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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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Meyer NK, In MH, Black DF, Campeau NG, Welker KM, Huston J, Halverson MA, Bernstein MA, Trzasko JD. Model-based iterative reconstruction for direct imaging with point spread function encoded echo planar MRI. Magn Reson Imaging 2024; 109:189-202. [PMID: 38490504 PMCID: PMC11075760 DOI: 10.1016/j.mri.2024.03.009] [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: 12/20/2023] [Revised: 03/08/2024] [Accepted: 03/08/2024] [Indexed: 03/17/2024]
Abstract
BACKGROUND Echo planar imaging (EPI) is a fast measurement technique commonly used in magnetic resonance imaging (MRI), but is highly sensitive to measurement non-idealities in reconstruction. Point spread function (PSF)-encoded EPI is a multi-shot strategy which alleviates distortion, but acquisition of encodings suitable for direct distortion-free imaging prolongs scan time. In this work, a model-based iterative reconstruction (MBIR) framework is introduced for direct imaging with PSF-EPI to improve image quality and acceleration potential. METHODS An MBIR platform was developed for accelerated PSF-EPI. The reconstruction utilizes a subspace representation, is regularized to promote local low-rankedness (LLR), and uses variable splitting for efficient iteration. Comparisons were made against standard reconstructions from prospectively accelerated PSF-EPI data and with retrospective subsampling. Exploring aggressive partial Fourier acceleration of the PSF-encoding dimension, additional comparisons were made against an extension of Homodyne to direct PSF-EPI in numerical experiments. A neuroradiologists' assessment was completed comparing images reconstructed with MBIR from retrospectively truncated data directly against images obtained with standard reconstructions from non-truncated datasets. RESULTS Image quality results were consistently superior for MBIR relative to standard and Homodyne reconstructions. As the MBIR signal model and reconstruction allow for arbitrary sampling of the PSF space, random sampling of the PSF-encoding dimension was also demonstrated, with quantitative assessments indicating best performance achieved through nonuniform PSF sampling combined with partial Fourier. With retrospective subsampling, MBIR reconstructs high-quality images from sub-minute scan datasets. MBIR was shown to be superior in a neuroradiologists' assessment with respect to three of five performance criteria, with equivalence for the remaining two. CONCLUSIONS A novel image reconstruction framework is introduced for direct imaging with PSF-EPI, enabling arbitrary PSF space sampling and reconstruction of diagnostic-quality images from highly accelerated PSF-encoded EPI data.
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Affiliation(s)
- Nolan K Meyer
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Myung-Ho In
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - David F Black
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Norbert G Campeau
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Kirk M Welker
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - John Huston
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Maria A Halverson
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Matt A Bernstein
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Joshua D Trzasko
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
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Cao X, Liao C, Zhou Z, Zhong Z, Li Z, Dai E, Iyer SS, Hannum AJ, Yurt M, Schauman S, Chen Q, Wang N, Wei J, Yan Y, He H, Skare S, Zhong J, Kerr A, Setsompop K. DTI-MR fingerprinting for rapid high-resolution whole-brain T 1 , T 2 , proton density, ADC, and fractional anisotropy mapping. Magn Reson Med 2024; 91:987-1001. [PMID: 37936313 PMCID: PMC11068310 DOI: 10.1002/mrm.29916] [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: 07/14/2023] [Revised: 10/17/2023] [Accepted: 10/18/2023] [Indexed: 11/09/2023]
Abstract
PURPOSE This study aims to develop a high-efficiency and high-resolution 3D imaging approach for simultaneous mapping of multiple key tissue parameters for routine brain imaging, including T1 , T2 , proton density (PD), ADC, and fractional anisotropy (FA). The proposed method is intended for pushing routine clinical brain imaging from weighted imaging to quantitative imaging and can also be particularly useful for diffusion-relaxometry studies, which typically suffer from lengthy acquisition time. METHODS To address challenges associated with diffusion weighting, such as shot-to-shot phase variation and low SNR, we integrated several innovative data acquisition and reconstruction techniques. Specifically, we used M1-compensated diffusion gradients, cardiac gating, and navigators to mitigate phase variations caused by cardiac motion. We also introduced a data-driven pre-pulse gradient to cancel out eddy currents induced by diffusion gradients. Additionally, to enhance image quality within a limited acquisition time, we proposed a data-sharing joint reconstruction approach coupled with a corresponding sequence design. RESULTS The phantom and in vivo studies indicated that the T1 and T2 values measured by the proposed method are consistent with a conventional MR fingerprinting sequence and the diffusion results (including diffusivity, ADC, and FA) are consistent with the spin-echo EPI DWI sequence. CONCLUSION The proposed method can achieve whole-brain T1 , T2 , diffusivity, ADC, and FA maps at 1-mm isotropic resolution within 10 min, providing a powerful tool for investigating the microstructural properties of brain tissue, with potential applications in clinical and research settings.
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Affiliation(s)
- Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Congyu Liao
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Zihan Zhou
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Zheng Zhong
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Zhitao Li
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Erpeng Dai
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Siddharth Srinivasan Iyer
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts, USA
| | - Ariel J Hannum
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Mahmut Yurt
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Sophie Schauman
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Quan Chen
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Nan Wang
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Jintao Wei
- Center for Brain Imaging Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yifan Yan
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China
- School of Physics, Zhejiang University, Hangzhou, Zhejiang, China
| | - Stefan Skare
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Jianhui Zhong
- Department of Imaging Sciences, University of Rochester, NY, USA
| | - Adam Kerr
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
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Sun K, Chen Z, Dan G, Luo Q, Yan L, Liu F, Zhou XJ. Three-dimensional echo-shifted EPI with simultaneous blip-up and blip-down acquisitions for correcting geometric distortion. Magn Reson Med 2023; 90:2375-2387. [PMID: 37667533 PMCID: PMC10903279 DOI: 10.1002/mrm.29828] [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: 01/18/2023] [Revised: 07/08/2023] [Accepted: 07/25/2023] [Indexed: 09/06/2023]
Abstract
PURPOSE EPI with blip-up/down acquisition (BUDA) can provide high-quality images with minimal distortions by using two readout trains with opposing phase-encoding gradients. Because of the need for two separate acquisitions, BUDA doubles the scan time and degrades the temporal resolution when compared to single-shot EPI, presenting a major challenge for many applications, particularly fMRI. This study aims at overcoming this challenge by developing an echo-shifted EPI BUDA (esEPI-BUDA) technique to acquire both blip-up and blip-down datasets in a single shot. METHODS A 3D esEPI-BUDA pulse sequence was designed by using an echo-shifting strategy to produce two EPI readout trains. These readout trains produced a pair of k-space datasets whose k-space trajectories were interleaved with opposite phase-encoding gradient directions. The two k-space datasets were separately reconstructed using a 3D SENSE algorithm, from which time-resolved B0 -field maps were derived using TOPUP in FSL and then input into a forward model of joint parallel imaging reconstruction to correct for geometric distortion. In addition, Hankel structured low-rank constraint was incorporated into the reconstruction framework to improve image quality by mitigating the phase errors between the two interleaved k-space datasets. RESULTS The 3D esEPI-BUDA technique was demonstrated in a phantom and an fMRI study on healthy human subjects. Geometric distortions were effectively corrected in both phantom and human brain images. In the fMRI study, the visual activation volumes and their BOLD responses were comparable to those from conventional 3D echo-planar images. CONCLUSION The improved imaging efficiency and dynamic distortion correction capability afforded by 3D esEPI-BUDA are expected to benefit many EPI applications.
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Affiliation(s)
- Kaibao Sun
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States
| | - Zhifeng Chen
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC, Australia
| | - Guangyu Dan
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States
| | - Qingfei Luo
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States
| | - Lirong Yan
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Feng Liu
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Xiaohong Joe Zhou
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States
- Departments of Radiology and Neurosurgery, University of Illinois at Chicago, Chicago, IL, United States
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Liao C, Yarach U, Cao X, Iyer SS, Wang N, Kim TH, Tian Q, Bilgic B, Kerr AB, Setsompop K. High-fidelity mesoscale in-vivo diffusion MRI through gSlider-BUDA and circular EPI with S-LORAKS reconstruction. Neuroimage 2023; 275:120168. [PMID: 37187364 PMCID: PMC10451786 DOI: 10.1016/j.neuroimage.2023.120168] [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: 03/06/2023] [Revised: 04/27/2023] [Accepted: 05/12/2023] [Indexed: 05/17/2023] Open
Abstract
PURPOSE To develop a high-fidelity diffusion MRI acquisition and reconstruction framework with reduced echo-train-length for less T2* image blurring compared to typical highly accelerated echo-planar imaging (EPI) acquisitions at sub-millimeter isotropic resolution. METHODS We first proposed a circular-EPI trajectory with partial Fourier sampling on both the readout and phase-encoding directions to minimize the echo-train-length and echo time. We then utilized this trajectory in an interleaved two-shot EPI acquisition with reversed phase-encoding polarity, to aid in the correction of off-resonance-induced image distortions and provide complementary k-space coverage in the missing partial Fourier regions. Using model-based reconstruction with structured low-rank constraint and smooth phase prior, we corrected the shot-to-shot phase variations across the two shots and recover the missing k-space data. Finally, we combined the proposed acquisition/reconstruction framework with an SNR-efficient RF-encoded simultaneous multi-slab technique, termed gSlider, to achieve high-fidelity 720 µm and 500 µm isotropic resolution in-vivo diffusion MRI. RESULTS Both simulation and in-vivo results demonstrate the effectiveness of the proposed acquisition and reconstruction framework to provide distortion-corrected diffusion imaging at the mesoscale with markedly reduced T2*-blurring. The in-vivo results of 720 µm and 500 µm datasets show high-fidelity diffusion images with reduced image blurring and echo time using the proposed approaches. CONCLUSIONS The proposed method provides high-quality distortion-corrected diffusion-weighted images with ∼40% reduction in the echo-train-length and T2* blurring at 500µm-isotropic-resolution compared to standard multi-shot EPI.
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Affiliation(s)
- Congyu Liao
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Uten Yarach
- Radiologic Technology Department, Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand
| | - Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
| | - Siddharth Srinivasan Iyer
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nan Wang
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Tae Hyung Kim
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Department of Computer Engineering, Hongik University, Seoul, South Korea
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Adam B Kerr
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Stanford Center for Cognitive and Neurobiological Imaging, Stanford University, Stanford, CA, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
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Chen Z, Liao C, Cao X, Poser BA, Xu Z, Lo WC, Wen M, Cho J, Tian Q, Wang Y, Feng Y, Xia L, Chen W, Liu F, Bilgic B. 3D-EPI blip-up/down acquisition (BUDA) with CAIPI and joint Hankel structured low-rank reconstruction for rapid distortion-free high-resolution T 2 * mapping. Magn Reson Med 2023; 89:1961-1974. [PMID: 36705076 PMCID: PMC10072851 DOI: 10.1002/mrm.29578] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 12/19/2022] [Accepted: 12/22/2022] [Indexed: 01/28/2023]
Abstract
PURPOSE This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitativeT 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping. METHODS 3D Blip-up and -down acquisition (3D-BUDA) sequence is designed for both single- and multi-echo 3D gradient recalled echo (GRE)-EPI imaging using multiple shots with blip-up and -down readouts to encode B0 field map information. Complementary k-space coverage is achieved using controlled aliasing in parallel imaging (CAIPI) sampling across the shots. For image reconstruction, an iterative hard-thresholding algorithm is employed to minimize the cost function that combines field map information informed parallel imaging with the structured low-rank constraint for multi-shot 3D-BUDA data. Extending 3D-BUDA to multi-echo imaging permitsT 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping. For this, we propose constructing a joint Hankel matrix along both echo and shot dimensions to improve the reconstruction. RESULTS Experimental results on in vivo multi-echo data demonstrate that, by performing joint reconstruction along with both echo and shot dimensions, reconstruction accuracy is improved compared to standard 3D-BUDA reconstruction. CAIPI sampling is further shown to enhance image quality. ForT 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping, parameter values from 3D-Joint-CAIPI-BUDA and reference multi-echo GRE are within limits of agreement as quantified by Bland-Altman analysis. CONCLUSIONS The proposed technique enables rapid 3D distortion-free high-resolution imaging andT 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping. Specifically, 3D-BUDA enables 1-mm isotropic whole-brain imaging in 22 s at 3T and 9 s on a 7T scanner. The combination of multi-echo 3D-BUDA with CAIPI acquisition and joint reconstruction enables distortion-free whole-brainT 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping in 47 s at 1.1 × 1.1 × 1.0 mm3 resolution.
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Affiliation(s)
- Zhifeng Chen
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Charlestown, MA, USA
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC, Australia
| | - Congyu Liao
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Benedikt A. Poser
- Maastricht Brain Imaging Center, Faculty of Psychology and Neuroscience, University of Maastricht, the Netherlands
| | - Zhongbiao Xu
- Department of Radiotherapy, Cancer Center, Guangdong Provincial People’s Hospital & Guangdong Academy of Medical Science, Guangzhou, China
| | | | - Manyi Wen
- Department of Chemical Pathology, The Chinese University of Hong Kong, Hong Kong, China
| | - Jaejin Cho
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Yaohui Wang
- Division of Superconducting Magnet Science and Technology, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China
| | - Yanqiu Feng
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
| | - Ling Xia
- Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Wufan Chen
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Feng Liu
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Charlestown, MA, USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
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Otikovs M, Basak A, Frydman L. Spatiotemporal encoding MRI using subspace-constrained sampling and locally-low-rank regularization: Applications to diffusion weighted and diffusion kurtosis imaging of human brain and prostate. Magn Reson Imaging 2022; 94:151-160. [PMID: 36216145 DOI: 10.1016/j.mri.2022.09.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 09/21/2022] [Accepted: 09/28/2022] [Indexed: 06/16/2023]
Abstract
The benefits of performing locally low-rank (LLR) reconstructions on subsampled diffusion weighted and diffusion kurtosis imaging data employing spatiotemporal encoding (SPEN) methods, is investigated. SPEN allows for self-referenced correction of motion-induced phase errors in case of interleaved diffusion-oriented acquisitions, and allows one to overcome distortions otherwise observed along EPI's phase-encoded dimension. In combination with LLR-based reconstructions of the pooled imaging data and with a joint subsampling of b-weighted and interleaved images, additional improvements in terms of sensitivity as well as shortened acquisition times are demonstrated, without noticeable penalties. Details on how the LLR-regularized, subspace-constrained image reconstructions were adapted to SPEN are given; the improvements introduced by adopting these reconstruction frameworks for the accelerated acquisition of diffusivity and of kurtosis imaging data in both relatively homogeneous regions like the human brain and in more challenging regions like the human prostate, are presented and discussed within the context of similar efforts in the field.
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Affiliation(s)
- Martins Otikovs
- Department of Chemical and Biological Physics and Azrieli National Center for Brain Imaging, Weizmann Institute of Science, Rehovot, Israel
| | - Ankit Basak
- Department of Chemical and Biological Physics and Azrieli National Center for Brain Imaging, Weizmann Institute of Science, Rehovot, Israel
| | - Lucio Frydman
- Department of Chemical and Biological Physics and Azrieli National Center for Brain Imaging, Weizmann Institute of Science, Rehovot, Israel.
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Ma L, Wu J, Yang Q, Zhou Z, He H, Bao J, Bao L, Wang X, Zhang P, Zhong J, Cai C, Cai S, Chen Z. Single-shot multi-parametric mapping based on multiple overlapping-echo detachment (MOLED) imaging. Neuroimage 2022; 263:119645. [PMID: 36155244 DOI: 10.1016/j.neuroimage.2022.119645] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 09/21/2022] [Accepted: 09/21/2022] [Indexed: 11/29/2022] Open
Abstract
Multi-parametric quantitative magnetic resonance imaging (mqMRI) allows the characterization of multiple tissue properties non-invasively and has shown great potential to enhance the sensitivity of MRI measurements. However, real-time mqMRI during dynamic physiological processes or general motions remains challenging. To overcome this bottleneck, we propose a novel mqMRI technique based on multiple overlapping-echo detachment (MOLED) imaging, termed MQMOLED, to enable mqMRI in a single shot. In the data acquisition of MQMOLED, multiple MR echo signals with different multi-parametric weightings and phase modulations are generated and acquired in the same k-space. The k-space data is Fourier transformed and fed into a well-trained neural network for the reconstruction of multi-parametric maps. We demonstrated the accuracy and repeatability of MQMOLED in simultaneous mapping apparent proton density (APD) and any two parameters among T2, T2*, and apparent diffusion coefficient (ADC) in 130-170 ms. The abundant information delivered by the multiple overlapping-echo signals in MQMOLED makes the technique potentially robust to system imperfections, such as inhomogeneity of static magnetic field or radiofrequency field. Benefitting from the single-shot feature, MQMOLED exhibits a strong motion tolerance to the continuous movements of subjects. For the first time, it captured the synchronous changes of ADC, T2, and T1-weighted APD in contrast-enhanced perfusion imaging on patients with brain tumors, providing additional information about vascular density to the hemodynamic parametric maps. We expect that MQMOLED would promote the development of mqMRI technology and greatly benefit the applications of mqMRI, including therapeutics and analysis of metabolic/functional processes.
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Affiliation(s)
- Lingceng Ma
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
| | - Jian Wu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
| | - Qinqin Yang
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
| | - Zihan Zhou
- The Center for Brain Imaging Science and Technology, The Collaborative Innovation Center for Diagnosis and The Treatment of Infectious Diseases, Zhejiang University, Hangzhou 310027, China
| | - Hongjian He
- The Center for Brain Imaging Science and Technology, The Collaborative Innovation Center for Diagnosis and The Treatment of Infectious Diseases, Zhejiang University, Hangzhou 310027, China
| | - Jianfeng Bao
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - Lijun Bao
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
| | - Xiaoyin Wang
- The Center for Brain Imaging Science and Technology, The Collaborative Innovation Center for Diagnosis and The Treatment of Infectious Diseases, Zhejiang University, Hangzhou 310027, China
| | - Pujie Zhang
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
| | - Jianhui Zhong
- The Center for Brain Imaging Science and Technology, The Collaborative Innovation Center for Diagnosis and The Treatment of Infectious Diseases, Zhejiang University, Hangzhou 310027, China; Department of Imaging Sciences, University of Rochester, Rochester, NY 14642, USA
| | - Congbo Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China.
| | - Shuhui Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China.
| | - Zhong Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China.
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Zhang Z, Cho J, Wang L, Liao C, Shin HG, Cao X, Lee J, Xu J, Zhang T, Ye H, Setsompop K, Liu H, Bilgic B. Blip up-down acquisition for spin- and gradient-echo imaging (BUDA-SAGE) with self-supervised denoising enables efficient T 2 , T 2 *, para- and dia-magnetic susceptibility mapping. Magn Reson Med 2022; 88:633-650. [PMID: 35436357 DOI: 10.1002/mrm.29219] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 02/14/2022] [Accepted: 02/15/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE To rapidly obtain high resolution T2 , T2 *, and quantitative susceptibility mapping (QSM) source separation maps with whole-brain coverage and high geometric fidelity. METHODS We propose Blip Up-Down Acquisition for Spin And Gradient Echo imaging (BUDA-SAGE), an efficient EPI sequence for quantitative mapping. The acquisition includes multiple T2 *-, T2 '-, and T2 -weighted contrasts. We alternate the phase-encoding polarities across the interleaved shots in this multi-shot navigator-free acquisition. A field map estimated from interim reconstructions was incorporated into the joint multi-shot EPI reconstruction with a structured low rank constraint to eliminate distortion. A self-supervised neural network (NN), MR-Self2Self (MR-S2S), was used to perform denoising to boost SNR. Using Slider encoding allowed us to reach 1 mm isotropic resolution by performing super-resolution reconstruction on volumes acquired with 2 mm slice thickness. Quantitative T2 (=1/R2 ) and T2 * (=1/R2 *) maps were obtained using Bloch dictionary matching on the reconstructed echoes. QSM was estimated using nonlinear dipole inversion on the gradient echoes. Starting from the estimated R2 /R2 * maps, R2 ' information was derived and used in source separation QSM reconstruction, which provided additional para- and dia-magnetic susceptibility maps. RESULTS In vivo results demonstrate the ability of BUDA-SAGE to provide whole-brain, distortion-free, high-resolution, multi-contrast images and quantitative T2 /T2 * maps, as well as yielding para- and dia-magnetic susceptibility maps. Estimated quantitative maps showed comparable values to conventional mapping methods in phantom and in vivo measurements. CONCLUSION BUDA-SAGE acquisition with self-supervised denoising and Slider encoding enables rapid, distortion-free, whole-brain T2 /T2 * mapping at 1 mm isotropic resolution under 90 s.
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Affiliation(s)
- Zijing Zhang
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Jaejin Cho
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Long Wang
- Subtle Medical Inc, Menlo Park, CA, USA
| | - Congyu Liao
- Radiological Sciences Laboratory, Stanford University, Stanford, CA, USA
| | - Hyeong-Geol Shin
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Xiaozhi Cao
- Radiological Sciences Laboratory, Stanford University, Stanford, CA, USA
| | - Jongho Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Jinmin Xu
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Tao Zhang
- Subtle Medical Inc, Menlo Park, CA, USA
| | - Huihui Ye
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Kawin Setsompop
- Radiological Sciences Laboratory, Stanford University, Stanford, CA, USA
| | - Huafeng Liu
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
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So S, Park HW, Kim B, Fritz FJ, Poser BA, Roebroeck A, Bilgic B. BUDA-MESMERISE: Rapid acquisition and unsupervised parameter estimation for T 1 , T 2 , M 0 , B 0 , and B 1 maps. Magn Reson Med 2022; 88:292-308. [PMID: 35344611 DOI: 10.1002/mrm.29228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 02/21/2022] [Accepted: 02/22/2022] [Indexed: 11/10/2022]
Abstract
PURPOSE Rapid acquisition scheme and parameter estimation method are proposed to acquire distortion-free spin- and stimulated-echo signals and combine the signals with a physics-driven unsupervised network to estimate T1 , T2 , and proton density (M0 ) parameter maps, along with B0 and B1 information from the acquired signals. THEORY AND METHODS An imaging sequence with three 90° RF pulses is utilized to acquire spin- and stimulated-echo signals. We utilize blip-up/-down acquisition to eliminate geometric distortion incurred by the effects of B0 inhomogeneity on rapid EPI acquisitions. For multislice imaging, echo-shifting is applied to utilize dead time between the second and third RF pulses to encode information from additional slice positions. To estimate parameter maps from the spin- and stimulated-echo signals with high fidelity, 2 estimation methods, analytic fitting and a novel unsupervised deep neural network method, are developed. RESULTS The proposed acquisition provided distortion-free T1 , T2 , relative proton density (M0), B0 , and B1 maps with high fidelity both in phantom and in vivo brain experiments. From the rapidly acquired spin- and stimulated-echo signals, analytic fitting and the network-based method were able to estimate T1 , T2 , M0 , B0 , and B1 maps with high accuracy. Network estimates demonstrated noise robustness owing to the fact that the convolutional layers take information into account from spatially adjacent voxels. CONCLUSION The proposed acquisition/reconstruction technique enabled whole-brain acquisition of coregistered, distortion-free, T1 , T2 , M0 , B0 , and B1 maps at 1 × 1 × 5 mm3 resolution in 50 s. The proposed unsupervised neural network provided noise-robust parameter estimates from this rapid acquisition.
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Affiliation(s)
- Seohee So
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Hyun Wook Park
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Byungjai Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Francisco J Fritz
- Institute of Systems Neuroscience, Center for Experimental Medicine, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Benedikt A Poser
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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11
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Cao X, Liao C, Iyer SS, Wang Z, Zhou Z, Dai E, Liberman G, Dong Z, Gong T, He H, Zhong J, Bilgic B, Setsompop K. Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging. Magn Reson Med 2022; 88:133-150. [PMID: 35199877 DOI: 10.1002/mrm.29194] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 12/16/2021] [Accepted: 01/21/2022] [Indexed: 11/07/2022]
Abstract
PURPOSE To improve image quality and accelerate the acquisition of 3D MR fingerprinting (MRF). METHODS Building on the multi-axis spiral-projection MRF technique, a subspace reconstruction with locally low-rank constraint and a modified spiral-projection spatiotemporal encoding scheme called tiny golden-angle shuffling were implemented for rapid whole-brain high-resolution quantitative mapping. Reconstruction parameters such as the locally low-rank regularization parameter and the subspace rank were tuned using retrospective in vivo data and simulated examinations. B0 inhomogeneity correction using multifrequency interpolation was incorporated into the subspace reconstruction to further improve the image quality by mitigating blurring caused by off-resonance effect. RESULTS The proposed MRF acquisition and reconstruction framework yields high-quality 1-mm isotropic whole-brain quantitative maps in 2 min at better quality compared with 6-min acquisitions of prior approaches. The proposed method was validated to not induce bias in T1 and T2 mapping. High-quality whole-brain MRF data were also obtained at 0.66-mm isotropic resolution in 4 min using the proposed technique, where the increased resolution was shown to improve visualization of subtle brain structures. CONCLUSIONS The proposed tiny golden-angle shuffling, MRF with optimized spiral-projection trajectory and subspace reconstruction enables high-resolution quantitative mapping in ultrafast acquisition time.
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Affiliation(s)
- Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Congyu Liao
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Siddharth Srinivasan Iyer
- Department of Radiology, Stanford University, Stanford, California, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Zhixing Wang
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Zihan Zhou
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Erpeng Dai
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Gilad Liberman
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Ting Gong
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China.,Department of Imaging Sciences, University of Rochester, Rochester, New York, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Cambridge, Massachusetts, USA.,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
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