mri.2025.110328","url":null,"abstract":"<div><div>Magnetic resonance imaging (MRI) techniques have recently been developed for obtaining high T₁ contrast images using inversion recovery (IR) images at two inversion times (TIs) rather than a single TI. They use simple mathematical operations – multiplication, addition, subtraction, division – to create images not attainable by conventional IR. The present study describes a novel two-point IR technique formed by the subtraction of log images. Results show it has a near-linear response to T₁ between the nullpoints that peaks sharply at the nullpoints. This produces a bright isoT₁ contour at interfaces between tissues where partial volume mixing generates specific T₁s. This can provide anatomical information in areas where the signal is not well-differentiated on conventional images.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110328"},"PeriodicalIF":2.1,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143008249","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":"Accelerated MR cell size imaging through parallel acquisition technique (PAT) and simultaneous multi-slice (SMS) with local principal component analysis (LPCA) enhancement","authors":"Tianxiong Wu ,&nbsp;Jiayu Sun ,&nbsp;Zhihao Wang ,&nbsp;Jia Tan ,&nbsp;Xianqing Tang ,&nbsp;Deng Xiong ,&nbsp;Thorsten Feiweier ,&nbsp;Qiyong Gong ,&nbsp;Haoyang Xing ,&nbsp;Min Wu","doi":"10.1016/j.mri.2025.110327","DOIUrl":"10.1016/j.mri.2025.110327","url":null,"abstract":"<div><div>Microstructural parameters are essential in tumor research, aiding in the understanding tumor pathogenesis, grading, and therapeutic efficacy. The imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED) model is the most widely used MR cell size imaging technique, demonstrating success in measuring microstructural parameters of solid tumors <em>in vivo</em>. However, its clinical application is limited by the longer scan times required for both pulsed gradient spin-echo (PGSE) and multiple oscillating gradient spin-echo (OGSE) acquisitions across a range of b-values, which can be burdensome for patients and disrupt clinical workflows. In this work, we propose and evaluate an accelerating method that integrates parallel acquisition technique (PAT) and simultaneous multi-slice (SMS) with local principal component analysis (LPCA) denoising to reduce scan times while maintaining image quality in MR cell size imaging. PGSE and OGSE (25 Hz, 50 Hz) images were acquired using P2S2 (PAT2-SMS2), P2S3 (PAT2-SMS3), and P3S3 (PAT3-SMS3) configurations, incorporating LPCA denoising, and compared to standard P2 (PAT2-SMS1) in healthy volunteers and brain tumor patients at 3 T. Additionally, clinical feasibility was further assessed through qualitative and quantitative evaluations. Qualitative assessment, conducted by two radiologists using a 5-point Likert scale, and quantitative analysis, including noise estimation, apparent diffusion coefficient (ADC) calculation, and estimation of microstructural parameters—cell diameter (Dmean), intracellular volume fraction (Vin), and extracellular diffusivity (Dex), were performed. Overall, the integration of PAT and SMS techniques reduces acquisition time by approximately 60 % compared to standard P2 acceleration, while maintaining comparable image quality and structural fidelity with LPCA denoising.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110327"},"PeriodicalIF":2.1,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143008248","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 knee cartilage using accelerated 3 T MRI: Evaluation of an isotropic 3D fast spin-echo sequence (CUBE) with compressed sensing technique","authors":"Thibault Willaume ,&nbsp;Matthieu Ehlinger ,&nbsp;Henri Favreau ,&nbsp;Noëlle Weingertner ,&nbsp;Pierre-Emmanuel Zorn ,&nbsp;Jean-Philippe Dillenseger ,&nbsp;Guillaume Koch ,&nbsp;Michel Velten ,&nbsp;Guillaume Bierry","doi":"10.1016/j.mri.2025.110321","DOIUrl":"10.1016/j.mri.2025.110321","url":null,"abstract":"<div><h3>Purpose</h3><div>Compressed Sensing (CS) is an emerging technique to accelerate MRI acquisitions. The aim of this study was to assess the reliability and accuracy of cartilage thickness measurements in the knee using a CS-enabled isotropic 3D Fast Spin-Echo (FSE) sequence on a 3-T MRI scanner.</div></div><div><h3>Methods</h3><div>Twenty-eight tibial condyle sections were collected from 14 adult patients who underwent total knee arthroplasty. An isotropic 3D PDw FSE with variable flip-angle (CUBE) sequence with CS was used to acquire MR images of the tibial condyle sections. Minimum cartilage thickness measurements were independently performed by two experienced readers (R1 and R2) on MR images and compared to corresponding anatomical sections measurements. Intraclass correlation coefficients (ICCs) and Bland-Altman analyses, were used to assess agreement between MR and anatomical measurements.</div></div><div><h3>Results</h3><div>A total of 84 paired cartilage areas were analyzed [cartilage thickness measurements ranged from 0 to 3.40 mm at anatomical evaluation (mean, 1.08 mm ± 0.83)]. The agreements between MR and anatomical measurements were excellent (mean differences, 0.06 ± 0.31 mm for R1 and 0.03 ± 0.43 mm for R2) with respective ICC values of 0.93 and 0.88. Bland-Altman analyses revealed small differences between MR and anatomical measurements, with 95 % Limit of Agreements values falling within clinically acceptable ranges (−0.54 to 0.66 mm for R1, −0.87 to 0.80 mm for R2).</div></div><div><h3>Conclusion</h3><div>The 3D PDw FSE sequence with Compressed Sensing acceleration technique demonstrated reliable and accurate assessment of cartilage thickness of the tibial condyles within a timeframe suitable for routine clinical practice.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110321"},"PeriodicalIF":2.1,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142965536","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}