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Dong Z, Reese TG, Lee HH, Huang SY, Polimeni JR, Wald LL, Wang F. Romer-EPTI: rotating-view motion-robust super-resolution EPTI for SNR-efficient distortion-free in-vivo mesoscale dMRI and microstructure imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.26.577343. [PMID: 38352481 PMCID: PMC10862730 DOI: 10.1101/2024.01.26.577343] [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: 02/19/2024]
Abstract
Purpose To overcome the major challenges in dMRI acquisition, including low SNR, distortion/blurring, and motion vulnerability. Methods A novel Romer-EPTI technique is developed to provide distortion-free dMRI with significant SNR gain, high motion-robustness, sharp spatial resolution, and simultaneous multi-TE imaging. It introduces a ROtating-view Motion-robust supEr-Resolution technique (Romer) combined with a distortion/blurring-free EPTI encoding. Romer enhances SNR by a simultaneous multi-thick-slice acquisition with rotating-view encoding, while providing high motion-robustness through a motion-aware super-resolution reconstruction, which also incorporates slice-profile and real-value diffusion, to resolve high-isotropic-resolution volumes. The in-plane encoding is performed using distortion/blurring-free EPTI, which further improves effective spatial resolution and motion robustness by preventing not only T2/T2*-blurring but also additional blurring resulting from combining encoded volumes with inconsistent geometries caused by dynamic distortions. Self-navigation was incorporated to enable efficient phase correction. Additional developments include strategies to address slab-boundary artifacts, achieve minimal TE for SNR gain at 7T, and achieve high robustness to strong phase variations at high b-values. Results Using Romer-EPTI, we demonstrate distortion-free whole-brain mesoscale in-vivo dMRI at both 3T (500-μm-iso) and 7T (485-μm-iso) for the first time, with high SNR efficiency (e.g., 25 × ), and high image quality free from distortion and slab-boundary artifacts with minimal blurring. Motion experiments demonstrate Romer-EPTI's high motion-robustness and ability to recover sharp images in the presence of motion. Romer-EPTI also demonstrates significant SNR gain and robustness in high b-value (b=5000s/mm2) and time-dependent dMRI. Conclusion Romer-EPTI significantly improves SNR, motion-robustness, and image quality, providing a highly efficient acquisition for high-resolution dMRI and microstructure imaging.
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Affiliation(s)
- Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Timothy G. Reese
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Susie Y. Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | - Jonathan R. Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | - Lawrence L. Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
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Yang HC, Lavadi RS, Sauerbeck AD, Wallendorf M, Kummer TT, Song SK, Lin TH. Diffusion basis spectrum imaging detects subclinical traumatic optic neuropathy in a closed-head impact mouse model of traumatic brain injury. Front Neurol 2023; 14:1269817. [PMID: 38152638 PMCID: PMC10752006 DOI: 10.3389/fneur.2023.1269817] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 10/12/2023] [Indexed: 12/29/2023] Open
Abstract
Introduction Traumatic optic neuropathy (TON) is the optic nerve injury secondary to brain trauma leading to visual impairment and vision loss. Current clinical visual function assessments often fail to detect TON due to slow disease progression and clinically silent lesions resulting in potentially delayed or missed treatment in patients with traumatic brain injury (TBI). Methods Diffusion basis spectrum imaging (DBSI) is a novel imaging modality that can potentially fill this diagnostic gap. Twenty-two, 16-week-old, male mice were equally divided into a sham or TBI (induced by moderate Closed-Head Impact Model of Engineered Rotational Acceleration device) group. Briefly, mice were anesthetized with isoflurane (5% for 2.5 min followed by 2.5% maintenance during injury induction), had a helmet placed over the head, and were placed in a holder prior to a 2.1-joule impact. Serial visual acuity (VA) assessments, using the Virtual Optometry System, and DBSI scans were performed in both groups of mice. Immunohistochemistry (IHC) and histological analysis of optic nerves was also performed after in vivo MRI. Results VA of the TBI mice showed unilateral or bilateral impairment. DBSI of the optic nerves exhibited bilateral involvement. IHC results of the optic nerves revealed axonal loss, myelin injury, axonal injury, and increased cellularity in the optic nerves of the TBI mice. Increased DBSI axon volume, decreased DBSI λ||, and elevated DBSI restricted fraction correlated with decreased SMI-312, decreased SMI-31, and increased DAPI density, respectively, suggesting that DBSI can detect coexisting pathologies in the optic nerves of TBI mice. Conclusion DBSI provides an imaging modality capable of detecting subclinical changes of indirect TON in TBI mice.
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Affiliation(s)
- Hsin-Chieh Yang
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Raj Swaroop Lavadi
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Andrew D. Sauerbeck
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, United States
| | - Michael Wallendorf
- Department of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States
| | - Terrance T. Kummer
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, United States
- VA Medical Center, St. Louis, MO, United States
| | - Sheng-Kwei Song
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, United States
| | - Tsen-Hsuan Lin
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, 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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Haldar JP. On Ambiguity in Linear Inverse Problems: Entrywise Bounds on Nearly Data-Consistent Solutions and Entrywise Condition Numbers. IEEE TRANSACTIONS ON SIGNAL PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2023; 71:1083-1092. [PMID: 37383695 PMCID: PMC10299746 DOI: 10.1109/tsp.2023.3257989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/30/2023]
Abstract
Ill-posed linear inverse problems appear frequently in various signal processing applications. It can be very useful to have theoretical characterizations that quantify the level of ill-posedness for a given inverse problem and the degree of ambiguity that may exist about its solution. Traditional measures of ill-posedness, such as the condition number of a matrix, provide characterizations that are global in nature. While such characterizations can be powerful, they can also fail to provide full insight into situations where certain entries of the solution vector are more or less ambiguous than others. In this work, we derive novel theoretical lower- and upper-bounds that apply to individual entries of the solution vector, and are valid for all potential solution vectors that are nearly data-consistent. These bounds are agnostic to the noise statistics and the specific method used to solve the inverse problem, and are also shown to be tight. In addition, our results also lead us to introduce an entrywise version of the traditional condition number, which provides a substantially more nuanced characterization of scenarios where certain elements of the solution vector are less sensitive to perturbations than others. Our results are illustrated in an application to magnetic resonance imaging reconstruction, and we include discussions of practical computation methods for large-scale inverse problems, connections between our new theory and the traditional Cramér-Rao bound under statistical modeling assumptions, and potential extensions to cases involving constraints beyond just data-consistency.
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Affiliation(s)
- Justin P Haldar
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089 USA
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Kim Y, Joshi AA, Choi S, Joshi SH, Bhushan C, Varadarajan D, Haldar JP, Leahy RM, Shattuck DW. BrainSuite BIDS App: Containerized Workflows for MRI Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.14.532686. [PMID: 36993283 PMCID: PMC10055125 DOI: 10.1101/2023.03.14.532686] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The BrainSuite Anatomical Pipeline (BAP) extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The BrainSuite Diffusion Pipeline (BDP) processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for geometric image distortion, and fitting diffusion models to the DWI data. The BrainSuite Functional Pipeline (BFP) performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. BFP coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project's grayordinate space. Each of these outputs can then be processed during group-level analysis. The outputs of BAP and BDP are analyzed using the BrainSuite Statistics in R (bssr) toolbox, which provides functionality for hypothesis testing and statistical modeling. The outputs of BFP can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection's Population Imaging of Psychology dataset.
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Affiliation(s)
- Yeun Kim
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Anand A. Joshi
- Signal and Image Processing Institute, Department of Electrical Engineering – Systems, University of Southern California, Los Angeles, CA, USA
| | - Soyoung Choi
- Signal and Image Processing Institute, Department of Electrical Engineering – Systems, University of Southern California, Los Angeles, CA, USA
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shantanu H. Joshi
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Chitresh Bhushan
- Signal and Image Processing Institute, Department of Electrical Engineering – Systems, University of Southern California, Los Angeles, CA, USA
- GE Research, Schenectady, NY, USA
| | - Divya Varadarajan
- Signal and Image Processing Institute, Department of Electrical Engineering – Systems, University of Southern California, Los Angeles, CA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Justin P. Haldar
- Signal and Image Processing Institute, Department of Electrical Engineering – Systems, University of Southern California, Los Angeles, CA, USA
| | - Richard M. Leahy
- Signal and Image Processing Institute, Department of Electrical Engineering – Systems, University of Southern California, Los Angeles, CA, USA
| | - David W. Shattuck
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
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Mohammadi M, Kaye EA, Alus O, Kee Y, Golia Pernicka JS, El Homsi M, Petkovska I, Otazo R. Accelerated Diffusion-Weighted MRI of Rectal Cancer Using a Residual Convolutional Network. Bioengineering (Basel) 2023; 10:bioengineering10030359. [PMID: 36978750 PMCID: PMC10045764 DOI: 10.3390/bioengineering10030359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/28/2023] [Accepted: 03/09/2023] [Indexed: 03/16/2023] Open
Abstract
This work presents a deep-learning-based denoising technique to accelerate the acquisition of high b-value diffusion-weighted MRI for rectal cancer. A denoising convolutional neural network (DCNN) with a combined L1–L2 loss function was developed to denoise high b-value diffusion-weighted MRI data acquired with fewer repetitions (NEX: number of excitations) using the low b-value image as an anatomical guide. DCNN was trained using 85 datasets acquired on patients with rectal cancer and tested on 20 different datasets with NEX = 1, 2, and 4, corresponding to acceleration factors of 16, 8, and 4, respectively. Image quality was assessed qualitatively by expert body radiologists. Reader 1 scored similar overall image quality between denoised images with NEX = 1 and NEX = 2, which were slightly lower than the reference. Reader 2 scored similar quality between NEX = 1 and the reference, while better quality for NEX = 2. Denoised images with fourfold acceleration (NEX = 4) received even higher scores than the reference, which is due in part to the effect of gas-related motion in the rectum, which affects longer acquisitions. The proposed deep learning denoising technique can enable eightfold acceleration with similar image quality (average image quality = 2.8 ± 0.5) and fourfold acceleration with higher image quality (3.0 ± 0.6) than the clinical standard (2.5 ± 0.8) for improved diagnosis of rectal cancer.
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Affiliation(s)
- Mohaddese Mohammadi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Elena A. Kaye
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Or Alus
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Youngwook Kee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | | | - Maria El Homsi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Iva Petkovska
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Correspondence:
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Ramos-Llordén G, Lobos RA, Kim TH, Tian Q, Witzel T, Lee HH, Scholz A, Keil B, Yendiki A, Bilgiç B, Haldar JP, Huang SY. High-fidelity, high-spatial-resolution diffusion magnetic resonance imaging of ex vivo whole human brain at ultra-high gradient strength with structured low-rank echo-planar imaging ghost correction. NMR IN BIOMEDICINE 2023; 36:e4831. [PMID: 36106429 PMCID: PMC9883835 DOI: 10.1002/nbm.4831] [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: 02/08/2022] [Revised: 08/20/2022] [Accepted: 09/11/2022] [Indexed: 06/15/2023]
Abstract
Diffusion magnetic resonance imaging (dMRI) of whole ex vivo human brain specimens enables three-dimensional (3D) mapping of structural connectivity at the mesoscopic scale, providing detailed evaluation of fiber architecture and tissue microstructure at a spatial resolution that is difficult to access in vivo. To account for the short T2 and low diffusivity of fixed tissue, ex vivo dMRI is often acquired using strong diffusion-sensitizing gradients and multishot/segmented 3D echo-planar imaging (EPI) sequences to achieve high spatial resolution. However, the combination of strong diffusion-sensitizing gradients and multishot/segmented EPI readout can result in pronounced ghosting artifacts incurred by nonlinear spatiotemporal variations in the magnetic field produced by eddy currents. Such ghosting artifacts cannot be corrected with conventional correction solutions and pose a significant roadblock to leveraging human MRI scanners with ultrahigh gradients for ex vivo whole-brain dMRI. Here, we show that ghosting-correction approaches that correct for either polarity-related ghosting or shot-to-shot variations in a separate manner are suboptimal for 3D multishot diffusion-weighted EPI experiments in fixed human brain specimens using strong diffusion-sensitizing gradients on the 3-T Connectom MRI scanner, resulting in orientationally biased dMRI estimates. We apply a recently developed advanced k-space reconstruction method based on structured low-rank matrix (SLM) modeling that handles both polarity-related ghosting and shot-to-shot variation simultaneously, to mitigate artifacts in high-angular resolution multishot dMRI data acquired in several fixed human brain specimens at 0.7-0.8-mm isotropic spatial resolution using b-values up to 10,000 s/mm2 and gradient strengths up to 280 mT/m. We demonstrate the improved mapping of diffusion tensor imaging and fiber orientation distribution functions in key neuroanatomical areas distributed across the whole brain using SLM-based EPI ghost correction compared with alternative techniques.
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Affiliation(s)
- Gabriel Ramos-Llordén
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Rodrigo A. Lobos
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Tae Hyung Kim
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Computer Engineering, Hongik University, Seoul, Republic of Korea
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Alina Scholz
- Institute of Medical Physics and Radiation Protection, Mittelhessen University of Applied Sciences, Giessen, Germany
| | - Boris Keil
- Institute of Medical Physics and Radiation Protection, Mittelhessen University of Applied Sciences, Giessen, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Marburg, Philipps University of Marburg, Marburg, Germany
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Berkin Bilgiç
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Justin P. Haldar
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Susie Y. Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, 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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Kim TH, Haldar JP. Efficient iterative solutions to complex-valued nonlinear least-squares problems with mixed linear and antilinear operators. OPTIMIZATION AND ENGINEERING 2022; 23:749-768. [PMID: 35656362 PMCID: PMC9159680 DOI: 10.1007/s11081-021-09604-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 02/11/2021] [Accepted: 02/12/2021] [Indexed: 06/15/2023]
Abstract
We consider a setting in which it is desired to find an optimal complex vector x ∈ C N that satisfies A (x) ≈ b in a least-squares sense, where b ∈ C M is a data vector (possibly noise-corrupted), and A (·) : C N → C M is a measurement operator. If A (·) were linear, this reduces to the classical linear least-squares problem, which has a well-known analytic solution as well as powerful iterative solution algorithms. However, instead of linear least-squares, this work considers the more complicated scenario where A (·) is nonlinear, but can be represented as the summation and/or composition of some operators that are linear and some operators that are antilinear. Some common nonlinear operations that have this structure include complex conjugation or taking the real-part or imaginary-part of a complex vector. Previous literature has shown that this kind of mixed linear/antilinear least-squares problem can be mapped into a linear least-squares problem by considering x as a vector in R 2N instead of C N . While this approach is valid, the replacement of the original complex-valued optimization problem with a real-valued optimization problem can be complicated to implement, and can also be associated with increased computational complexity. In this work, we describe theory and computational methods that enable mixed linear/antilinear least-squares problems to be solved iteratively using standard linear least-squares tools, while retaining all of the complex-valued structure of the original inverse problem. An illustration is providedtodemonstratethatthisapproachcansimplifytheimplementationandreduce the computational complexity of iterative solution algorithms.
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Affiliation(s)
- Tae Hyung Kim
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Justin P. Haldar
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA
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10
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Kim KH, Seo S, Do WJ, Luu HM, Park SH. Varying undersampling directions for accelerating multiple acquisition magnetic resonance imaging. NMR IN BIOMEDICINE 2022; 35:e4572. [PMID: 34114253 DOI: 10.1002/nbm.4572] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 05/27/2021] [Accepted: 05/27/2021] [Indexed: 06/12/2023]
Abstract
In this study, we propose a new sampling strategy for efficiently accelerating multiple acquisition MRI. The new sampling strategy is to obtain data along different phase-encoding directions across multiple acquisitions. The proposed sampling strategy was evaluated in multicontrast MR imaging (T1, T2, proton density) and multiple phase-cycled (PC) balanced steady-state free precession (bSSFP) imaging by using convolutional neural networks with central and random sampling patterns. In vivo MRI acquisitions as well as a public database were used to test the concept. Based on both visual inspection and quantitative analysis, the proposed sampling strategy showed better performance than sampling along the same phase-encoding direction in both multicontrast MR imaging and multiple PC-bSSFP imaging, regardless of sampling pattern (central, random) or datasets (public, retrospective and prospective in vivo). For the prospective in vivo applications, acceleration was performed by sampling along different phase-encoding directions at the time of acquisition with a conventional rectangular field of view, which demonstrated the advantage of the proposed sampling strategy in the real environment. Preliminary trials on compressed sensing (CS) also demonstrated improvement of CS with the proposed idea. Sampling along different phase-encoding directions across multiple acquisitions is advantageous for accelerating multiacquisition MRI, irrespective of sampling pattern or datasets, with further improvement through transfer learning.
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Affiliation(s)
- Ki Hwan Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Sunghun Seo
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Won-Joon Do
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Huan Minh Luu
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Sung-Hong Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
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11
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Tian Q, Li Z, Fan Q, Polimeni JR, Bilgic B, Salat DH, Huang SY. SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI. Neuroimage 2022; 253:119033. [PMID: 35240299 DOI: 10.1016/j.neuroimage.2022.119033] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 02/15/2022] [Accepted: 02/21/2022] [Indexed: 12/12/2022] Open
Abstract
Diffusion tensor magnetic resonance imaging (DTI) is a widely adopted neuroimaging method for the in vivo mapping of brain tissue microstructure and white matter tracts. Nonetheless, the noise in the diffusion-weighted images (DWIs) decreases the accuracy and precision of DTI derived microstructural parameters and leads to prolonged acquisition time for achieving improved signal-to-noise ratio (SNR). Deep learning-based image denoising using convolutional neural networks (CNNs) has superior performance but often requires additional high-SNR data for supervising the training of CNNs, which reduces the feasibility of supervised learning-based denoising in practice. In this work, we develop a self-supervised deep learning-based method entitled "SDnDTI" for denoising DTI data, which does not require additional high-SNR data for training. Specifically, SDnDTI divides multi-directional DTI data into many subsets of six DWI volumes and transforms DWIs from each subset to along the same diffusion-encoding directions through the diffusion tensor model, generating multiple repetitions of DWIs with identical image contrasts but different noise observations. SDnDTI removes noise by first denoising each repetition of DWIs using a deep 3-dimensional CNN with the average of all repetitions with higher SNR as the training target, following the same approach as normal supervised learning based denoising methods, and then averaging CNN-denoised images for achieving higher SNR. The denoising efficacy of SDnDTI is demonstrated in terms of the similarity of output images and resultant DTI metrics compared to the ground truth generated using substantially more DWI volumes on two datasets with different spatial resolutions, b-values and numbers of input DWI volumes provided by the Human Connectome Project (HCP) and the Lifespan HCP in Aging. The SDnDTI results preserve image sharpness and textural details and substantially improve upon those from the raw data. The results of SDnDTI are comparable to those from supervised learning-based denoising and outperform those from state-of-the-art conventional denoising algorithms including BM4D, AONLM and MPPCA. By leveraging domain knowledge of diffusion MRI physics, SDnDTI makes it easier to use CNN-based denoising methods in practice and has the potential to benefit a wider range of research and clinical applications that require accelerated DTI acquisition and high-quality DTI data for mapping of tissue microstructure, fiber tracts and structural connectivity in the living human brain.
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Affiliation(s)
- Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA, United States.
| | - Ziyu Li
- Department of Biomedical Engineering, Tsinghua University, Beijing, PR China
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - David H Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
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12
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Dai E, Lee PK, Dong Z, Fu F, Setsompop K, McNab JA. Distortion-Free Diffusion Imaging Using Self-Navigated Cartesian Echo-Planar Time Resolved Acquisition and Joint Magnitude and Phase Constrained Reconstruction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:63-74. [PMID: 34383645 PMCID: PMC8799377 DOI: 10.1109/tmi.2021.3104291] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Echo-planar time resolved imaging (EPTI) is an effective approach for acquiring high-quality distortion-free images with a multi-shot EPI (ms-EPI) readout. As with traditional ms-EPI acquisitions, inter-shot phase variations present a main challenge when incorporating EPTI into a diffusion-prepared pulse sequence. The aim of this study is to develop a self-navigated Cartesian EPTI-based (scEPTI) acquisition together with a magnitude and phase constrained reconstruction for distortion-free diffusion imaging. A self-navigated Cartesian EPTI-based diffusion-prepared pulse sequence is designed. The different phase components in EPTI diffusion signal are analyzed and an approach to synthesize a fully phase-matched navigator for the inter-shot phase correction is demonstrated. Lastly, EPTI contains richer magnitude and phase information than conventional ms-EPI, such as the magnitude and phase correlations along the temporal dimension. The potential of these magnitude and phase correlations to enhance the reconstruction is explored. The reconstruction results with and without phase matching and with and without phase or magnitude constraints are compared. Compared with reconstruction without phase matching, the proposed phase matching method can improve the accuracy of inter-shot phase correction and reduce signal corruption in the final diffusion images. Magnitude constraints further improve image quality by suppressing the background noise and thereby increasing SNR, while phase constraints can mitigate possible image blurring from adding magnitude constraints. The high-quality distortion-free diffusion images and simultaneous diffusion-relaxometry imaging capacity provided by the proposed EPTI design represent a highly valuable tool for both clinical and neuroscientific assessments of tissue microstructure.
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13
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What's New and What's Next in Diffusion MRI Preprocessing. Neuroimage 2021; 249:118830. [PMID: 34965454 PMCID: PMC9379864 DOI: 10.1016/j.neuroimage.2021.118830] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/26/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on “what’s new” since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on “Mapping the Connectome” in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on “what’s next” in dMRI preprocessing.
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14
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Wang Z, Li Y, Lam F. High-resolution, 3D multi-TE 1 H MRSI using fast spatiospectral encoding and subspace imaging. Magn Reson Med 2021; 87:1103-1118. [PMID: 34752641 DOI: 10.1002/mrm.29015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 08/30/2021] [Accepted: 08/30/2021] [Indexed: 12/22/2022]
Abstract
PURPOSE To develop a novel method to achieve fast, high-resolution, 3D multi-TE 1 H-MRSI of the brain. METHODS A new multi-TE MRSI acquisition strategy was developed that integrates slab selective excitation with adiabatic refocusing for better volume coverage, rapid spatiospectral encoding, sparse multi-TE sampling, and interleaved water navigators for field mapping and calibration. Special data processing strategies were developed to interpolate the sparsely sampled data, remove nuisance signals, and reconstruct multi-TE spatiospectral distributions with high SNR. Phantom and in vivo experiments have been carried out to demonstrate the capability of the proposed method. RESULTS The proposed acquisition can produce multi-TE 1 H-MRSI data with three TEs at a nominal spatial resolution of 3.4 × 3.4 × 5.3 mm3 in around 20 min. High-SNR brain metabolite spatiospectral reconstructions can be obtained from both a metabolite phantom and in vivo experiments by the proposed method. CONCLUSION High-resolution, 3D multi-TE 1 H-MRSI of the brain can be achieved within clinically feasible time. This capability, with further optimizations, could be translated to clinical applications and neuroscience studies where simultaneously mapping metabolites and neurotransmitters and TE-dependent molecular spectral changes are of interest.
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Affiliation(s)
- Zepeng Wang
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yahang Li
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Fan Lam
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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15
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Pesce M, Repetti A, Auría A, Daducci A, Thiran JP, Wiaux Y. Fast Fiber Orientation Estimation in Diffusion MRI from kq-Space Sampling and Anatomical Priors. J Imaging 2021; 7:jimaging7110226. [PMID: 34821857 PMCID: PMC8618762 DOI: 10.3390/jimaging7110226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 10/09/2021] [Accepted: 10/10/2021] [Indexed: 11/18/2022] Open
Abstract
High spatio-angular resolution diffusion MRI (dMRI) has been shown to provide accurate identification of complex neuronal fiber configurations, albeit, at the cost of long acquisition times. We propose a method to recover intra-voxel fiber configurations at high spatio-angular resolution relying on a 3D kq-space under-sampling scheme to enable accelerated acquisitions. The inverse problem for the reconstruction of the fiber orientation distribution (FOD) is regularized by a structured sparsity prior promoting simultaneously voxel-wise sparsity and spatial smoothness of fiber orientation. Prior knowledge of the spatial distribution of white matter, gray matter, and cerebrospinal fluid is also leveraged. A minimization problem is formulated and solved via a stochastic forward–backward algorithm. Simulations and real data analysis suggest that accurate FOD mapping can be achieved from severe kq-space under-sampling regimes potentially enabling high spatio-angular resolution dMRI in the clinical setting.
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Affiliation(s)
- Marica Pesce
- Institute of Sensors Signals and Systems (ISSS), Heriot-Watt University, Edinburgh EH14 4AS, UK
- Correspondence: (M.P.); (A.R.); (Y.W.)
| | - Audrey Repetti
- Institute of Sensors Signals and Systems (ISSS), Heriot-Watt University, Edinburgh EH14 4AS, UK
- Department of Actuarial Mathematics & Statistics (AMS), Heriot-Watt University, Edinburgh EH14 4AS, UK
- Correspondence: (M.P.); (A.R.); (Y.W.)
| | - Anna Auría
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland; (A.A.); (J.-P.T.)
| | - Alessandro Daducci
- Department of Computer Science, University of Verona, 37134 Verona, Italy;
| | - Jean-Philippe Thiran
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland; (A.A.); (J.-P.T.)
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne, CH-1011 Lausanne, Switzerland
| | - Yves Wiaux
- Institute of Sensors Signals and Systems (ISSS), Heriot-Watt University, Edinburgh EH14 4AS, UK
- Correspondence: (M.P.); (A.R.); (Y.W.)
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16
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Peng X, Sui Y, Trzasko JD, Glaser KJ, Huston J, Ehman RL, Pipe JG. Fast 3D MR elastography of the whole brain using spiral staircase: Data acquisition, image reconstruction, and joint deblurring. Magn Reson Med 2021; 86:2011-2024. [PMID: 34096097 PMCID: PMC8498799 DOI: 10.1002/mrm.28855] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 04/06/2021] [Accepted: 05/04/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE To address the need for a method to acquire 3D data for MR elastography (MRE) of the whole brain with substantially improved spatial resolution, high SNR, and reduced acquisition time compared with conventional methods. METHODS We combined a novel 3D spiral staircase data-acquisition method with a spoiled gradient-echo pulse sequence and MRE motion-encoding gradients (MEGs). The spiral-out acquisition permitted use of longer-duration motion-encoding gradients (ie, over two full oscillatory cycles) to enhance displacement SNR, while still maintaining a reasonably short TE for good phase-SNR. Through-plane parallel imaging with low noise penalties was implemented to accelerate acquisition along the slice direction. Shared anatomical information was exploited in the deblurring procedure to further boost SNR for stiffness inversion. RESULTS In vivo and phantom experiments demonstrated the feasibility of the proposed method in producing brain MRE results comparable to the spin-echo-based approaches, both qualitatively and quantitatively. High-resolution (2-mm isotropic) brain MRE data were acquired in 5 minutes using our method with good SNR. Joint deblurring with shared anatomical information produced SNR-enhanced images, leading to upward stiffness estimation. CONCLUSION A novel 3D gradient-echo-based approach has been designed and implemented, and shown to have promising potential for fast and high-resolution in vivo MRE of the whole brain.
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Affiliation(s)
- Xi Peng
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Yi Sui
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Kevin J Glaser
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - John Huston
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Richard L Ehman
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - James G Pipe
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
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17
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Mani M, Magnotta VA, Jacob M. qModeL: A plug-and-play model-based reconstruction for highly accelerated multi-shot diffusion MRI using learned priors. Magn Reson Med 2021; 86:835-851. [PMID: 33759240 PMCID: PMC8076086 DOI: 10.1002/mrm.28756] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 02/08/2021] [Accepted: 02/09/2021] [Indexed: 01/19/2023]
Abstract
PURPOSE To introduce a joint reconstruction method for highly undersampled multi-shot diffusion weighted (msDW) scans. METHODS Multi-shot EPI methods enable higher spatial resolution for diffusion MRI, but at the expense of long scan-time. Highly accelerated msDW scans are needed to enable their utilization in advanced microstructure studies, which require high q-space coverage. Previously, joint k-q undersampling methods coupled with compressed sensing were shown to enable very high acceleration factors. However, the reconstruction of this data using sparsity priors is challenging and is not suited for multi-shell data. We propose a new reconstruction that recovers images from the combined k-q data jointly. The proposed qModeL reconstruction brings together the advantages of model-based iterative reconstruction and machine learning, extending the idea of plug-and-play algorithms. Specifically, qModeL works by prelearning the signal manifold corresponding to the diffusion measurement space using deep learning. The prelearned manifold prior is incorporated into a model-based reconstruction to provide a voxel-wise regularization along the q-dimension during the joint recovery. Notably, the learning does not require in vivo training data and is derived exclusively from biophysical modeling. Additionally, a plug-and-play total variation denoising provides regularization along the spatial dimension. The proposed framework is tested on k-q undersampled single-shell and multi-shell msDW acquisition at various acceleration factors. RESULTS The qModeL joint reconstruction is shown to recover DWIs from 8-fold accelerated msDW acquisitions with error less than 5% for both single-shell and multi-shell data. Advanced microstructural analysis performed using the undersampled reconstruction also report reasonable accuracy. CONCLUSION qModeL enables the joint recovery of highly accelerated multi-shot dMRI utilizing learning-based priors. The bio-physically driven approach enables the use of accelerated multi-shot imaging for multi-shell sampling and advanced microstructure studies.
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Affiliation(s)
- Merry Mani
- Department of Radiology, University of Iowa, Iowa City, Iowa
| | | | - Mathews Jacob
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa
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18
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Chan CC, Haldar JP. Local perturbation responses and checkerboard tests: Characterization tools for nonlinear MRI methods. Magn Reson Med 2021; 86:1873-1887. [PMID: 34080720 DOI: 10.1002/mrm.28828] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 04/09/2021] [Accepted: 04/13/2021] [Indexed: 01/11/2023]
Abstract
PURPOSE Modern methods for MR image reconstruction, denoising, and parameter mapping are becoming increasingly nonlinear, black-box, and at risk of "hallucination." These trends mean that traditional tools for judging confidence in an image (visual quality assessment, point-spread functions (PSFs), g-factor maps, etc.) are less helpful than before. This paper describes and evaluates an approach that can help with assessing confidence in images produced by arbitrary nonlinear methods. THEORY AND METHODS We propose to characterize nonlinear methods by examining the images they produce before and after applying controlled perturbations to the measured data. This results in functions known as local perturbation responses (LPRs) that can provide useful insight into sensitivity, spatial resolution, and aliasing characteristics. LPRs can be viewed as generalizations of classical PSFs, and are are very flexible-they can be applied to arbitary nonlinear methods and arbitrary datasets across a range of different reconstruction, denoising, and parameter mapping applications. Importantly, LPRs do not require a ground truth image. RESULTS Impulse-based and checkerboard-pattern LPRs are demonstrated in image reconstruction and denoising scenarios. We observe that these LPRs provide insights into spatial resolution, signal leakage, and aliasing that are not available with other methods. We also observe that popular reference-based image quality metrics (eg, mean-squared error and structural similarity) do not always correlate with good LPR characteristics. CONCLUSIONS LPRs are a useful tool that can be used to characterize and assess confidence in nonlinear MR methods, and provide insights that are distinct from and complementary to existing quality assessments.
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Affiliation(s)
- Chin-Cheng Chan
- Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.,Signal and Image Processing Institute, University of Southern California, Los Angeles, California, USA
| | - Justin P Haldar
- Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.,Signal and Image Processing Institute, University of Southern California, Los Angeles, California, USA
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19
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Liu Y, Haldar JP. PALMNUT: An Enhanced Proximal Alternating Linearized Minimization Algorithm with Application to Separate Regularization of Magnitude and Phase. IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2021; 7:530-518. [PMID: 34458504 PMCID: PMC8386764 DOI: 10.1109/tci.2021.3077806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
We introduce a new algorithm for complex image reconstruction with separate regularization of the image magnitude and phase. This optimization problem is interesting in many different image reconstruction contexts, although is nonconvex and can be difficult to solve. In this work, we first describe a novel implementation of the previous proximal alternating linearized minimization (PALM) algorithm to solve this optimization problem. We then make enhancements to PALM, leading to a new algorithm named PALMNUT that combines the PALM together with Nesterov's momentum and a novel approach that relies on uncoupled coordinatewise step sizes derived from coordinatewise Lipschitz-like bounds. Theoretically, we establish that a version of PALMNUT (without Nesterov's momentum) monotonically decreases the objective function, guaranteeing convergence of the cost function value. Empirical results obtained in the context of magnetic resonance imaging demonstrate that PALMNUT has computational advantages over common existing approaches like alternating minimization. Although our focus is on the application to separate magnitude and phase regularization, we expect that the same approach may also be useful in other nonconvex optimization problems with similar objective function structure.
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Affiliation(s)
- Yunsong Liu
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Justin P Haldar
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
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20
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Yi Z, Liu Y, Zhao Y, Xiao L, Leong ATL, Feng Y, Chen F, Wu EX. Joint calibrationless reconstruction of highly undersampled multicontrast MR datasets using a low-rank Hankel tensor completion framework. Magn Reson Med 2021; 85:3256-3271. [PMID: 33533092 DOI: 10.1002/mrm.28674] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 12/18/2020] [Accepted: 12/18/2020] [Indexed: 11/05/2022]
Abstract
PURPOSE To jointly reconstruct highly undersampled multicontrast two-dimensional (2D) datasets through a low-rank Hankel tensor completion framework. METHODS A multicontrast Hankel tensor completion (MC-HTC) framework is proposed to exploit the shareable information in multicontrast datasets with respect to their highly correlated image structure, common spatial support, and shared coil sensitivity for joint reconstruction. This is achieved by first organizing multicontrast k-space datasets into a single block-wise Hankel tensor. Subsequent low-rank tensor approximation via higher-order singular value decomposition (HOSVD) uses the image structural correlation by considering different contrasts as virtual channels. Meanwhile, the HOSVD imposes common spatial support and shared coil sensitivity by treating data from different contrasts as from additional k-space kernels. The missing k-space data are then recovered by iteratively performing such low-rank approximation and enforcing data consistency. This joint reconstruction framework was evaluated using multicontrast multichannel 2D human brain datasets (T1 -weighted, T2 -weighted, fluid-attenuated inversion recovery, and T1 -weighted-inversion recovery) of identical image geometry with random and uniform undersampling schemes. RESULTS The proposed method offered high acceleration, exhibiting significantly less residual errors when compared with both single-contrast SAKE (simultaneous autocalibrating and k-space estimation) and multicontrast J-LORAKS (joint parallel-imaging-low-rank matrix modeling of local k-space neighborhoods) low-rank reconstruction. Furthermore, the MC-HTC framework was applied uniquely to Cartesian uniform undersampling by incorporating a novel complementary k-space sampling strategy where the phase-encoding direction among different contrasts is orthogonally alternated. CONCLUSION The proposed MC-HTC approach presents an effective tensor completion framework to jointly reconstruct highly undersampled multicontrast 2D datasets without coil-sensitivity calibration.
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Affiliation(s)
- Zheyuan Yi
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China.,Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China.,Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, People's Republic of China
| | - Yilong Liu
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China.,Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Yujiao Zhao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China.,Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Linfang Xiao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China.,Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Alex T L Leong
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China.,Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, People's Republic of China
| | - Ed X Wu
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China.,Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
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21
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Sveinsson B, Gold GE, Hargreaves BA, Yoon D. Utilizing shared information between gradient-spoiled and RF-spoiled steady-state MRI signals. Phys Med Biol 2021; 66:01NT03. [PMID: 33246317 DOI: 10.1088/1361-6560/abce8a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
This work presents an analytical relationship between gradient-spoiled and RF-spoiled steady-state signals. The two echoes acquired in double-echo in steady-state scans are shown to lie on a line in the signal plane, where the two axes represent the amplitudes of each echo. The location along the line depends on the amount of spoiling and the diffusivity. The line terminates in a point corresponding to an RF-spoiled signal. In addition to the main contribution of demonstrating this signal relationship, we also include the secondary contribution of preliminary results from an example application of the relationship, in the form of a heuristic denoising method when both types of scans are performed. This is investigated in simulations, phantom scans, and in vivo scans. For the signal model, the main topic of this study, simulations confirmed its accuracy and explored its dependency on signal parameters and image noise. For the secondary topic of its preliminary application to reduce noise, simulations demonstrated the denoising method giving a reduction in noise-induced standard deviation of about 30%. The relative effect of the method on the signals is shown to depend on the slope of the described line, which is demonstrated to be zero at the Ernst angle. The phantom scans show a similar effect as the simulations. In vivo scans showed a slightly lower average improvement of about 28%.
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Affiliation(s)
- Bragi Sveinsson
- Athinoula A. Martinos Center, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States of America. Harvard Medical School, Boston, MA, United States of America
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22
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Kim D, Wisnowski JL, Nguyen CT, Haldar JP. Multidimensional correlation spectroscopic imaging of exponential decays: From theoretical principles to in vivo human applications. NMR IN BIOMEDICINE 2020; 33:e4244. [PMID: 31909534 PMCID: PMC7338241 DOI: 10.1002/nbm.4244] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 10/09/2019] [Accepted: 11/27/2019] [Indexed: 05/02/2023]
Abstract
Multiexponential modeling of relaxation or diffusion MR signal decays is a popular approach for estimating and spatially mapping different microstructural tissue compartments. While this approach can be quite powerful, it is also limited by the fact that one-dimensional multiexponential modeling is an ill-posed inverse problem with substantial ambiguities. In this article, we present an overview of a recent multidimensional correlation spectroscopic imaging approach to this problem. This approach helps to alleviate ill-posedness by making advantageous use of multidimensional contrast encoding (e.g., 2D diffusion-relaxation encoding or 2D relaxation-relaxation encoding) combined with a regularized spatial-spectral estimation procedure. Theoretical calculations, simulations, and experimental results are used to illustrate the benefits of this approach relative to classical methods. In addition, we demonstrate an initial proof-of-principle application of this kind of approach to in vivo human MRI experiments.
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Affiliation(s)
- Daeun Kim
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, CA, USA
- Signal and Image Processing Institute, University of Southern California, CA, USA
- Correspondence Daeun Kim,
| | - Jessica L. Wisnowski
- Radiology, Children’s Hospital Los Angeles, CA, USA
- Pediatrics, Children’s Hospital Los Angeles, CA, USA
| | - Christopher T. Nguyen
- Harvard Medical School and Cardiovascular Research Center, Massachusetts General Hospital, MA, USA
- Martinos Center for Biomedical Imaging, Radiology, Massachusetts General Hospital, MA, USA
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, CA, USA
| | - Justin P. Haldar
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, CA, USA
- Signal and Image Processing Institute, University of Southern California, CA, USA
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23
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Tan ET, Wilmes LJ, Joe BN, Onishi N, Arasu VA, Hylton NM, Marinelli L, Newitt DC. Denoising and Multiple Tissue Compartment Visualization of Multi-b-Valued Breast Diffusion MRI. J Magn Reson Imaging 2020; 53:271-282. [PMID: 32614125 DOI: 10.1002/jmri.27268] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 06/10/2020] [Accepted: 06/11/2020] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Multi-b-valued/multi-shell diffusion provides potentially valuable metrics in breast MRI but suffers from low signal-to-noise ratio and has potentially long scan times. PURPOSE To investigate the effects of model-based denoising with no loss of spatial resolution on multi-shell breast diffusion MRI; to determine the effects of downsampling on multi-shell diffusion; and to quantify these effects in multi-b-valued (three directions per b-value) acquisitions. STUDY TYPE Prospective ("fully-sampled" multi-shell) and retrospective longitudinal (multi-b). SUBJECTS One normal subject (multi-shell) and 10 breast cancer subjects imaging at four timepoints (multi-b). FIELD STRENGTH/SEQUENCE 3T multi-shell acquisition and 1.5T multi-b acquisition. ASSESSMENT The "fully-sampled" multi-shell acquisition was retrospectively downsampled to determine the bias and error from downsampling. Mean, axial/parallel, radial diffusivity, and fractional anisotropy (FA) were analyzed. Denoising was applied retrospectively to the multi-b-valued breast cancer subject dataset and assessed subjectively for image noise level and tumor conspicuity. STATISTICAL TESTS Parametric paired t-test (P < 0.05 considered statistically significant) on mean and coefficient of variation of each metric-the apparent diffusion coefficient (ADC) from all b-values, fast ADC, slow ADC, and perfusion fraction. Paired and two-sample t-tests for each metric comparing normal and tumor tissue. RESULTS In the multi-shell data, denoising effectively suppressed FA (-45% to -78%), with small biases in mean diffusivity (-5% in normal, +23% in tumor, and -4% in vascular compartments). In the multi-b data, denoising resulted in small biases to the ADC metrics in tumor and normal contralateral tissue (by -3% to +11%), but greatly reduced the coefficient of variation for every metric (by -1% to -24%). Denoising improved differentiation of tumor and normal tissue regions in most metrics and timepoints; subjectively, image noise level and tumor conspicuity were improved in the fast ADC maps. DATA CONCLUSION Model-based denoising effectively suppressed erroneously high FA and improved the accuracy of diffusivity metrics. EVIDENCE LEVEL 3 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Ek T Tan
- GE Global Research, Niskayuna, New York, USA.,Department of Radiology and Imaging, Hospital for Special Surgery, New York, New York, USA
| | - Lisa J Wilmes
- Department of Radiology, University of California, San Francisco, California, USA
| | - Bonnie N Joe
- Department of Radiology, University of California, San Francisco, California, USA
| | - Natsuko Onishi
- Department of Radiology, University of California, San Francisco, California, USA
| | - Vignesh A Arasu
- Department of Radiology, University of California, San Francisco, California, USA.,Department of Radiology, Kaiser Permanente Medical Center, Vallejo, California, USA.,Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
| | - Nola M Hylton
- Department of Radiology, University of California, San Francisco, California, USA
| | | | - David C Newitt
- Department of Radiology, University of California, San Francisco, California, USA
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24
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Denoise magnitude diffusion magnetic resonance images via variance-stabilizing transformation and optimal singular-value manipulation. Neuroimage 2020; 215:116852. [PMID: 32305566 PMCID: PMC7292796 DOI: 10.1016/j.neuroimage.2020.116852] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 04/07/2020] [Accepted: 04/10/2020] [Indexed: 12/12/2022] Open
Abstract
Although shown to have a great utility for a wide range of neuroscientific and clinical applications, diffusion-weighted magnetic resonance imaging (dMRI) faces a major challenge of low signal-to-noise ratio (SNR), especially when pushing the spatial resolution for improved delineation of brain's fine structure or increasing the diffusion weighting for increased angular contrast or both. Here, we introduce a comprehensive denoising framework for denoising magnitude dMRI. The framework synergistically combines the variance stabilizing transform (VST) with optimal singular value manipulation. The purpose of VST is to transform the Rician data to Gaussian-like data so that an asymptotically optimal singular value manipulation strategy tailored for Gaussian data can be used. The output of the framework is the estimated underlying diffusion signal for each voxel in the image domain. The usefulness of the proposed framework for denoising magnitude dMRI is demonstrated using both simulation and real-data experiments. Our results show that the proposed denoising framework can significantly improve SNR across the entire brain, leading to substantially enhanced performances for estimating diffusion tensor related indices and for resolving crossing fibers when compared to another competing method. More encouragingly, the proposed method when used to denoise a single average of 7 Tesla Human Connectome Project-style diffusion acquisition provided comparable performances relative to those achievable with ten averages for resolving multiple fiber populations across the brain. As such, the proposed denoising method is expected to have a great utility for high-quality, high-resolution whole-brain dMRI, desirable for many neuroscientific and clinical applications.
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25
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Lam F, Sutton BP. Intravoxel B 0 inhomogeneity corrected reconstruction using a low-rank encoding operator. Magn Reson Med 2020; 84:885-894. [PMID: 32020661 DOI: 10.1002/mrm.28182] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 01/03/2020] [Accepted: 01/04/2020] [Indexed: 12/25/2022]
Abstract
PURPOSE To present a general and efficient method for macroscopic intravoxel B 0 inhomogeneity corrected reconstruction from multi-TE acquisitions. THEORY AND METHODS A signal encoding model for multi-TE gradient echo (GRE) acquisitions that incorporates 3D intravoxel B 0 field variations is derived, and a low-rank approximation to the encoding operator is introduced under piecewise linear B 0 assumption. The low-rank approximation enables very efficient computation and memory usage, and allows the proposed signal model to be integrated into general inverse problem formulations that are compatible with multi-coil and undersampling acquisitions as well as different regularization functions. RESULTS Experimental multi-echo GRE data were acquired to evaluate the proposed method. Effective reduction of macroscopic intravoxel B 0 inhomogeneity induced artifacts was demonstrated. Improved R 2 ∗ estimation from the corrected reconstruction over standard Fourier reconstruction has also been obtained. CONCLUSIONS The proposed method can effectively correct the effects of intravoxel B 0 inhomogeneity, and can be useful for various imaging applications involving GRE-based acquisitions, including fMRI, quantitative R 2 ∗ and susceptibility mapping, and MR spectroscopic imaging.
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Affiliation(s)
- Fan Lam
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, IL, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Bradley P Sutton
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, IL, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA
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26
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Zhang C, Arefin TM, Nakarmi U, Lee CH, Li H, Liang D, Zhang J, Ying L. Acceleration of three-dimensional diffusion magnetic resonance imaging using a kernel low-rank compressed sensing method. Neuroimage 2020; 210:116584. [PMID: 32004717 DOI: 10.1016/j.neuroimage.2020.116584] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Revised: 12/23/2019] [Accepted: 01/23/2020] [Indexed: 12/13/2022] Open
Abstract
Diffusion Magnetic Resonance Imaging (dMRI) has shown great potential in probing tissue microstructure and structural connectivity in the brain but is often limited by the lengthy scan time needed to sample the diffusion profile by acquiring multiple diffusion weighted images (DWIs). Although parallel imaging technique has improved the speed of dMRI acquisition, attaining high resolution three dimensional (3D) dMRI on preclinical MRI systems remained still time consuming. In this paper, kernel principal component analysis, a machine learning approach, was employed to estimate the correlation among DWIs. We demonstrated the feasibility of such correlation estimation from low-resolution training DWIs and used the correlation as a constraint to reconstruct high-resolution DWIs from highly under-sampled k-space data, which significantly reduced the scan time. Using full k-space 3D dMRI data of post-mortem mouse brains, we retrospectively compared the performance of the so-called kernel low rank (KLR) method with a conventional compressed sensing (CS) method in terms of image quality and ability to resolve complex fiber orientations and connectivity. The results demonstrated that the KLR-CS method outperformed the conventional CS method for acceleration factors up to 8 and was likely to enhance our ability to investigate brain microstructure and connectivity using high-resolution 3D dMRI.
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Affiliation(s)
- Chaoyi Zhang
- Electrical Engineering, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Tanzil Mahmud Arefin
- Radiology, New York University School of Medicine, New York City, NY, United States
| | - Ukash Nakarmi
- Radiology, Stanford University, Stanford, CA, United States
| | - Choong Heon Lee
- Radiology, New York University School of Medicine, New York City, NY, United States
| | - Hongyu Li
- Electrical Engineering, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, People's Republic of China
| | - Jiangyang Zhang
- Radiology, New York University School of Medicine, New York City, NY, United States
| | - Leslie Ying
- Electrical Engineering, University at Buffalo, State University of New York, Buffalo, NY, United States; Biomedical Engineering, University at Buffalo, State University at New York, Buffalo, NY, United States.
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27
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Haldar JP, Liu Y, Liao C, Fan Q, Setsompop K. Fast submillimeter diffusion MRI using gSlider-SMS and SNR-enhancing joint reconstruction. Magn Reson Med 2020; 84:762-776. [PMID: 31919908 DOI: 10.1002/mrm.28172] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 12/06/2019] [Accepted: 12/23/2019] [Indexed: 12/12/2022]
Abstract
PURPOSE We evaluate a new approach for achieving diffusion MRI data with high spatial resolution, large volume coverage, and fast acquisition speed. THEORY AND METHODS A recent method called gSlider-SMS enables whole-brain submillimeter diffusion MRI with high signal-to-noise ratio (SNR) efficiency. However, despite the efficient acquisition, the resulting images can still suffer from low SNR due to the small size of the imaging voxels. This work proposes to mitigate the SNR problem by combining gSlider-SMS with a regularized SNR-enhancing reconstruction approach. RESULTS Illustrative results show that, from gSlider-SMS data acquired over a span of only 25 minutes on a 3T scanner, the proposed method is able to produce 71 MRI images (64 diffusion encoding orientations with b = 1500 s/ mm 2 , and 7 images without diffusion weighting) of the entire in vivo human brain with nominal 0.66 mm spatial resolution. Using data acquired from 75 minutes of acquisition as a gold standard reference, we demonstrate that the proposed SNR-enhancement procedure leads to substantial improvements in estimated diffusion parameters compared to conventional gSlider reconstruction. Results also demonstrate that the proposed method has advantages relative to denoising methods based on low-rank matrix modeling. A theoretical analysis of the trade-off between spatial resolution and SNR suggests that the proposed approach has high efficiency. CONCLUSIONS The combination of gSlider-SMS with advanced regularized reconstruction enables high-resolution quantitative diffusion MRI from a relatively fast acquisition.
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Affiliation(s)
- Justin P Haldar
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Yunsong Liu
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
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28
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Globally Optimized Super-Resolution of Diffusion MRI Data via Fiber Continuity. ACTA ACUST UNITED AC 2020. [PMID: 34734215 DOI: 10.1007/978-3-030-59728-3_26] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
In this paper, we introduce a technique for super-resolution reconstruction of diffusion MRI, harnessing fiber-continuity (FC) as a constraint in a global whole-brain optimization framework. FC is a biologically-motivated constraint that relates orientation information between neighboring voxels. We show that it can be used to effectively constrain the inverse problem of recovering high-resolution data from low-resolution data. Since voxels are inter-related by FC, we devise a global optimization framework that allows solutions pertaining to all voxels to be solved simultaneously. We demonstrate that the proposed super-resolution framework is effective for diffusion MRI data of a glioma patient, a healthy subject, and a macaque.
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29
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Ma S, Nguyen CT, Han F, Wang N, Deng Z, Binesh N, Moser FG, Christodoulou AG, Li D. Three-dimensional simultaneous brain T 1 , T 2 , and ADC mapping with MR Multitasking. Magn Reson Med 2019; 84:72-88. [PMID: 31765496 DOI: 10.1002/mrm.28092] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 10/01/2019] [Accepted: 10/31/2019] [Indexed: 02/06/2023]
Abstract
PURPOSE To develop a simultaneous T1 , T2 , and ADC mapping method that provides co-registered, distortion-free images and enables multiparametric quantification of 3D brain coverage in a clinically feasible scan time with the MR Multitasking framework. METHODS The T1 /T2 /diffusion weighting was generated by a series of T2 preparations and diffusion preparations. The underlying multidimensional image containing 3 spatial dimensions, 1 T1 weighting dimension, 1 T2 -preparation duration dimension, 1 b-value dimension, and 1 diffusion direction dimension was modeled as a 5-way low-rank tensor. A separate real-time low-rank model incorporating time-resolved phase correction was also used to compensate for both inter-shot and intra-shot phase inconsistency induced by physiological motion. The proposed method was validated on both phantom and 16 healthy subjects. The quantification of T1 /T2 /ADC was evaluated for each case. Three post-surgery brain tumor patients were scanned for demonstration of clinical feasibility. RESULTS Multitasking T1 /T2 /ADC maps were perfectly co-registered and free from image distortion. Phantom studies showed substantial quantitative agreement ( R 2 = 0.999 ) with reference protocols for T1 /T2 /ADC. In vivo studies showed nonsignificant T1 (P = .248), T2 (P = .97), ADC (P = .328) differences among the frontal, parietal, and occipital regions. Although Multitasking showed significant differences of T1 (P = .03), T2 (P < .001), and ADC (P = .001) biases against the references, the mean bias estimates were small (ΔT1 % < 5%, ΔT2 % < 7%, ΔADC% < 5%), with all intraclass correlation coefficients greater than 0.82 indicating "excellent" agreement. Patient studies showed that Multitasking T1 /T2 /ADC maps were consistent with the clinical qualitative images. CONCLUSION The Multitasking approach simultaneously quantifies T1 /T2 /ADC with substantial agreement with the references and is promising for clinical applications.
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Affiliation(s)
- Sen Ma
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California.,Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Christopher T Nguyen
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts
| | - Fei Han
- Siemens Healthcare, Los Angeles, California
| | - Nan Wang
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California.,Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Zixin Deng
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Nader Binesh
- S. Mark Taper Foundation Imaging Center, Cedars-Sinai Medical Center, Los Angeles, California
| | - Franklin G Moser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California
| | | | - Debiao Li
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California.,Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California
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30
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Liao C, Stockmann J, Tian Q, Bilgic B, Arango NS, Manhard MK, Huang SY, Grissom WA, Wald LL, Setsompop K. High-fidelity, high-isotropic-resolution diffusion imaging through gSlider acquisition with B 1 + and T 1 corrections and integrated ΔB 0 /Rx shim array. Magn Reson Med 2019; 83:56-67. [PMID: 31373048 DOI: 10.1002/mrm.27899] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 06/10/2019] [Accepted: 06/17/2019] [Indexed: 01/24/2023]
Abstract
PURPOSE B 1 + and T1 corrections and dynamic multicoil shimming approaches were proposed to improve the fidelity of high-isotropic-resolution generalized slice-dithered enhanced resolution (gSlider) diffusion imaging. METHODS An extended reconstruction incorporating B 1 + inhomogeneity and T1 recovery information was developed to mitigate slab-boundary artifacts in short-repetition time (TR) gSlider acquisitions. Slab-by-slab dynamic B0 shimming using a multicoil integrated ΔB0 /Rx shim array and high in-plane acceleration (Rinplane = 4) achieved with virtual-coil GRAPPA were also incorporated into a 1-mm isotropic resolution gSlider acquisition/reconstruction framework to achieve a significant reduction in geometric distortion compared to single-shot echo planar imaging (EPI). RESULTS The slab-boundary artifacts were alleviated by the proposed B 1 + and T1 corrections compared to the standard gSlider reconstruction pipeline for short-TR acquisitions. Dynamic shimming provided >50% reduction in geometric distortion compared to conventional global second-order shimming. One-millimeter isotropic resolution diffusion data show that the typically problematic temporal and frontal lobes of the brain can be imaged with high geometric fidelity using dynamic shimming. CONCLUSIONS The proposed B 1 + and T1 corrections and local-field control substantially improved the fidelity of high-isotropic-resolution diffusion imaging, with reduced slab-boundary artifacts and geometric distortion compared to conventional gSlider acquisition and reconstruction. This enabled high-fidelity whole-brain 1-mm isotropic diffusion imaging with 64 diffusion directions in 20 min using a 3T clinical scanner.
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Affiliation(s)
- Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Jason Stockmann
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Nicolas S Arango
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Mary Kate Manhard
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - William A Grissom
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
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31
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Phase-matched virtual coil reconstruction for highly accelerated diffusion echo-planar imaging. Neuroimage 2019; 194:291-302. [DOI: 10.1016/j.neuroimage.2019.04.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 03/26/2019] [Accepted: 04/01/2019] [Indexed: 11/24/2022] Open
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32
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Senel LK, Kilic T, Gungor A, Kopanoglu E, Guven HE, Saritas EU, Koc A, Cukur T. Statistically Segregated k-Space Sampling for Accelerating Multiple-Acquisition MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1701-1714. [PMID: 30640604 DOI: 10.1109/tmi.2019.2892378] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
A central limitation of multiple-acquisition magnetic resonance imaging (MRI) is the degradation in scan efficiency as the number of distinct datasets grows. Sparse recovery techniques can alleviate this limitation via randomly undersampled acquisitions. A frequent sampling strategy is to prescribe for each acquisition a different random pattern drawn from a common sampling density. However, naive random patterns often contain gaps or clusters across the acquisition dimension that, in turn, can degrade reconstruction quality or reduce scan efficiency. To address this problem, a statistically segregated sampling method is proposed for multiple-acquisition MRI. This method generates multiple patterns sequentially while adaptively modifying the sampling density to minimize k-space overlap across patterns. As a result, it improves incoherence across acquisitions while still maintaining similar sampling density across the radial dimension of k-space. Comprehensive simulations and in vivo results are presented for phase-cycled balanced steady-state free precession and multi-echo [Formula: see text]-weighted imaging. Segregated sampling achieves significantly improved quality in both Fourier and compressed-sensing reconstructions of multiple-acquisition datasets.
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33
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Exploiting structural redundancy in q-space for improved EAP reconstruction from highly undersampled (k, q)-space in DMRI. Med Image Anal 2019; 54:122-137. [DOI: 10.1016/j.media.2019.02.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Revised: 11/22/2018] [Accepted: 02/26/2019] [Indexed: 11/22/2022]
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34
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Wang H, Zheng R, Dai F, Wang Q, Wang C. High-field mr diffusion-weighted image denoising using a joint denoising convolutional neural network. J Magn Reson Imaging 2019; 50:1937-1947. [PMID: 31012226 DOI: 10.1002/jmri.26761] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 04/11/2019] [Accepted: 04/11/2019] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Low signal-to-noise ratio (SNR) has been a major limiting factor for the application of higher-resolution diffusion-weighted imaging (DWI). Most of the conventional denoising models suffer from the drawbacks of shallow feature extraction and hand-crafted parameter tuning. Although multiple studies have shown the promising applications of image denoising using convolutional neural networks (CNNs), none of them have considered denoising multiple b-value DWIs using a multichannel CNN model. PURPOSE To present a joint denoising CNN (JD-CNN) model to improve the SNR of multiple b-value DWI. STUDY TYPE Retrospective technical development. POPULATION Twenty healthy rats and two rats with clinically confirmed focal cortical dysplasia were included to evaluate the performance of the proposed method. FIELD STRENGTH/SEQUENCE 11.7T MRI, a multiple b-values DWI sequence. ASSESSMENT The total variation (TV) and BM3D denoising methods were also performed on the same dataset for comparison. Peak SNR (PSNR) and normalized mean square error (NMSE) were calculated for the assessment of image qualities. STATISTICAL TESTS A paired Student's t-test was conducted to compare the diffusion parameter measurements between different approaches. P < 0.01 was considered statistically significant. RESULTS Simulation results showed substantial improvement of image quality after JD-CNN denoising (PSNR of original image: 23.15 ± 1.77; PSNR of denoised image: 42.94 ± 2.12). The proposed method outperforms the state-of-the-art methods on high b-value DWIs in terms of PSNR (TV: 33.51 ± 0.83, BM3D: 35.12 ± 0.94, JD-CNN: 46.52 ± 0.98). In addition, the NMSE of the estimated apparent diffusion coefficient (ADC) reduces from 0.72 ± 0.13 to 0.45 ± 0.06 (P < 0.01) with the application of the JD-CNN model. DATA CONCLUSION The proposed method is able to remove noise with a wide range of noise levels in multiple b-value DWI and improve the diffusion parameter estimation. This shows potential clinical promise. LEVEL OF EVIDENCE 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1937-1947.
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Affiliation(s)
- He Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Human Phenome Institute, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Rencheng Zheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Fei Dai
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Qianfeng Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Chengyan Wang
- Human Phenome Institute, Fudan University, Shanghai, China
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Li J, Liu Q, Zhao J. Self-prior image-guided MRI reconstruction with dictionary learning. Med Phys 2018; 46:517-527. [PMID: 30548875 DOI: 10.1002/mp.13337] [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: 06/18/2018] [Revised: 10/30/2018] [Accepted: 12/03/2018] [Indexed: 11/11/2022] Open
Abstract
PURPOSE A novel method, named self-prior image-guided MRI reconstruction with dictionary learning (SPIDLE), is developed to improve the performance of MR imaging with high acceleration rates. "self-prior" means that the prior image is obtained from the target image itself and any extra MRI scans are not needed. METHODS The proposed method integrates self-prior image constraint with compressed sensing (CS) and the dictionary learning (DL) technique. The self-prior image is a preliminary result reconstructed using the undersampled k-space measurements of the target image. Therefore, the self-prior image has similar structural features with the target image, and they match each other accurately. CS approach is applied to the residual error of the target image with the self-prior image, because the error image is much sparser than the target image. The split Bregman method is used to solve the proposed approach to promote fast convergence. For multicoil measurements, each coil image is reconstructed individually and the final result is produced as the square root of sum of squares (SOS) of all channel images. RESULTS The performance of the proposed SPIDLE method was inspected using different undersampling schemes and acceleration rates with various types of in vivo MR datasets. Experiments showed that the SPIDLE method is superior to other typical state-of-the-art methods. Specifically, the SPIDLE method produces fewer reconstruction errors, and it is robust to initialization. CONCLUSIONS The proposed SPIDLE method substantially widens the applications of prior image-guided MRI reconstruction, especially for applications that are not suitable to use existing MR scans as prior images. The SPIDLE method obviously improves the reconstruction quality for highly undersampled MRI. It is also promising for reconstruction of dynamic MRI and other imaging modalities, such as CT and CT-MRI multimodality imaging.
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Affiliation(s)
- Jiansen Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Qiegen Liu
- Department of Electronic Information Engineering, Nanchang University, Nanchang, 330031, China
| | - Jun Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.,SJTU-UIH Research Institute for Advanced Medical Imaging Technology, Shanghai Jiao Tong University, Shanghai, 200240, China.,MED-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200240, China
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MRI-based assessment of function and dysfunction in myelinated axons. Proc Natl Acad Sci U S A 2018; 115:E10225-E10234. [PMID: 30297414 DOI: 10.1073/pnas.1801788115] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Repetitive electrical activity produces microstructural alteration in myelinated axons, which may afford the opportunity to noninvasively monitor function of myelinated fibers in peripheral nervous system (PNS)/CNS pathways. Microstructural changes were assessed via two different magnetic-resonance-based approaches: diffusion fMRI and dynamic T2 spectroscopy in the ex vivo perfused bullfrog sciatic nerves. Using this robust, classical model as a platform for testing, we demonstrate that noninvasive diffusion fMRI, based on standard diffusion tensor imaging (DTI), can clearly localize the sites of axonal conduction blockage as might be encountered in neurotrauma or other lesion types. It is also shown that the diffusion fMRI response is graded in proportion to the total number of electrical impulses carried through a given locus. Dynamic T2 spectroscopy of the perfused frog nerves point to an electrical-activity-induced redistribution of tissue water and myelin structural changes. Diffusion basis spectrum imaging (DBSI) reveals a reversible shift of tissue water into a restricted isotropic diffusion signal component. Submyelinic vacuoles are observed in electron-microscopy images of tissue fixed during electrical stimulation. A slowing of the compound action potential conduction velocity accompanies repetitive electrical activity. Correlations between electrophysiology and MRI parameters during and immediately after stimulation are presented. Potential mechanisms and interpretations of these results are discussed.
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Bilgic B, Kim TH, Liao C, Manhard MK, Wald LL, Haldar JP, Setsompop K. Improving parallel imaging by jointly reconstructing multi-contrast data. Magn Reson Med 2018; 80:619-632. [PMID: 29322551 PMCID: PMC5910232 DOI: 10.1002/mrm.27076] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2017] [Revised: 12/10/2017] [Accepted: 12/15/2017] [Indexed: 12/14/2022]
Abstract
PURPOSE To develop parallel imaging techniques that simultaneously exploit coil sensitivity encoding, image phase prior information, similarities across multiple images, and complementary k-space sampling for highly accelerated data acquisition. METHODS We introduce joint virtual coil (JVC)-generalized autocalibrating partially parallel acquisitions (GRAPPA) to jointly reconstruct data acquired with different contrast preparations, and show its application in 2D, 3D, and simultaneous multi-slice (SMS) acquisitions. We extend the joint parallel imaging concept to exploit limited support and smooth phase constraints through Joint (J-) LORAKS formulation. J-LORAKS allows joint parallel imaging from limited autocalibration signal region, as well as permitting partial Fourier sampling and calibrationless reconstruction. RESULTS We demonstrate highly accelerated 2D balanced steady-state free precession with phase cycling, SMS multi-echo spin echo, 3D multi-echo magnetization-prepared rapid gradient echo, and multi-echo gradient recalled echo acquisitions in vivo. Compared to conventional GRAPPA, proposed joint acquisition/reconstruction techniques provide more than 2-fold reduction in reconstruction error. CONCLUSION JVC-GRAPPA takes advantage of additional spatial encoding from phase information and image similarity, and employs different sampling patterns across acquisitions. J-LORAKS achieves a more parsimonious low-rank representation of local k-space by considering multiple images as additional coils. Both approaches provide dramatic improvement in artifact and noise mitigation over conventional single-contrast parallel imaging reconstruction. Magn Reson Med 80:619-632, 2018. © 2018 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Tae Hyung Kim
- Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA
- Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA
| | - Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Mary Kate Manhard
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Lawrence L. Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Justin P. Haldar
- Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA
- Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
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Kafali SG, Çukur T, Saritas EU. Phase-correcting non-local means filtering for diffusion-weighted imaging of the spinal cord. Magn Reson Med 2018; 80:1020-1035. [PMID: 29427379 DOI: 10.1002/mrm.27105] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 12/26/2017] [Accepted: 01/02/2018] [Indexed: 11/09/2022]
Affiliation(s)
- Sevgi Gokce Kafali
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey.,National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey
| | - Tolga Çukur
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey.,National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey.,Neuroscience Program, Sabuncu Brain Research Center, Bilkent University, Ankara, Turkey
| | - Emine Ulku Saritas
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey.,National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey.,Neuroscience Program, Sabuncu Brain Research Center, Bilkent University, Ankara, Turkey
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Maggu J, Singh P, Majumdar A. Multi-echo reconstruction from partial K-space scans via adaptively learnt basis. Magn Reson Imaging 2018; 45:105-112. [DOI: 10.1016/j.mri.2017.09.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Revised: 09/07/2017] [Accepted: 09/24/2017] [Indexed: 11/24/2022]
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40
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Habibi A, Damasio A, Ilari B, Veiga R, Joshi AA, Leahy RM, Haldar JP, Varadarajan D, Bhushan C, Damasio H. Childhood Music Training Induces Change in Micro and Macroscopic Brain Structure: Results from a Longitudinal Study. Cereb Cortex 2017; 28:4336-4347. [DOI: 10.1093/cercor/bhx286] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Accepted: 10/06/2017] [Indexed: 12/21/2022] Open
Affiliation(s)
- Assal Habibi
- Brain and Creativity Institute, Dornsife College of Letters, Arts and Sciences, University of Southern California, CA, USA
| | - Antonio Damasio
- Brain and Creativity Institute, Dornsife College of Letters, Arts and Sciences, University of Southern California, CA, USA
| | - Beatriz Ilari
- Thornton School of Music, University of Southern California, CA, USA
| | - Ryan Veiga
- Brain and Creativity Institute, Dornsife College of Letters, Arts and Sciences, University of Southern California, CA, USA
| | - Anand A Joshi
- Brain and Creativity Institute, Dornsife College of Letters, Arts and Sciences, University of Southern California, CA, USA
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical Engineering, University of Southern California, CA, USA
| | - Richard M Leahy
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical Engineering, University of Southern California, CA, USA
| | - Justin P Haldar
- Brain and Creativity Institute, Dornsife College of Letters, Arts and Sciences, University of Southern California, CA, USA
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical Engineering, University of Southern California, CA, USA
| | - Divya Varadarajan
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical Engineering, University of Southern California, CA, USA
| | - Chitresh Bhushan
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical Engineering, University of Southern California, CA, USA
| | - Hanna Damasio
- Brain and Creativity Institute, Dornsife College of Letters, Arts and Sciences, University of Southern California, CA, USA
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41
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Recent Computational Advances in Denoising for Magnetic Resonance Diffusional Kurtosis Imaging (DKI). J Indian Inst Sci 2017. [DOI: 10.1007/s41745-017-0036-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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42
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Bustin A, Ferry P, Codreanu A, Beaumont M, Liu S, Burschka D, Felblinger J, Brau ACS, Menini A, Odille F. Impact of denoising on precision and accuracy of saturation-recovery-based myocardial T 1 mapping. J Magn Reson Imaging 2017; 46:1377-1388. [PMID: 28376285 DOI: 10.1002/jmri.25684] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 02/07/2017] [Indexed: 11/10/2022] Open
Abstract
PURPOSE To evaluate the impact of a novel postprocessing denoising technique on accuracy and precision in myocardial T1 mapping. MATERIALS AND METHODS This study introduces a fast and robust denoising method developed for magnetic resonance T1 mapping. The technique imposes edge-preserving regularity and exploits the co-occurence of spatial gradients in the acquired T1 -weighted images. The proposed approach was assessed in simulations, ex vivo data and in vivo imaging on a cohort of 16 healthy volunteers (12 males, average age 39 ± 8 years, 62 ± 9 bpm) both in pre- and postcontrast injection. The method was evaluated in myocardial T1 mapping at 3T with a saturation-recovery technique that is accurate but sensitive to noise. ROIs in the myocardium and left-ventricle blood pool were analyzed by an experienced reader. Mean T1 values and standard deviation were extracted and compared in all studies. RESULTS Simulations on synthetic phantom showed signal-to-noise ratio and sharpness improvement with the proposed method in comparison with conventional denoising. In vivo results demonstrated that our method preserves accuracy, as no difference in mean T1 values was observed in the myocardium (precontrast: 1433/1426 msec, 95%CI: [-40.7, 55.9], p = 0.75, postcontrast: 766/759 msec, 95%CI: [-60.7, 77.2], p = 0.8). Meanwhile, precision was improved with standard deviations of T1 values being significantly decreased (precontrast: 223/151 msec, 95%CI: [27.3, 116.5], p = 0.003, postcontrast: 176/135 msec, 95%CI: [5.5, 77.1], p = 0.03). CONCLUSION The proposed denoising method preserves accuracy and improves precision in myocardial T1 mapping, with the potential to offer better map visualization and analysis. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1377-1388.
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Affiliation(s)
- Aurélien Bustin
- GE Global Research, Munich, Germany.,Department of Computer Science, Technische Universität München, Munich, Germany.,Imagerie Adaptative Diagnostique et Interventionnelle, INSERM U947 et Université de Lorraine, Nancy, France
| | - Pauline Ferry
- Imagerie Adaptative Diagnostique et Interventionnelle, INSERM U947 et Université de Lorraine, Nancy, France
| | - Andrei Codreanu
- Service de Cardiologie, Centre Hospitalier de Luxembourg, Luxembourg
| | - Marine Beaumont
- Imagerie Adaptative Diagnostique et Interventionnelle, INSERM U947 et Université de Lorraine, Nancy, France.,CIC-IT 1433, INSERM, Université de Lorraine, CHRU de Nancy, Nancy, France
| | - Shufang Liu
- GE Global Research, Munich, Germany.,Department of Computer Science, Technische Universität München, Munich, Germany
| | - Darius Burschka
- Department of Computer Science, Technische Universität München, Munich, Germany
| | - Jacques Felblinger
- Imagerie Adaptative Diagnostique et Interventionnelle, INSERM U947 et Université de Lorraine, Nancy, France.,CIC-IT 1433, INSERM, Université de Lorraine, CHRU de Nancy, Nancy, France
| | - Anja C S Brau
- GE Healthcare, Cardiac Center of Excellence, Munich, Germany
| | | | - Freddy Odille
- Imagerie Adaptative Diagnostique et Interventionnelle, INSERM U947 et Université de Lorraine, Nancy, France.,CIC-IT 1433, INSERM, Université de Lorraine, CHRU de Nancy, Nancy, France
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Damon BM, Froeling M, Buck AKW, Oudeman J, Ding Z, Nederveen AJ, Bush EC, Strijkers GJ. Skeletal muscle diffusion tensor-MRI fiber tracking: rationale, data acquisition and analysis methods, applications and future directions. NMR IN BIOMEDICINE 2017; 30:10.1002/nbm.3563. [PMID: 27257975 PMCID: PMC5136336 DOI: 10.1002/nbm.3563] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Revised: 03/19/2016] [Accepted: 04/27/2016] [Indexed: 05/21/2023]
Abstract
The mechanical functions of muscles involve the generation of force and the actuation of movement by shortening or lengthening under load. These functions are influenced, in part, by the internal arrangement of muscle fibers with respect to the muscle's mechanical line of action. This property is known as muscle architecture. In this review, we describe the use of diffusion tensor (DT)-MRI muscle fiber tracking for the study of muscle architecture. In the first section, the importance of skeletal muscle architecture to function is discussed. In addition, traditional and complementary methods for the assessment of muscle architecture (brightness-mode ultrasound imaging and cadaver analysis) are presented. Next, DT-MRI is introduced and the structural basis for the reduced and anisotropic diffusion of water in muscle is discussed. The third section discusses issues related to the acquisition of skeletal muscle DT-MRI data and presents recommendations for optimal strategies. The fourth section discusses methods for the pre-processing of DT-MRI data, the available approaches for the calculation of the diffusion tensor and the seeding and propagating of fiber tracts, and the analysis of the tracking results to measure structural properties pertinent to muscle biomechanics. Lastly, examples are presented of how DT-MRI fiber tracking has been used to provide new insights into how muscles function, and important future research directions are highlighted. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Bruce M. Damon
- Institute of Imaging Science, Vanderbilt University, Nashville TN USA
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville TN USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville TN USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville TN USA
| | - Martijn Froeling
- Department of Radiology, University Medical Center, Utrecht, the Netherlands
| | - Amanda K. W. Buck
- Institute of Imaging Science, Vanderbilt University, Nashville TN USA
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville TN USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville TN USA
| | - Jos Oudeman
- Department of Radiology, Academic Medical Center, Amsterdam, the Netherlands
| | - Zhaohua Ding
- Institute of Imaging Science, Vanderbilt University, Nashville TN USA
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville TN USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville TN USA
- Department of Electrical Engineering and Computer Engineering, Vanderbilt University, Nashville TN USA
| | - Aart J. Nederveen
- Department of Radiology, Academic Medical Center, Amsterdam, the Netherlands
| | - Emily C. Bush
- Institute of Imaging Science, Vanderbilt University, Nashville TN USA
| | - Gustav J. Strijkers
- Department of Biomedical Engineering and Physics, Academic Medical Center, Amsterdam, the Netherlands
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Sperl JI, Sprenger T, Tan ET, Menzel MI, Hardy CJ, Marinelli L. Model-based denoising in diffusion-weighted imaging using generalized spherical deconvolution. Magn Reson Med 2017; 78:2428-2438. [PMID: 28244188 DOI: 10.1002/mrm.26626] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Revised: 12/14/2016] [Accepted: 01/09/2017] [Indexed: 12/30/2022]
Abstract
PURPOSE Diffusion MRI often suffers from low signal-to-noise ratio, especially for high b-values. This work proposes a model-based denoising technique to address this limitation. METHODS A generalization of the multi-shell spherical deconvolution model using a Richardson-Lucy algorithm is applied to noisy data. The reconstructed coefficients are then used in the forward model to compute denoised diffusion-weighted images (DWIs). The proposed method operates in the diffusion space and thus is complementary to image-based denoising methods. RESULTS We demonstrate improved image quality on the DWIs themselves, maps of neurite orientation dispersion and density imaging, and diffusional kurtosis imaging (DKI), as well as reduced spurious peaks in deterministic tractography. For DKI in particular, we observe up to 50% error reduction and demonstrate high image quality using just 30 DWIs. This corresponds to greater than fourfold reduction in scan time if compared to the widely used 140-DWI acquisitions. We also confirm consistent performance in pathological data sets, namely in white matter lesions of a multiple sclerosis patient. CONCLUSION The proposed denoising technique termed generalized spherical deconvolution has the potential of significantly improving image quality in diffusion MRI. Magn Reson Med 78:2428-2438, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
| | - Tim Sprenger
- GE Global Research, Munich, Germany.,Technische Universität München, Institute of Medical Engineering, Munich, Germany
| | - Ek T Tan
- GE Global Research, Niskayuna, New York, USA
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Chatnuntawech I, Martin A, Bilgic B, Setsompop K, Adalsteinsson E, Schiavi E. Vectorial total generalized variation for accelerated multi-channel multi-contrast MRI. Magn Reson Imaging 2016; 34:1161-70. [DOI: 10.1016/j.mri.2016.05.014] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2015] [Accepted: 05/30/2016] [Indexed: 11/24/2022]
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46
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47
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Huang J, Wang L, Chu C, Zhang Y, Liu W, Zhu Y. Cardiac diffusion tensor imaging based on compressed sensing using joint sparsity and low-rank approximation. Technol Health Care 2016; 24 Suppl 2:S593-9. [PMID: 27163322 DOI: 10.3233/thc-161186] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Diffusion tensor magnetic resonance (DTMR) imaging and diffusion tensor imaging (DTI) have been widely used to probe noninvasively biological tissue structures. However, DTI suffers from long acquisition times, which limit its practical and clinical applications. This paper proposes a new Compressed Sensing (CS) reconstruction method that employs joint sparsity and rank deficiency to reconstruct cardiac DTMR images from undersampled k-space data. Diffusion-weighted images acquired in different diffusion directions were firstly stacked as columns to form the matrix. The matrix was row sparse in the transform domain and had a low rank. These two properties were then incorporated into the CS reconstruction framework. The underlying constrained optimization problem was finally solved by the first-order fast method. Experiments were carried out on both simulation and real human cardiac DTMR images. The results demonstrated that the proposed approach had lower reconstruction errors for DTI indices, including fractional anisotropy (FA) and mean diffusivities (MD), compared to the existing CS-DTMR image reconstruction techniques.
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Affiliation(s)
- Jianping Huang
- Metislab, LIA CNRS, Harbin Institute of Technology, Harbin, Heilongjiang, China.,CREATIS, INSA Lyon, INSA Lyon, University of Lyon, Villeurbanne, France
| | - Lihui Wang
- College of Computer Science and Technology, Guizhou University, Guiyang, Guizhou, China
| | - Chunyu Chu
- Metislab, LIA CNRS, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Yanli Zhang
- Metislab, LIA CNRS, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Wanyu Liu
- Metislab, LIA CNRS, Harbin Institute of Technology, Harbin, Heilongjiang, China.,CREATIS, INSA Lyon, INSA Lyon, University of Lyon, Villeurbanne, France
| | - Yuemin Zhu
- Metislab, LIA CNRS, Harbin Institute of Technology, Harbin, Heilongjiang, China.,CREATIS, INSA Lyon, INSA Lyon, University of Lyon, Villeurbanne, France
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Odille F, Menini A, Escanyé JM, Vuissoz PA, Marie PY, Beaumont M, Felblinger J. Joint Reconstruction of Multiple Images and Motion in MRI: Application to Free-Breathing Myocardial T₂Quantification. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:197-207. [PMID: 26259015 DOI: 10.1109/tmi.2015.2463088] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Exploiting redundancies between multiple images of an MRI examination can be formalized as the joint reconstruction of these images. The anatomy is preserved indeed so that specific constraints can be implemented (e.g. most of the features or spatial gradients should be in the same place in all these images) and only the contrast changes from one image to another need to be encoded. The application of this concept is particularly challenging in cardiovascular and body imaging due to the complex organ deformations, especially with the patient breathing. In this study a joint optimization framework is proposed for reconstructing multiple MR images together with a nonrigid motion model. The motion model takes into account both intra-image and inter-image motion and therefore can correct for most ghosting/blurring artifacts and misregistration between images. The framework was validated with free-breathing myocardial T2 mapping experiments from nine heart transplant patients at 1.5 T. Results showed improved image quality and excellent image alignment with the multi-image reconstruction compared to the independent reconstruction of each image. Segment-wise myocardial T2 values were in good agreement with the reference values obtained from multiple breath-holds (62.5 ± 11.1 ms against 62.2 ± 11.2 ms which was not significant with p=0.49).
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Varadarajan D, Haldar JP. A majorize-minimize framework for Rician and non-central chi MR images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:2191-202. [PMID: 25935028 PMCID: PMC4596756 DOI: 10.1109/tmi.2015.2427157] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
The statistics of many MR magnitude images are described by the non-central chi (NCC) family of probability distributions, which includes the Rician distribution as a special case. These distributions have complicated negative log-likelihoods that are nontrivial to optimize. This paper describes a novel majorize-minimize framework for NCC data that allows penalized maximum likelihood estimates to be obtained by solving a series of much simpler regularized least-squares surrogate problems. The proposed framework is general and can be useful in a range of applications. We illustrate the potential advantages of the framework with real and simulated data in two contexts: 1) MR image denoising and 2) diffusion profile estimation in high angular resolution diffusion MRI. The proposed approach is shown to yield improved results compared to methods that model the noise statistics inaccurately and faster computation relative to commonly-used nonlinear optimization techniques.
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Shen CY, Tyan YS, Kuo LW, Wu CW, Weng JC. Quantitative Evaluation of Rabbit Brain Injury after Cerebral Hemisphere Radiation Exposure Using Generalized q-Sampling Imaging. PLoS One 2015; 10:e0133001. [PMID: 26168047 PMCID: PMC4500591 DOI: 10.1371/journal.pone.0133001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2014] [Accepted: 06/23/2015] [Indexed: 01/09/2023] Open
Abstract
Radiation therapy is widely used for the treatment of brain tumors and may result in cellular, vascular and axonal injury and further behavioral deficits. The non-invasive longitudinal imaging assessment of brain injury caused by radiation therapy is important for determining patient prognoses. Several rodent studies have been performed using magnetic resonance imaging (MRI), but further studies in rabbits and large mammals with advanced magnetic resonance (MR) techniques are needed. Previously, we used diffusion tensor imaging (DTI) to evaluate radiation-induced rabbit brain injury. However, DTI is unable to resolve the complicated neural structure changes that are frequently observed during brain injury after radiation exposure. Generalized q-sampling imaging (GQI) is a more accurate and sophisticated diffusion MR approach that can extract additional information about the altered diffusion environments. Therefore, herein, a longitudinal study was performed that used GQI indices, including generalized fractional anisotropy (GFA), quantitative anisotropy (QA), and the isotropic value (ISO) of the orientation distribution function and DTI indices, including fractional anisotropy (FA) and mean diffusivity (MD) over a period of approximately half a year to observe long-term, radiation-induced changes in the different brain compartments of a rabbit model after a hemi-brain single dose (30 Gy) radiation exposure. We revealed that in the external capsule, the GFA right to left (R/L) ratio showed similar trends as the FA R/L ratio, but no clear trends in the remaining three brain compartments. Both the QA and ISO R/L ratios showed similar trends in the all four different compartments during the acute to early delayed post-irradiation phase, which could be explained and reflected the histopathological changes of the complicated dynamic interactions among astrogliosis, demyelination and vasogenic edema. We suggest that GQI is a promising non-invasive technique and as compared with DTI, it has better potential ability in detecting and monitoring the pathophysiological cascades in acute to early delayed radiation-induced brain injury by using clinical MR scanners.
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Affiliation(s)
- Chao-Yu Shen
- School of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung, Taiwan
- Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Yeu-Sheng Tyan
- School of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung, Taiwan
- Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Li-Wei Kuo
- Division of Medical Engineering Research, National Health Research Institutes, Miaoli County, Taiwan
| | - Changwei W. Wu
- Graduate Institute of Biomedical Engineering, National Central University, Taoyuan, Taiwan
| | - Jun-Cheng Weng
- School of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung, Taiwan
- Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung, Taiwan
- * E-mail:
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