Magnetic Resonance in Medicine最新文献

{"title":"A steady-state MR fingerprinting sequence optimization framework applied to the fast 3D quantification of fat fraction and water T1 in the thigh muscles","authors":"Constantin Slioussarenko, Pierre-Yves Baudin, Benjamin Marty","doi":"10.1002/mrm.30490","DOIUrl":"10.1002/mrm.30490","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The aim of this study was to develop an optimization framework to shorten GRE-based MRF sequences while keeping similar parameter estimation quality.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>An optimization framework taking into account steady-state initial longitudinal magnetization, undersampling artifacts, and mitigating overfitting by drawing from a realistic numerical thighs phantom database was developed and validated on numerical simulations and 10 healthy volunteers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The sequences optimized with the proposed framework decreased the original sequence duration by 30% (8 s per repetition instead of 11.2 s) while showing improved accuracy (SSIM going up from 96% to 99% for <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>F</mi>\u0000 <mi>F</mi>\u0000 </mrow>\u0000 <annotation>$$ FF $$</annotation>\u0000 </semantics></math>, from 93% to 96% for <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>T</mi>\u0000 <msub>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>H</mi>\u0000 <mn>2</mn>\u0000 <mi>O</mi>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ T{1}_{H2O} $$</annotation>\u0000 </semantics></math> on numerical simulations) and precision, especially when compared with sequences optimized through other means.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The proposed framework paves the way for fast 3D quantification of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>F</mi>\u0000 <mi>F</mi>\u0000 </mrow>\u0000 <annotation>$$ FF $$</annotation>\u0000 </semantics></math> and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>T</mi>\u0000 <msub>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 <mrow>\u0000 ","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":"93 6","pages":"2623-2639"},"PeriodicalIF":3.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.30490","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143542458","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}

{"title":"Free-breathing hepatic 2D magnetic resonance elastography","authors":"Jiahui Li,&nbsp;Marzanna Obrzut,&nbsp;Jie Chen,&nbsp;Bogdan Dzyubak,&nbsp;Kevin J. Glaser,&nbsp;Ziying Yin,&nbsp;Jun Chen,&nbsp;Yuan Le,&nbsp;Alina M. Allen,&nbsp;Sudhakar K. Venkatesh,&nbsp;Armando Manduca,&nbsp;Vijay H. Shah,&nbsp;Richard L. Ehman,&nbsp;Meng Yin","doi":"10.1002/mrm.30483","DOIUrl":"10.1002/mrm.30483","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To evaluate the test–retest repeatability of a rapid, free-breathing two-dimensional (2D) MR elastography (MRE) technique and to assess the reliability of the liver-stiffness measurements compared with conventional breath-hold MREs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Fifteen and 115 participants were enrolled in the technical repeatability and measurement equivalence assessment cohorts, respectively. All participants underwent rapid free-breathing and conventional breath-hold 2D MRE (twice in repeatability cohort) on 1.5T scanners. Both methods have four phase offsets over one harmonic motion cycle at 60 Hz; one and 10 cycles were collected and processed in breath-hold and free-breathing MREs, respectively. The liver stiffness measurements of free-breathing (LS_F) and breath-hold MRE (LS_B) were calculated from manually drawn regions of interest. The repeatability coefficients and Spearman correlation were used to assess technical repeatability and measurement agreement between LS_F and LS_B. Univariable and multivariable linear regressions were performed to identify potential influencing factors, including age, sex, body mass index, and fat fraction, in the measurement agreement between LS_B and LS_F.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The repeatability coefficient of free-breathing 2D MRE is comparable to breath-hold MRE (LS_F: 20.8%, LS_B: 20.4%). LS_F showed a strong agreement and significant correlation with LS_B (LS_F = 1.01 × LS_B, <i>ρ</i> = 0.94, <i>p</i> &lt; 0.001). The measurement agreement between LS_F and LS_B was only significantly affected by sex (<i>p</i> = 0.047) after adjusting for confounding factors of age, body mass index, and fat fraction.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The nongated, free-breathing, multislice 2D MRE technique can provide reliable liver stiffness measurements compared with conventional breath-hold 2D MRE. It could provide a comfortable alternative method with reliable liver stiffness measurements for patients who have difficulty in suspending respiration.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":"93 6","pages":"2434-2443"},"PeriodicalIF":3.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143542465","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}

{"title":"Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 2—Ex vivo imaging: Added value and acquisition","authors":"Kurt G. Schilling,&nbsp;Francesco Grussu,&nbsp;Andrada Ianus,&nbsp;Brian Hansen,&nbsp;Amy F. D. Howard,&nbsp;Rachel L. C. Barrett,&nbsp;Manisha Aggarwal,&nbsp;Stijn Michielse,&nbsp;Fatima Nasrallah,&nbsp;Warda Syeda,&nbsp;Nian Wang,&nbsp;Jelle Veraart,&nbsp;Alard Roebroeck,&nbsp;Andrew F. Bagdasarian,&nbsp;Cornelius Eichner,&nbsp;Farshid Sepehrband,&nbsp;Jan Zimmermann,&nbsp;Lucas Soustelle,&nbsp;Christien Bowman,&nbsp;Benjamin C. Tendler,&nbsp;Andreea Hertanu,&nbsp;Ben Jeurissen,&nbsp;Marleen Verhoye,&nbsp;Lucio Frydman,&nbsp;Yohan van de Looij,&nbsp;David Hike,&nbsp;Jeff F. Dunn,&nbsp;Karla Miller,&nbsp;Bennett A. Landman,&nbsp;Noam Shemesh,&nbsp;Adam Anderson,&nbsp;Emilie McKinnon,&nbsp;Shawna Farquharson,&nbsp;Flavio Dell'Acqua,&nbsp;Carlo Pierpaoli,&nbsp;Ivana Drobnjak,&nbsp;Alexander Leemans,&nbsp;Kevin D. Harkins,&nbsp;Maxime Descoteaux,&nbsp;Duan Xu,&nbsp;Hao Huang,&nbsp;Mathieu D. Santin,&nbsp;Samuel C. Grant,&nbsp;Andre Obenaus,&nbsp;Gene S. Kim,&nbsp;Dan Wu,&nbsp;Denis Le Bihan,&nbsp;Stephen J. Blackband,&nbsp;Luisa Ciobanu,&nbsp;Els Fieremans,&nbsp;Ruiliang Bai,&nbsp;Trygve B. Leergaard,&nbsp;Jiangyang Zhang,&nbsp;Tim B. Dyrby,&nbsp;G. Allan Johnson,&nbsp;Julien Cohen-Adad,&nbsp;Matthew D. Budde,&nbsp;Ileana O. Jelescu","doi":"10.1002/mrm.30435","DOIUrl":"10.1002/mrm.30435","url":null,"abstract":"<p>The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher SNR and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents “Part 2” of a three-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":"93 6","pages":"2535-2560"},"PeriodicalIF":3.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.30435","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143542461","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}

{"title":"Repeatability-encouraging self-supervised learning reconstruction for quantitative MRI.","authors":"Zihao Chen, Zheyuan Hu, Yibin Xie, Debiao Li, Anthony G Christodoulou","doi":"10.1002/mrm.30478","DOIUrl":"10.1002/mrm.30478","url":null,"abstract":"<p><strong>Purpose: </strong>The clinical value of quantitative MRI hinges on its measurement repeatability. Deep learning methods to reconstruct undersampled quantitative MRI can accelerate reconstruction but do not aim to promote quantitative repeatability. This study proposes a repeatability-encouraging self-supervised learning (SSL) reconstruction method for quantitative MRI.</p><p><strong>Methods: </strong>The proposed SSL reconstruction network minimized cross-data-consistency between two equally sized, mutually exclusive temporal subsets of k-t-space data, encouraging repeatability by enabling each subset's reconstruction to predict the other's k-t-space data. The method was evaluated on cardiac MR Multitasking T₁ mapping data and compared with supervised learning methods trained on full 60-s inputs (Sup60) and on split 30-s inputs (Sup30/30). Reconstruction quality was evaluated on full 60-s inputs, comparing results to iterative wavelet-regularized references using Bland-Altman limits of agreement (LOAs). Repeatability was evaluated by splitting the 60-s data into two 30-s inputs, evaluating T₁ differences between reconstructions from the two halves of the scan.</p><p><strong>Results: </strong>On 60-s inputs, the proposed method produced comparable-quality images and T₁ maps to the Sup60 method, with T₁ values in general agreement with the wavelet reference (LOA Sup60 = ±75 ms, SSL = ±81 ms), whereas the Sup30/30 method generated blurrier results and showed poor T₁ agreement (LOA Sup30/30 = ±132 ms). On back-to-back 30-s inputs, the proposed method had the best T₁ repeatability (coefficient of variation SSL = 6.3%, Sup60 = 12.0%, Sup30/30 = 6.9%). Of the three deep learning methods, only the SSL method produced sharp and repeatable images.</p><p><strong>Conclusion: </strong>Without the need for labeled training data, the proposed SSL method demonstrated superior repeatability compared with supervised learning without sacrificing sharpness, and reduced reconstruction time versus iterative methods.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143523870","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}

{"title":"High-resolution and high-fidelity diffusion tensor imaging of cervical spinal cord using 3D reduced-FOV multiplexed sensitivity encoding (3D-rFOV-MUSE).","authors":"Chenglang Yuan, Shihui Chen, Liyuan Liang, Xiaorui Xu, Hailin Xiong, Yi Li, Tianbaige Liu, Nan-Kuei Chen, Hing-Chiu Chang","doi":"10.1002/mrm.30455","DOIUrl":"https://doi.org/10.1002/mrm.30455","url":null,"abstract":"<p><strong>Purpose: </strong>To develop a 3D isotropic high-resolution and high-fidelity cervical spinal cord DTI technique for addressing the current challenges existing in 2D cervical spinal cord DTI.</p><p><strong>Methods: </strong>A 3D multi-shot DWI acquisition and reconstruction technique was developed by combining 3D multiplexed sensitivity encoding (3D-MUSE) with two reduced FOV techniques, termed 3D-rFOV-MUSE, to acquire 3D cervical spinal cord DTI data using a sagittal thin slab. A self-referenced 2D ghost correction method and a 2D navigator-based inter-shot phase correction were integrated into the reconstruction framework to simultaneously eliminate Nyquist ghost and aliasing artifacts. Cardiac triggering was used during data acquisition to minimize the influence of cerebrospinal fluid pulsation. In vivo experiments were conducted on five healthy subjects at a 1.5 T MRI scanner for evaluating the feasibility of 3D cervical spinal cord DTI using 3D-rFOV-MUSE in terms of geometric fidelity, reconstruction performance, and SNR efficiency.</p><p><strong>Results: </strong>A 3D-rFOV-MUSE can achieve high-resolution cervical spinal cord DTI at 1.0 mm isotropic resolution. The integration of reduced FOV and multi-shot acquisitions can improve the geometric fidelity of 3D cervical spinal cord DTI. Compared with routine 2D single-shot diffusion-weighed EPI (2D-ss-EPI), the proposed technique can mitigate through-plane partial volume effect and enable multi-planar data reformation for cervical spinal cord DTI, with effective reductions of distortions and improved signal-to-noise ratio.</p><p><strong>Conclusion: </strong>We demonstrated the feasibility of high-resolution and high-fidelity 3D cervical spinal cord DTI at 1.0 mm isotropic resolution using 3D-rFOV-MUSE technique, which may potentially improve the quantitative assessment of cervical spinal cord DTI biomarkers.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143523867","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}

{"title":"Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3—Ex vivo imaging: Data processing, comparisons with microscopy, and tractography","authors":"Kurt G. Schilling,&nbsp;Amy F. D. Howard,&nbsp;Francesco Grussu,&nbsp;Andrada Ianus,&nbsp;Brian Hansen,&nbsp;Rachel L. C. Barrett,&nbsp;Manisha Aggarwal,&nbsp;Stijn Michielse,&nbsp;Fatima Nasrallah,&nbsp;Warda Syeda,&nbsp;Nian Wang,&nbsp;Jelle Veraart,&nbsp;Alard Roebroeck,&nbsp;Andrew F. Bagdasarian,&nbsp;Cornelius Eichner,&nbsp;Farshid Sepehrband,&nbsp;Jan Zimmermann,&nbsp;Lucas Soustelle,&nbsp;Christien Bowman,&nbsp;Benjamin C. Tendler,&nbsp;Andreea Hertanu,&nbsp;Ben Jeurissen,&nbsp;Marleen Verhoye,&nbsp;Lucio Frydman,&nbsp;Yohan van de Looij,&nbsp;David Hike,&nbsp;Jeff F. Dunn,&nbsp;Karla Miller,&nbsp;Bennett A. Landman,&nbsp;Noam Shemesh,&nbsp;Adam Anderson,&nbsp;Emilie McKinnon,&nbsp;Shawna Farquharson,&nbsp;Flavio Dell'Acqua,&nbsp;Carlo Pierpaoli,&nbsp;Ivana Drobnjak,&nbsp;Alexander Leemans,&nbsp;Kevin D. Harkins,&nbsp;Maxime Descoteaux,&nbsp;Duan Xu,&nbsp;Hao Huang,&nbsp;Mathieu D. Santin,&nbsp;Samuel C. Grant,&nbsp;Andre Obenaus,&nbsp;Gene S. Kim,&nbsp;Dan Wu,&nbsp;Denis Le Bihan,&nbsp;Stephen J. Blackband,&nbsp;Luisa Ciobanu,&nbsp;Els Fieremans,&nbsp;Ruiliang Bai,&nbsp;Trygve B. Leergaard,&nbsp;Jiangyang Zhang,&nbsp;Tim B. Dyrby,&nbsp;G. Allan Johnson,&nbsp;Julien Cohen-Adad,&nbsp;Matthew D. Budde,&nbsp;Ileana O. Jelescu","doi":"10.1002/mrm.30424","DOIUrl":"10.1002/mrm.30424","url":null,"abstract":"<p>Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high SNR images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a three-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing, and comparisons with microscopy. In each section, we attempt to provide guidelines and recommendations but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing and point toward open-source software and databases specific to small animal and ex vivo imaging.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":"93 6","pages":"2561-2582"},"PeriodicalIF":3.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.30424","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143501839","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}

{"title":"Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging","authors":"Ileana O. Jelescu,&nbsp;Francesco Grussu,&nbsp;Andrada Ianus,&nbsp;Brian Hansen,&nbsp;Rachel L. C. Barrett,&nbsp;Manisha Aggarwal,&nbsp;Stijn Michielse,&nbsp;Fatima Nasrallah,&nbsp;Warda Syeda,&nbsp;Nian Wang,&nbsp;Jelle Veraart,&nbsp;Alard Roebroeck,&nbsp;Andrew F. Bagdasarian,&nbsp;Cornelius Eichner,&nbsp;Farshid Sepehrband,&nbsp;Jan Zimmermann,&nbsp;Lucas Soustelle,&nbsp;Christien Bowman,&nbsp;Benjamin C. Tendler,&nbsp;Andreea Hertanu,&nbsp;Ben Jeurissen,&nbsp;Marleen Verhoye,&nbsp;Lucio Frydman,&nbsp;Yohan van de Looij,&nbsp;David Hike,&nbsp;Jeff F. Dunn,&nbsp;Karla Miller,&nbsp;Bennett A. Landman,&nbsp;Noam Shemesh,&nbsp;Adam Anderson,&nbsp;Emilie McKinnon,&nbsp;Shawna Farquharson,&nbsp;Flavio Dell'Acqua,&nbsp;Carlo Pierpaoli,&nbsp;Ivana Drobnjak,&nbsp;Alexander Leemans,&nbsp;Kevin D. Harkins,&nbsp;Maxime Descoteaux,&nbsp;Duan Xu,&nbsp;Hao Huang,&nbsp;Mathieu D. Santin,&nbsp;Samuel C. Grant,&nbsp;Andre Obenaus,&nbsp;Gene S. Kim,&nbsp;Dan Wu,&nbsp;Denis Le Bihan,&nbsp;Stephen J. Blackband,&nbsp;Luisa Ciobanu,&nbsp;Els Fieremans,&nbsp;Ruiliang Bai,&nbsp;Trygve B. Leergaard,&nbsp;Jiangyang Zhang,&nbsp;Tim B. Dyrby,&nbsp;G. Allan Johnson,&nbsp;Julien Cohen-Adad,&nbsp;Matthew D. Budde,&nbsp;Kurt G. Schilling","doi":"10.1002/mrm.30429","DOIUrl":"10.1002/mrm.30429","url":null,"abstract":"<p>Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":"93 6","pages":"2507-2534"},"PeriodicalIF":3.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.30429","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143501836","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}

{"title":"On the RF safety of titanium mesh head implants in 7 T MRI systems: an investigation.","authors":"Mazin Mustafa, Nur Izzati Huda Zulkarnain, Alireza Sadeghi-Tarakameh, Andrea Grant, David Darrow, Can Ozutemiz, Yigitcan Eryaman","doi":"10.1002/mrm.30477","DOIUrl":"10.1002/mrm.30477","url":null,"abstract":"<p><strong>Purpose: </strong>Patients undergoing craniofacial surgery for skull reconstruction may have titanium mesh implants. The safety risks related to 7 T MRI with these patients are not well understood. This study investigates the RF heating of titanium mesh head implants at 7 T.</p><p><strong>Methods: </strong>A simulation model for a 7 T birdcage head coil was developed and validated against <math> <semantics> <mrow> <mfenced><msubsup><mi>B</mi> <mn>1</mn> <mo>+</mo></msubsup> </mfenced> </mrow> <annotation>$$ left|{B}_1^{+}right| $$</annotation></semantics> </math> , 1 g-averaged specific absorption rate (SAR), and temperature measurements in the presence of a titanium mesh. Various mesh sizes and shapes at different angular positions were simulated to determine the worst-case scenario in a spherical phantom in addition to the effect of rounding the mesh edges. Full-wave electromagnetic and bioheat thermal simulations were conducted on anatomical human models.</p><p><strong>Results: </strong>Preliminary results indicate an increase in the local SAR near the meshes depending on the shape, size, and location. The maximum absolute temperatures in the head were, on average, around 38.2°C after 15 min of RF power exposure, corresponding to 3.2 W/kg whole-head SAR without a titanium mesh implant. The maximum absolute temperatures did not significantly change after introducing the titanium mesh implants, and the highest temperature was 38.4°C, observed near the cerebellum and the facial muscles. The maximum local increase in temperature was observed at the vicinity of the mesh as 2.8°C. Finally, it was shown that large mesh implants can negatively impact <math> <semantics> <mrow> <mfenced><msubsup><mi>B</mi> <mn>1</mn> <mo>+</mo></msubsup> </mfenced> </mrow> <annotation>$$ left|{B}_1^{+}right| $$</annotation></semantics> </math> field.</p><p><strong>Conclusions: </strong>Small rounded titanium mesh head implants can be generally safe for 7 T MRI scans under the standard guidelines. Avoiding sharp corners and edges may reduce the chances of RF safety risks.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143501843","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}

{"title":"3D joint T1/T1ρ/T2 mapping and water-fat imaging for contrast-agent free myocardial tissue characterization at 1.5T","authors":"Michael G. Crabb,&nbsp;Karl P. Kunze,&nbsp;Simon J. Littlewood,&nbsp;Donovan Tripp,&nbsp;Anastasia Fotaki,&nbsp;Claudia Prieto,&nbsp;René M. Botnar","doi":"10.1002/mrm.30397","DOIUrl":"10.1002/mrm.30397","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To develop a novel, free-breathing, 3D joint &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {T}_1 $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;/&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mi&gt;ρ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {T}_{1rho } $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;/&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {T}_2 $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; mapping sequence with Dixon encoding to provide co-registered 3D &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {T}_1 $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mi&gt;ρ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {T}_{1rho } $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 ","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":"93 6","pages":"2297-2310"},"PeriodicalIF":3.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.30397","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143468476","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}

{"title":"Whole liver phase-based R2 mapping in liver iron overload within a breath-hold.","authors":"Daiki Tamada, Ruvini Navaratna, Jayse Merle Weaver, Diego Hernando, Scott B Reeder","doi":"10.1002/mrm.30461","DOIUrl":"https://doi.org/10.1002/mrm.30461","url":null,"abstract":"<p><strong>Purpose: </strong>Diagnosis and treatment monitoring of iron overload increasingly relies on non-invasive MRI-based measurement of liver iron concentration (LIC). Liver R₂ mapping is known to correlate well with LIC. However, traditional spin-echo based R₂ mapping methods have drawbacks such as long acquisition times and limited volumetric coverage. In this work, we present an optimized phase-based R₂ mapping method to quantify whole-liver R₂ in iron overload patients within a single breathhold.</p><p><strong>Theory and methods: </strong>A recently developed phase-based R₂ mapping method is optimized in this study to improve estimation of high R₂ values using reduced TR, spatial averaging, and R₁ correction. Using Bloch equation simulations, the proposed optimization method was evaluated. Furthermore, the impact of fat on R₂ bias was investigated through simulations. The feasibility of the optimized phase-based R₂ method was assessed in healthy volunteers and patients with iron overload and compared to reference STEAM-MRS R₂ measurements.</p><p><strong>Results: </strong>Simulations demonstrate that a shorter TR extends the dynamic range of R₂ estimation to higher values and that averaging of signal phase before R₂ estimation is necessary when R₂ is high. Phantom experiments also demonstrate reduced phase-based R₂ bias using R₁ correction. Good agreement (1.5 T: r² = 0.76, 3.0 T: r² = 0.70) between the modified phase-based R₂ method and reference STEAM R₂ was found in healthy volunteers and iron overload patients over a wide range of LIC values.</p><p><strong>Conclusion: </strong>This study demonstrates the feasibility of the proposed phase-based R₂ method to accurately measure whole-liver R₂ mapping in severe iron overloaded patients during a single breathhold.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143440471","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}