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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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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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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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Lee PK, Hargreaves BA. A joint linear reconstruction for multishot diffusion weighted non-Carr-Purcell-Meiboom-Gill fast spin echo with full signal. Magn Reson Med 2022; 88:2139-2156. [PMID: 35906924 PMCID: PMC9732866 DOI: 10.1002/mrm.29393] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 06/25/2022] [Accepted: 06/27/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE Diffusion weighted Fast Spin Echo (DW-FSE) is a promising approach for distortionless DW imaging that is robust to system imperfections such as eddy currents and off-resonance. Due to non-Carr-Purcell-Meiboom-Gill (CPMG) magnetization, most DW-FSE sequences discard a large fraction of the signal ( 2 - 2 × $$ \sqrt{2}-2\times $$ ), reducing signal-to-noise ratio (SNR) efficiency compared to DW-EPI. The full FSE signal can be preserved by quadratically incrementing the transmit phase of the refocusing pulses, but this method of resolving non-CPMG magnetization has only been applied to single-shot DW-FSE due to challenges associated with image reconstruction. We present a joint linear reconstruction for multishot quadratic phase increment data that addresses these challenges and corrects ghosting from both shot-to-shot phase and intrashot signal oscillations. Multishot imaging reduces T2 blur and joint reconstruction of shots improves g-factor performance. A thorough analysis on the condition number of the proposed linear system is described. METHODS A joint multishot reconstruction is derived from the non-CPMG signal model. Multishot quadratic phase increment DW-FSE was tested in a standardized diffusion phantom and compared to single-shot DW-FSE and DW-EPI in vivo in the brain, cervical spine, and prostate. The pseudo multiple replica technique was applied to generate g-factor and SNR maps. RESULTS The proposed joint shot reconstruction eliminates ghosting from shot-to-shot phase and intrashot oscillations. g-factor performance is improved compared to previously proposed reconstructions, permitting efficient multishot imaging. apparent diffusion coefficient estimates in phantom experiments and in vivo are comparable to those obtained with conventional methods. CONCLUSION Multi-shot non-CPMG DW-FSE data with full signal can be jointly reconstructed using a linear model.
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Affiliation(s)
- Philip K. Lee
- Radiology, Stanford University, Stanford, CA, 94305, USA,Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Brian A. Hargreaves
- Radiology, Stanford University, Stanford, CA, 94305, USA,Electrical Engineering, Stanford University, Stanford, CA, 94305, USA,Bioengineering, Stanford University, Stanford, CA, 94305, USA
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Afzali M, Mueller L, Sakaie K, Hu S, Chen Y, Szczepankiewicz F, Griswold MA, Jones DK, Ma D. MR Fingerprinting with b-Tensor Encoding for Simultaneous Quantification of Relaxation and Diffusion in a Single Scan. Magn Reson Med 2022; 88:2043-2057. [PMID: 35713357 PMCID: PMC9420788 DOI: 10.1002/mrm.29352] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 05/19/2022] [Accepted: 05/20/2022] [Indexed: 11/08/2022]
Abstract
PURPOSE Although both relaxation and diffusion imaging are sensitive to tissue microstructure, studies have reported limited sensitivity and robustness of using relaxation or conventional diffusion alone to characterize tissue microstructure. Recently, it has been shown that tensor-valued diffusion encoding and joint relaxation-diffusion quantification enable more reliable quantification of compartment-specific microstructural properties. However, scan times to acquire such data can be prohibitive. Here, we aim to simultaneously quantify relaxation and diffusion using MR fingerprinting (MRF) and b-tensor encoding in a clinically feasible time. METHODS We developed multidimensional MRF scans (mdMRF) with linear and spherical b-tensor encoding (LTE and STE) to simultaneously quantify T1, T2, and ADC maps from a single scan. The image quality, accuracy, and scan efficiency were compared between the mdMRF using LTE and STE. Moreover, we investigated the robustness of different sequence designs to signal errors and their impact on the maps. RESULTS T1 and T2 maps derived from the mdMRF scans have consistently high image quality, while ADC maps are sensitive to different sequence designs. Notably, the fast imaging steady state precession (FISP)-based mdMRF scan with peripheral pulse gating provides the best ADC maps that are free of image distortion and shading artifacts. CONCLUSION We demonstrated the feasibility of quantifying T1, T2, and ADC maps simultaneously from a single mdMRF scan in around 24 s/slice. The map quality and quantitative values are consistent with the reference scans.
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Affiliation(s)
- Maryam Afzali
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of Leeds
LeedsUK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff UniversityCardiffUK
| | - Lars Mueller
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of Leeds
LeedsUK
| | - Ken Sakaie
- Imaging Institute, Cleveland ClinicClevelandOhioUSA
| | - Siyuan Hu
- Biomedical EngineeringCase Western Reserve UniversityClevelandOhioUSA
| | - Yong Chen
- RadiologyCase Western Reserve UniversityClevelandOhioUSA
| | | | | | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff UniversityCardiffUK
| | - Dan Ma
- Biomedical EngineeringCase Western Reserve UniversityClevelandOhioUSA
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