{"title":"DIM: A diffusion instability measure for MRI quality assurance","authors":"Tim Schmidt, Zoltan Nagy","doi":"10.1002/mrm.70012","DOIUrl":"10.1002/mrm.70012","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The aim of the presented work is to develop a diffusion instability measure (DIM) that can be used in quality assurance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Single-shot, spin-echo, echo-planar imaging HARDI diffusion data sets were collected on a spherical silicone oil phantom with 64 different diffusion directions on a 3T Philips Achieva and a 3T Siemens Cima.X scanner with similar acquisition protocols. A few data sets on the Philips Achieva included concurrent magnetic field monitoring. A correlation coefficient matrix among the diffusion directions for each data set was calculated, and subsequently, one minus its eigenratio was defined as the DIM.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The DIM values ranged between about 5000 and 280 000 ppm across the data sets. The worst and best image quality—and hence the highest and lowest DIM values—were observed for b-value = 4000 s/mm<sup>2</sup> on the Philips Achieva and for b-value = 500 s/mm<sup>2</sup> on the Siemens Cima.X without concurrent field monitoring, respectively.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>DIM is a sensitive tool for evaluating image quality in HARDI scans on a quantitative basis. It is simple to implement without the need for hardware or software modifications.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":"94 6","pages":"2624-2631"},"PeriodicalIF":3.0,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144816993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Brian Toner, Simon Arberet, Shu Zhang, Fei Han, Eze Ahanonu, Ute Goerke, Kevin Johnson, Zeyad Abouelfetouh, Ion Codreanu, Sajeev Sridhar, Hina Arif-Tiwari, Vibhas Deshpande, Diego R. Martin, Mariappan Nadar, Maria I. Altbach, Ali Bilgin
{"title":"Accelerated free-breathing abdominal T2 mapping with deep learning reconstruction of radial turbo spin-echo data","authors":"Brian Toner, Simon Arberet, Shu Zhang, Fei Han, Eze Ahanonu, Ute Goerke, Kevin Johnson, Zeyad Abouelfetouh, Ion Codreanu, Sajeev Sridhar, Hina Arif-Tiwari, Vibhas Deshpande, Diego R. Martin, Mariappan Nadar, Maria I. Altbach, Ali Bilgin","doi":"10.1002/mrm.70017","DOIUrl":"10.1002/mrm.70017","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To accelerate respiratory triggered free-breathing T2 mapping of the abdomen while maintaining high-quality anatomical images, accurate T2 maps, and fast reconstruction times.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We developed a flexible deep learning framework that can be trained in a fully supervised manner to improve T2-weighted images or in a self-supervised manner to reconstruct T2 maps.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>For retrospectively undersampled data, anatomical images and T2 maps reconstructed by the proposed deep learning method demonstrated reduced voxel-wise error compared to existing traditional and compressed sensing techniques. Reconstruction times were approximately 1 s per slice, significantly faster than existing compressed sensing techniques. Prospectively undersampled data were also acquired to assess the model.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The proposed deep-learning framework reconstructed high-quality anatomical images and accurate T2 maps from datasets undersampled to only 160 total radial views (5 views per echo time), enabling full liver coverage in under three minutes on average with per-slice reconstruction times of approximately one second.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":"94 6","pages":"2475-2491"},"PeriodicalIF":3.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144784629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qianxue Shan, Ziqiang Yu, Baiyan Jiang, Jian Hou, Qiuyi Shen, Winnie Chiu Wing Chu, Vincent Wai Sun Wong, Weitian Chen
{"title":"Quantitative macromolecular proton fraction imaging using pulsed spin-lock","authors":"Qianxue Shan, Ziqiang Yu, Baiyan Jiang, Jian Hou, Qiuyi Shen, Winnie Chiu Wing Chu, Vincent Wai Sun Wong, Weitian Chen","doi":"10.1002/mrm.70021","DOIUrl":"10.1002/mrm.70021","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Recent studies have shown that spin-lock MRI can simplify quantitative magnetization transfer (MT) by eliminating its dependency on water pool parameters, removing the need for a T1 map in macromolecular proton fraction (MPF) quantification. However, its application is often limited by the requirement for long radiofrequency (RF) pulse durations, which are constrained by RF hardware capabilities despite remaining within specific absorption rate (SAR) safety limits.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>To address this challenge, we propose a novel method, MPF mapping using pulsed spin-lock (MPF-PSL). MPF-PSL employs a pulsed spin-lock train with intermittent free precession periods, enabling extended total spin-lock durations without exceeding hardware and specific absorption rate limits. A comprehensive analytical framework was developed to model the magnetization dynamics of the two-pool MT system under pulsed spin-lock, demonstrating that MPF-PSL achieves MT-specific quantification while minimizing confounding effects from the water pool. The proposed method is validated with Bloch–McConnell simulations, phantoms, and in vivo studies at 3T.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Both Bloch–McConnell simulations and phantom validation demonstrated that MPF-PSL exhibits insensitivity to water pool parameters while enabling robust MPF quantification. In vivo validation studies confirmed the method's clinical utility in detecting collagen deposition in patients with liver fibrosis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>MPF-PSL presents a practical solution for quantitative MT imaging, with strong potential for clinical applications.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":"94 6","pages":"2492-2507"},"PeriodicalIF":3.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144784632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pedram Yazdanbakhsh, Marcus J. Couch, Christian Sprang, Kyle M. Gilbert, Sajjad Feizollah, Christine L. Tardif, David A. Rudko
{"title":"A size-adaptive RF coil for MRI of the pediatric human brain at 7 T","authors":"Pedram Yazdanbakhsh, Marcus J. Couch, Christian Sprang, Kyle M. Gilbert, Sajjad Feizollah, Christine L. Tardif, David A. Rudko","doi":"10.1002/mrm.70011","DOIUrl":"10.1002/mrm.70011","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The purpose of this work was to design and build a size-adaptive pediatric RF head coil for 7 T neuroimaging. The coil can be safely applied for imaging children 4–9 years old.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The pediatric head coil incorporates eight, transmit dipole elements for operation in parallel transmit (pTx) mode. The receive architecture is comprised of a 32-channel conformal, size-adaptive receive array. Receive elements were arranged into five sections of a mechanically adjustable 3D printed head former, allowing adjustment of the receive array according to child head size. The transmit coil was carefully simulated to calculate specific absorption rate (SAR) and B<sub>1</sub><sup>+</sup> efficiency. Coil performance was then evaluated with a pediatric head phantom at both the largest and smallest dimensions of the receive former. In vivo imaging was carried out in 3 pediatric subjects (aged 5, 6, and 9 years old) to acquire B<sub>1</sub><sup>+</sup> field maps and anatomical MP2RAGE images.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A comparison of simulated and experimental B<sub>1</sub><sup>+</sup> performance in the pediatric head phantom was used to validate SAR models and to demonstrate that the coil was safe for pediatric imaging. The SNR performance in the pediatric phantom was improved by adjusting the position of the receive array to the smallest possible position. The in vivo B<sub>1</sub><sup>+</sup> efficiency agreed with expectations, and the coil provided precise anatomical images of the brain.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The proposed size-adaptive coil enables safe, high-quality imaging of children at 7 T, with a range of ages and head sizes. Accurate SAR modeling enabled imaging using both combined circularly polarized and dynamic pTx modes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":"94 6","pages":"2771-2784"},"PeriodicalIF":3.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144784628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Junru Zhong, Chaoxing Huang, Ziqiang Yu, Fan Xiao, Thierry Blu, Siyue Li, Tim-Yun Michael Ong, Ki-Wai Kevin Ho, Queenie Chan, James F. Griffith, Weitian Chen
{"title":"Utilizing 3D fast spin echo anatomical imaging to reduce the number of contrast preparations in \u0000 \u0000 \u0000 \u0000 \u0000 T\u0000 \u0000 \u0000 1\u0000 ρ\u0000 \u0000 \u0000 \u0000 $$ {T}_{1rho } $$\u0000 quantification of knee cartilage using learning-based methods","authors":"Junru Zhong, Chaoxing Huang, Ziqiang Yu, Fan Xiao, Thierry Blu, Siyue Li, Tim-Yun Michael Ong, Ki-Wai Kevin Ho, Queenie Chan, James F. Griffith, Weitian Chen","doi":"10.1002/mrm.70022","DOIUrl":"10.1002/mrm.70022","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To propose and evaluate an accelerated <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>T</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 <mi>ρ</mi>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {T}_{1rho } $$</annotation>\u0000 </semantics></math> quantification method that combines <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>T</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 <mi>ρ</mi>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {T}_{1rho } $$</annotation>\u0000 </semantics></math>-weighted fast spin echo (FSE) images and proton density (PD)-weighted anatomical FSE images, leveraging deep learning models for <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>T</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 <mi>ρ</mi>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {T}_{1rho } $$</annotation>\u0000 </semantics></math> mapping. The goal is to reduce scan time and facilitate integration into routine clinical workflows for osteoarthritis (OA) assessment.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This retrospective study utilized MRI data from 40 participants (30 OA patients and 10 healthy volunteers). A volume of PD-weighted anatomical FSE images and a volume of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>T</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 <mi>ρ</mi>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 ","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":"94 6","pages":"2745-2757"},"PeriodicalIF":3.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144784627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}