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Adanyeguh IM, Park YW, Henry PG, Deelchand DK. B 0-insensitive image navigators for prospective motion-corrected MRS with localized second-order shimming. Magn Reson Med 2024; 92:1338-1347. [PMID: 38704666 PMCID: PMC11262980 DOI: 10.1002/mrm.30151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/19/2024] [Accepted: 04/23/2024] [Indexed: 05/06/2024]
Abstract
PURPOSE Localized shimming in single-voxel MRS often results in large B0 inhomogeneity outside the volume-of-interest. This causes unacceptable degradation in motion navigator images. Switching back and forth between whole-brain shim and localized shim is possible for linear shims, but not for higher-order shims. Here we propose motion navigators largely insensitive to B0 inhomogeneity for prospective motion-corrected MRS with localized higher-order shimming. METHODS A recent fast high-resolution motion navigator based on spiral-in/out k-space trajectories and multislice-to-volume registration was modified by splitting the readout into multiple shot interleaves which shortened the echo time and reduced the effect of B0 inhomogeneity. The performance of motion correction was assessed in healthy subjects in the prefrontal cortex using a sLASER sequence at 3T (N = 5) and 7T (N = 5). RESULTS With multiple spatial interleaves, excellent quality navigator images were acquired in the whole brain in spite of large B0 inhomogeneity outside the MRS voxel. The total duration of the navigator in sLASER remained relatively short even with multiple shots (3T: 10 spatial interleaves 94 ms per slice; 7T: 15 spatial interleaves 103 ms per slice). Prospective motion correction using the multi-shot navigators yielded comparable spectral quality (water linewidth and metabolite SNR) with and without subject motion. CONCLUSION B0-insensitive motion navigators enable prospective motion correction for MRS with all first- and second-order shims adjusted in the MRS voxel, providing optimal spectral linewidth.
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Affiliation(s)
- Isaac M Adanyeguh
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota Medical School, Minneapolis, Minnesota, USA
| | - Young Woo Park
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota Medical School, Minneapolis, Minnesota, USA
| | - Pierre-Gilles Henry
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota Medical School, Minneapolis, Minnesota, USA
| | - Dinesh K Deelchand
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota Medical School, Minneapolis, Minnesota, USA
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Heckel R, Jacob M, Chaudhari A, Perlman O, Shimron E. Deep learning for accelerated and robust MRI reconstruction. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01173-8. [PMID: 39042206 DOI: 10.1007/s10334-024-01173-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/24/2024] [Accepted: 05/28/2024] [Indexed: 07/24/2024]
Abstract
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. It explores end-to-end neural networks, pre-trained and generative models, and self-supervised methods, and highlights their contributions to overcoming traditional MRI limitations. It also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling biases. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.Affiliations [3 and 6] has been split into two different affiliations. Please check if action taken is appropriate and amend if necessary.looks good.
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Affiliation(s)
- Reinhard Heckel
- Department of computer engineering, Technical University of Munich, Munich, Germany
| | - Mathews Jacob
- Department of Electrical and Computer Engineering, University of Iowa, Iowa, 52242, IA, USA
| | - Akshay Chaudhari
- Department of Radiology, Stanford University, Stanford, 94305, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, CA, USA
| | - Or Perlman
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Efrat Shimron
- Department of Electrical and Computer Engineering, Technion-Israel Institute of Technology, Haifa, 3200004, Israel.
- Department of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa, 3200004, Israel.
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3
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Jalnefjord O, Rosenqvist L, Warsame A, Björkman-Burtscher IM. Signal drift in diffusion MRI of the brain: effects on intravoxel incoherent motion parameter estimates. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01183-6. [PMID: 39003384 DOI: 10.1007/s10334-024-01183-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 06/19/2024] [Accepted: 06/20/2024] [Indexed: 07/15/2024]
Abstract
OBJECTIVES Signal drift has been put forward as one of the fundamental confounding factors in diffusion MRI (dMRI) of the brain. This study characterizes signal drift in dMRI of the brain, evaluates correction methods, and exemplifies its impact on parameter estimation for three intravoxel incoherent motion (IVIM) protocols. MATERIALS AND METHODS dMRI of the brain was acquired in ten healthy subjects using protocols designed to enable retrospective characterization and correction of signal drift. All scans were acquired twice for repeatability analysis. Three temporal polynomial correction methods were evaluated: (1) global, (2) voxelwise, and (3) spatiotemporal. Effects of acquisition order were simulated using estimated drift fields. RESULTS Signal drift was around 2% per 5 min in the brain as a whole, but reached above 5% per 5 min in the frontal regions. Only correction methods taking spatially varying signal drift into account could achieve effective corrections. Altered acquisition order introduced both systematic changes and differences in repeatability in the presence of signal drift. DISCUSSION Signal drift in dMRI of the brain was found to be spatially varying, calling for correction methods taking this into account. Without proper corrections, choice of protocol can affect dMRI parameter estimates and their repeatability.
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Affiliation(s)
- Oscar Jalnefjord
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, MRI Center, Bruna Stråket 13, 413 45, Gothenburg, Sweden.
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden.
| | - Louise Rosenqvist
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, MRI Center, Bruna Stråket 13, 413 45, Gothenburg, Sweden
| | - Amina Warsame
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, MRI Center, Bruna Stråket 13, 413 45, Gothenburg, Sweden
| | - Isabella M Björkman-Burtscher
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Radiology, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
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Yang H, Wang G, Li Z, Li H, Zheng J, Hu Y, Cao X, Liao C, Ye H, Tian Q. Artificial intelligence for neuro MRI acquisition: a review. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01182-7. [PMID: 38922525 DOI: 10.1007/s10334-024-01182-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 06/11/2024] [Accepted: 06/14/2024] [Indexed: 06/27/2024]
Abstract
OBJECT To review recent advances of artificial intelligence (AI) in enhancing the efficiency and throughput of the MRI acquisition workflow in neuroimaging, including planning, sequence design, and correction of acquisition artifacts. MATERIALS AND METHODS A comprehensive analysis was conducted on recent AI-based methods in neuro MRI acquisition. The study focused on key technological advances, their impact on clinical practice, and potential risks associated with these methods. RESULTS The findings indicate that AI-based algorithms have a substantial positive impact on the MRI acquisition process, improving both efficiency and throughput. Specific algorithms were identified as particularly effective in optimizing acquisition steps, with reported improvements in workflow efficiency. DISCUSSION The review highlights the transformative potential of AI in neuro MRI acquisition, emphasizing the technological advances and clinical benefits. However, it also discusses potential risks and challenges, suggesting areas for future research to mitigate these concerns and further enhance AI integration in MRI acquisition.
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Affiliation(s)
- Hongjia Yang
- School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Guanhua Wang
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Ziyu Li
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Haoxiang Li
- School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Jialan Zheng
- School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Yuxin Hu
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Xiaozhi Cao
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Congyu Liao
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Huihui Ye
- State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China
| | - Qiyuan Tian
- School of Biomedical Engineering, Tsinghua University, Beijing, China.
- Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China.
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5
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Luo Q, Sun K, Dan G, Zhou XJ. Fast 3D fMRI acquisition with high spatial resolutions over a reduced FOV. Magn Reson Med 2024. [PMID: 38888135 DOI: 10.1002/mrm.30191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 04/29/2024] [Accepted: 05/21/2024] [Indexed: 06/20/2024]
Abstract
PURPOSE To develop and demonstrate a fast 3D fMRI acquisition technique with high spatial resolution over a reduced FOV, named k-t 3D reduced FOV imaging (3D-rFOVI). METHODS Based on 3D gradient-echo EPI, k-t 3D-rFOVI used a 2D RF pulse to reduce the FOV in the in-plane phase-encoding direction, boosting spatial resolution without increasing echo train length. For image acceleration, full sampling was applied in the central k-space region along the through-slab direction (kz) for all time frames, while randomized undersampling was used in outer kz regions at different time frames. Images were acquired at 3T and reconstructed using a method based on partial separability. fMRI detection sensitivity of k-t 3D-rFOVI was quantitively analyzed with simulation data. Human visual fMRI experiments were performed to evaluate k-t 3D-rFOVI and compare it with a commercial multiband EPI sequence. RESULTS The simulation data showed that k-t 3D-rFOVI can detect 100% of fMRI activations with an acceleration factor (R) of 2 and ˜80% with R = 6. In the human fMRI data acquired with 1.5-mm spatial resolution and 800-ms volume TR (TRvol), k-t 3D-rFOVI with R = 4 detected 46% more activated voxels in the visual cortex than the multiband EPI. Additional fMRI experiments showed that k-t 3D-rFOVI can achieve TRvol of 480 ms with R = 6, while reliably detecting visual activation. CONCLUSIONS k-t 3D-rFOVI can simultaneously achieve a high spatial resolution (1.5-mm isotropically) and short TRvol (480-ms) at 3T. It offers a robust acquisition technique for fast fMRI studies over a focused brain volume.
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Affiliation(s)
- Qingfei Luo
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Kaibao Sun
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Guangyu Dan
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Xiaohong Joe Zhou
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Neurosurgery, University of Illinois at Chicago, Chicago, Illinois, USA
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Wallimann P, Piccirelli M, Nowakowska S, Armstrong T, Mayinger M, Boss A, Bink A, Guckenberger M, Tanadini-Lang S, Andratschke N, Pouymayou B. Validation of echo planar imaging based diffusion-weighted magnetic resonance imaging on a 0.35 T MR-Linac. Phys Imaging Radiat Oncol 2024; 30:100579. [PMID: 38707628 PMCID: PMC11068927 DOI: 10.1016/j.phro.2024.100579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 03/08/2024] [Accepted: 04/17/2024] [Indexed: 05/07/2024] Open
Abstract
Background and Purpose The feasibility of acquiring diffusion-weighted imaging (DWI) images on an MR-Linac for quantitative response assessment during radiotherapy was explored. DWI data obtained with a Spin Echo Echo Planar Imaging sequence adapted for a 0.35 T MR-Linac were examined and compared with DWI data from a conventional 3 T scanner. Materials and Methods Apparent diffusion coefficient (ADC) measurements and a distortion correction technique were investigated using DWI-calibrated phantoms and in the brains of seven volunteers. All DWI utilized two phase-encoding directions for distortion correction and off-resonance field estimation. ADC maps in the brain were analyzed for automatically segmented normal tissues. Results Phantom ADC measurements on the MR-Linac were within a 3 % margin of those recorded by the 3 T scanner. The maximum distortion observed in the phantom was 2.0 mm prior to correction and 1.1 mm post-correction on the MR-Linac, compared to 6.0 mm before correction and 3.6 mm after correction at 3 T. In vivo, the average ADC values for gray and white matter exhibited variations of 14 % and 4 %, respectively, for different selections of b-values on the MR-Linac. Distortions in brain images before correction, estimated through the off-resonance field, reached 2.7 mm on the MR-Linac and 12 mm at 3 T. Conclusion Accurate ADC measurements are achievable on a 0.35 T MR-Linac, both in phantom and in vivo. The selection of b-values significantly influences ADC values in vivo. DWI on the MR-Linac demonstrated lower distortion levels, with a maximum distortion reduced to 1.1 mm after correction.
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Affiliation(s)
- Philipp Wallimann
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Marco Piccirelli
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Sylwia Nowakowska
- Institute for Diagnostic and Interventional Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Tess Armstrong
- ViewRay Inc., 2 Thermo Fisher Way, Oakwood Village, OH 44146, USA
| | - Michael Mayinger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andreas Boss
- Institute for Diagnostic and Interventional Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
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7
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Jagadeesan B, Tariq F, Nada A, Bhatti IA, Masood K, Siddiq F. Principles Behind 4D Time-Resolved MRA/Dynamic MRA in Neurovascular Imaging. Semin Roentgenol 2024; 59:191-202. [PMID: 38880517 DOI: 10.1053/j.ro.2024.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 02/28/2024] [Indexed: 06/18/2024]
Affiliation(s)
- Bharathi Jagadeesan
- Departments of Radiology, Neurology and Neurosurgery, University of Minnesota, Minneapolis, MN.
| | - Farzana Tariq
- Departments of Neurosurgery and Radiology, University of Missouri, Columbia, MO
| | - Ayman Nada
- Departments of Neurosurgery and Radiology, University of Missouri, Columbia, MO
| | - Ibrahim A Bhatti
- Departments of Neurosurgery and Radiology, University of Missouri, Columbia, MO
| | - Kamran Masood
- Departments of Radiology, Neurology and Neurosurgery, University of Minnesota, Minneapolis, MN
| | - Farhan Siddiq
- Departments of Neurosurgery and Radiology, University of Missouri, Columbia, MO
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8
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Chaomulige, Matsuo T, Sugimoto K, Miyaji M, Hosoya O, Ueda M, Kobayashi R, Horii T, Hatada I. Morphometric Analysis of the Eye by Magnetic Resonance Imaging in MGST2-Gene-Deficient Mice. Biomedicines 2024; 12:370. [PMID: 38397974 PMCID: PMC10887158 DOI: 10.3390/biomedicines12020370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/23/2024] [Accepted: 02/01/2024] [Indexed: 02/25/2024] Open
Abstract
Strabismus, a neuro-ophthalmological condition characterized by misalignment of the eyes, is a common ophthalmic disorder affecting both children and adults. In our previous study, we identified the microsomal glutathione S-transferase 2 (MGST2) gene as one of the potential candidates for comitant strabismus susceptibility in a Japanese population. The MGST2 gene belongs to the membrane-associated protein involved in the generation of pro-inflammatory mediators, and it is also found in the protection against oxidative stress by decreasing the reactivity of oxidized lipids. To look for the roles of the MGST2 gene in the development, eye alignment, and overall morphology of the eye as the possible background of strabismus, MGST2 gene knockout (KO) mice were generated by CRISPR/Cas9-mediated gene editing with guide RNAs targeting the MGST2 exon 2. The ocular morphology of the KO mice was analyzed through high-resolution images obtained by a magnetic resonance imaging (MRI) machine for small animals. The morphometric analyses showed that the height, width, and volume of the eyeballs in MGST2 KO homozygous mice were significantly greater than those of wild-type mice, indicating that the eyes of MGST2 KO homozygous mice were significantly enlarged. There were no significant differences in the axis length and axis angle. These morphological changes may potentially contribute to the development of a subgroup of strabismus.
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Affiliation(s)
- Chaomulige
- Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama 700-8558, Japan;
| | - Toshihiko Matsuo
- Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama 700-8558, Japan;
- Department of Ophthalmology, Okayama University Hospital, Okayama 700-8558, Japan
| | - Kohei Sugimoto
- Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama 700-8558, Japan;
| | - Mary Miyaji
- Department of Medical Neurobiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan; (M.M.); (O.H.)
| | - Osamu Hosoya
- Department of Medical Neurobiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan; (M.M.); (O.H.)
| | - Masashi Ueda
- Department of Biofunctional Imaging Analysis, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University, Okayama 700-8530, Japan;
| | - Ryosuke Kobayashi
- Biosignal Genome Resource Center, Institute for Molecular and Cellular Regulation, Gunma University, Maebashi 371-8512, Japan; (R.K.); (T.H.); (I.H.)
| | - Takuro Horii
- Biosignal Genome Resource Center, Institute for Molecular and Cellular Regulation, Gunma University, Maebashi 371-8512, Japan; (R.K.); (T.H.); (I.H.)
| | - Izuho Hatada
- Biosignal Genome Resource Center, Institute for Molecular and Cellular Regulation, Gunma University, Maebashi 371-8512, Japan; (R.K.); (T.H.); (I.H.)
- Viral Vector Core, Gunma University Initiative for Advanced Research (GIAR), Maebashi 371-8511, Japan
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9
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Tian Y, Nayak KS. New clinical opportunities of low-field MRI: heart, lung, body, and musculoskeletal. MAGMA (NEW YORK, N.Y.) 2024; 37:1-14. [PMID: 37902898 PMCID: PMC10876830 DOI: 10.1007/s10334-023-01123-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 09/28/2023] [Accepted: 10/05/2023] [Indexed: 11/01/2023]
Abstract
Contemporary whole-body low-field MRI scanners (< 1 T) present new and exciting opportunities for improved body imaging. The fundamental reason is that the reduced off-resonance and reduced SAR provide substantially increased flexibility in the design of MRI pulse sequences. Promising body applications include lung parenchyma imaging, imaging adjacent to metallic implants, cardiac imaging, and dynamic imaging in general. The lower cost of such systems may make MRI favorable for screening high-risk populations and population health research, and the more open configurations allowed may prove favorable for obese subjects and for pregnant women. This article summarizes promising body applications for contemporary whole-body low-field MRI systems, with a focus on new platforms developed within the past 5 years. This is an active area of research, and one can expect many improvements as MRI physicists fully explore the landscape of pulse sequences that are feasible, and as clinicians apply these to patient populations.
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Affiliation(s)
- Ye Tian
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, 3740 McClintock Ave, EEB 406, Los Angeles, CA, 90089-2564, USA.
| | - Krishna S Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, 3740 McClintock Ave, EEB 406, Los Angeles, CA, 90089-2564, USA
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10
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Van AN, Montez DF, Laumann TO, Suljic V, Madison T, Baden NJ, Ramirez-Perez N, Scheidter KM, Monk JS, Whiting FI, Adeyemo B, Chauvin RJ, Krimmel SR, Metoki A, Rajesh A, Roland JL, Salo T, Wang A, Weldon KB, Sotiras A, Shimony JS, Kay BP, Nelson SM, Tervo-Clemmens B, Marek SA, Vizioli L, Yacoub E, Satterthwaite TD, Gordon EM, Fair DA, Tisdall MD, Dosenbach NU. Framewise multi-echo distortion correction for superior functional MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.28.568744. [PMID: 38077010 PMCID: PMC10705259 DOI: 10.1101/2023.11.28.568744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Functional MRI (fMRI) data are severely distorted by magnetic field (B0) inhomogeneities which currently must be corrected using separately acquired field map data. However, changes in the head position of a scanning participant across fMRI frames can cause changes in the B0 field, preventing accurate correction of geometric distortions. Additionally, field maps can be corrupted by movement during their acquisition, preventing distortion correction altogether. In this study, we use phase information from multi-echo (ME) fMRI data to dynamically sample distortion due to fluctuating B0 field inhomogeneity across frames by acquiring multiple echoes during a single EPI readout. Our distortion correction approach, MEDIC (Multi-Echo DIstortion Correction), accurately estimates B0 related distortions for each frame of multi-echo fMRI data. Here, we demonstrate that MEDIC's framewise distortion correction produces improved alignment to anatomy and decreases the impact of head motion on resting-state functional connectivity (RSFC) maps, in higher motion data, when compared to the prior gold standard approach (i.e., TOPUP). Enhanced framewise distortion correction with MEDIC, without the requirement for field map collection, furthers the advantage of multi-echo over single-echo fMRI.
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Affiliation(s)
- Andrew N. Van
- Department of Biomedical Engineering, Washington University in St. Louis, MO 63130
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - David F. Montez
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
| | - Timothy O. Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
| | - Vahdeta Suljic
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Thomas Madison
- Institute of Child Development, University of Minnesota Medical School, Minneapolis, MN 55455
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Noah J. Baden
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | | | - Kristen M. Scheidter
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Julia S. Monk
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Forrest I. Whiting
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Roselyne J. Chauvin
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Samuel R. Krimmel
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Athanasia Metoki
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Aishwarya Rajesh
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Jarod L. Roland
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO 63110
| | - Taylor Salo
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104
| | - Anxu Wang
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Division of Computation and Data Science, Washington University School of Medicine, St. Louis, MO 63110
| | - Kimberly B. Weldon
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine, St. Louis, MO 63130
| | - Joshua S. Shimony
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Benjamin P. Kay
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Steven M. Nelson
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Brenden Tervo-Clemmens
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
- Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Scott A. Marek
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Luca Vizioli
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Theodore D. Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104
| | - Evan M. Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Damien A. Fair
- Institute of Child Development, University of Minnesota Medical School, Minneapolis, MN 55455
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Nico U.F. Dosenbach
- Department of Biomedical Engineering, Washington University in St. Louis, MO 63130
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110
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11
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Gilani N, Mikheev A, Brinkmann IM, Basukala D, Benkert T, Kumbella M, Babb JS, Chandarana H, Sigmund EE. Characterization of motion dependent magnetic field inhomogeneity for DWI in the kidneys. Magn Reson Imaging 2023; 100:93-101. [PMID: 36924807 PMCID: PMC10108090 DOI: 10.1016/j.mri.2023.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/10/2023] [Accepted: 03/12/2023] [Indexed: 03/15/2023]
Abstract
PURPOSE Diffusion-weighted imaging (DWI) of the abdomen has increased dramatically for both research and clinical purposes. Motion and static field inhomogeneity related challenges limit image quality of abdominopelvic imaging with the most conventional echo-planar imaging (EPI) pulse sequence. While reversed phase encoded imaging is increasingly used to facilitate distortion correction, it typically assumes one motion independent magnetic field distribution. In this study, we describe a more generalized workflow for the case of kidney DWI in which the field inhomogeneity at multiple respiratory phases is mapped and used to correct all images in a multi-contrast DWI series. METHODS In this HIPAA-compliant and IRB-approved prospective study, 8 volunteers (6 M, ages 28-51) had abdominal imaging performed in a 3 T MRI system (MAGNETOM Prisma; Siemens Healthcare, Erlangen, Germany) with ECG gating. Coronal oblique T2-weighted HASTE images were collected for anatomical reference. Sagittal phase-contrast (PC) MRI images through the left renal artery were collected to determine systolic and diastolic phases. Cardiac triggered oblique coronal DWI were collected at 10 b-values between 0 and 800 s/mm2 and 12 directions. DWI series were distortion corrected using field maps generated by forward and reversed phase encoded b = 0 images collected over the full respiratory cycle and matched by respiratory phase. Morphologic accuracy, intraseries spatial variability, and diffusion tensor imaging (DTI) metrics mean diffusivity (MD) and fractional anisotropy (FA) were compared for results generated with no distortion correction, correction with only one respiratory bin, and correction with multiple respiratory bins across the breathing cycle. RESULTS Computed field maps showed significant variation in static field with kidney laterality, region, and respiratory phase. Distortion corrected images showed significantly better registration to morphologic images than uncorrected images; for the left kidney, the multiple bin correction outperformed one bin correction. Line profile analysis showed significantly reduced spatial variation with multiple bins than one bin correction. DTI metrics were mostly similar between correction methods, with some differences observed in MD between uncorrected and corrected datasets. CONCLUSIONS Our results indicate improved morphology of kidney DWI and derived parametric maps as well as reduced variability over the full image series using the motion-resolved distortion correction. This work highlights some morphologic and quantitative metric improvements can be obtained for kidney DWI when distortion correction is performed in a respiratory-resolved manner.
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Affiliation(s)
- Nima Gilani
- Center for Advanced Imaging and Innovation (CAI(2)R), Center for Biomedical Imaging, Department of Radiology, NYU Langone Health, New York, USA.
| | - Artem Mikheev
- Center for Advanced Imaging and Innovation (CAI(2)R), Center for Biomedical Imaging, Department of Radiology, NYU Langone Health, New York, USA
| | | | - Dibash Basukala
- Center for Advanced Imaging and Innovation (CAI(2)R), Center for Biomedical Imaging, Department of Radiology, NYU Langone Health, New York, USA
| | | | - Malika Kumbella
- Center for Advanced Imaging and Innovation (CAI(2)R), Center for Biomedical Imaging, Department of Radiology, NYU Langone Health, New York, USA
| | - James S Babb
- Center for Advanced Imaging and Innovation (CAI(2)R), Center for Biomedical Imaging, Department of Radiology, NYU Langone Health, New York, USA
| | - Hersh Chandarana
- Center for Advanced Imaging and Innovation (CAI(2)R), Center for Biomedical Imaging, Department of Radiology, NYU Langone Health, New York, USA
| | - Eric E Sigmund
- Center for Advanced Imaging and Innovation (CAI(2)R), Center for Biomedical Imaging, Department of Radiology, NYU Langone Health, New York, USA.
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12
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Arefeen Y, Xu J, Zhang M, Dong Z, Wang F, White J, Bilgic B, Adalsteinsson E. Latent signal models: Learning compact representations of signal evolution for improved time-resolved, multi-contrast MRI. Magn Reson Med 2023; 90:483-501. [PMID: 37093775 DOI: 10.1002/mrm.29657] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/09/2023] [Accepted: 03/16/2023] [Indexed: 04/25/2023]
Abstract
PURPOSE To improve time-resolved reconstructions by training auto-encoders to learn compact representations of Bloch-simulated signal evolution and inserting the decoder into the forward model. METHODS Building on model-based nonlinear and linear subspace techniques, we train auto-encoders on dictionaries of simulated signal evolution to learn compact, nonlinear, latent representations. The proposed latent signal model framework inserts the decoder portion of the auto-encoder into the forward model and directly reconstructs the latent representation. Latent signal models essentially serve as a proxy for fast and feasible differentiation through the Bloch equations used to simulate signal. This work performs experiments in the context of T2 -shuffling, gradient echo EPTI, and MPRAGE-shuffling. We compare how efficiently auto-encoders represent signal evolution in comparison to linear subspaces. Simulation and in vivo experiments then evaluate if reducing degrees of freedom by incorporating our proxy for the Bloch equations, the decoder portion of the auto-encoder, into the forward model improves reconstructions in comparison to subspace constraints. RESULTS An auto-encoder with 1 real latent variable represents single-tissue fast spin echo, EPTI, and MPRAGE signal evolution to within 0.15% normalized RMS error, enabling reconstruction problems with 3 degrees of freedom per voxel (real latent variable + complex scaling) in comparison to linear models with 4-8 degrees of freedom per voxel. In simulated/in vivo T2 -shuffling and in vivo EPTI experiments, the proposed framework achieves consistent quantitative normalized RMS error improvement over linear approaches. From qualitative evaluation, the proposed approach yields images with reduced blurring and noise amplification in MPRAGE-shuffling experiments. CONCLUSION Directly solving for nonlinear latent representations of signal evolution improves time-resolved MRI reconstructions.
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Affiliation(s)
- Yamin Arefeen
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Junshen Xu
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Molin Zhang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
| | - Jacob White
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Elfar Adalsteinsson
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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