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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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Rauh SS, Maier O, Gurney-Champion OJ, Hooijmans MT, Stollberger R, Nederveen AJ, Strijkers GJ. Model-based reconstructions for intravoxel incoherent motion and diffusion tensor imaging parameter map estimations. NMR IN BIOMEDICINE 2023:e4927. [PMID: 36932842 DOI: 10.1002/nbm.4927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 01/16/2023] [Accepted: 03/06/2023] [Indexed: 06/18/2023]
Abstract
Intravoxel incoherent motion (IVIM) imaging and diffusion tensor imaging (DTI) facilitate noninvasive quantification of tissue perfusion and diffusion. Both are promising biomarkers in various diseases and a combined acquisition is therefore desirable. This comes with challenges, including noisy parameter maps and long scan times, especially for the perfusion fraction f and pseudo-diffusion coefficient D*. A model-based reconstruction has the potential to overcome these challenges. As a first step, our goal was to develop a model-based reconstruction framework for IVIM and combined IVIM-DTI parameter estimation. The IVIM and IVIM-DTI models were implemented in the PyQMRI model-based reconstruction framework and validated with simulations and in vivo data. Commonly used voxel-wise nonlinear least-squares fitting was used as the reference. Simulations with the IVIM and IVIM-DTI models were performed with 100 noise realizations to assess accuracy and precision. Diffusion-weighted data were acquired for IVIM reconstruction in the liver (n = 5), as well as for IVIM-DTI in the kidneys (n = 5) and lower-leg muscles (n = 6) of healthy volunteers. The median and interquartile range (IQR) values of the IVIM and IVIM-DTI parameters were compared to assess bias and precision. With model-based reconstruction, the parameter maps exhibited less noise, which was most pronounced in the f and D* maps, both in the simulations and in vivo. The bias values in the simulations were comparable between model-based reconstruction and the reference method. The IQR was lower with model-based reconstruction compared with the reference for all parameters. In conclusion, model-based reconstruction is feasible for IVIM and IVIM-DTI and improves the precision of the parameter estimates, particularly for f and D* maps.
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Affiliation(s)
- Susanne S Rauh
- Department of Biomedical Engineering and Physics, Amsterdam UMC, Amsterdam Movement Sciences, University of Amsterdam, The Netherlands
| | - Oliver Maier
- Institute of Medical Engineering, Graz University of Technology, Graz, Austria
| | - Oliver J Gurney-Champion
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam Movement Sciences, University of Amsterdam, The Netherlands
| | - Melissa T Hooijmans
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam Movement Sciences, University of Amsterdam, The Netherlands
| | - Rudolf Stollberger
- Institute of Medical Engineering, Graz University of Technology, Graz, Austria
| | - Aart J Nederveen
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam Movement Sciences, University of Amsterdam, The Netherlands
| | - Gustav J Strijkers
- Department of Biomedical Engineering and Physics, Amsterdam UMC, Amsterdam Movement Sciences, University of Amsterdam, The Netherlands
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Shafieizargar B, Jeurissen B, Poot DHJ, Klein S, Van Audekerke J, Verhoye M, den Dekker AJ, Sijbers J. ADEPT: Accurate Diffusion Echo‐Planar imaging with multi‐contrast shoTs. Magn Reson Med 2022; 89:396-410. [DOI: 10.1002/mrm.29398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 06/10/2022] [Accepted: 07/04/2022] [Indexed: 11/08/2022]
Affiliation(s)
- Banafshe Shafieizargar
- imec‐Vision Lab, Department of Physics University of Antwerp Antwerp Belgium
- NEURO Research Centre of Excellence University of Antwerp Antwerp Belgium
| | - Ben Jeurissen
- imec‐Vision Lab, Department of Physics University of Antwerp Antwerp Belgium
- NEURO Research Centre of Excellence University of Antwerp Antwerp Belgium
| | - Dirk H. J. Poot
- Department of Radiology and Nuclear Medicine, Biomedical Imaging Group Rotterdam Erasmus MC Rotterdam The Netherlands
| | - Stefan Klein
- Department of Radiology and Nuclear Medicine, Biomedical Imaging Group Rotterdam Erasmus MC Rotterdam The Netherlands
| | - Johan Van Audekerke
- NEURO Research Centre of Excellence University of Antwerp Antwerp Belgium
- Bio‐Imaging Lab, Department of Biomedical Sciences University of Antwerp Antwerp Belgium
| | - Marleen Verhoye
- NEURO Research Centre of Excellence University of Antwerp Antwerp Belgium
- Bio‐Imaging Lab, Department of Biomedical Sciences University of Antwerp Antwerp Belgium
| | - Arnold J. den Dekker
- imec‐Vision Lab, Department of Physics University of Antwerp Antwerp Belgium
- NEURO Research Centre of Excellence University of Antwerp Antwerp Belgium
| | - Jan Sijbers
- imec‐Vision Lab, Department of Physics University of Antwerp Antwerp Belgium
- NEURO Research Centre of Excellence University of Antwerp Antwerp Belgium
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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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Guo L, Lyu J, Zhang Z, Shi J, Feng Q, Feng Y, Gao M, Zhang X. A Joint Framework for Denoising and Estimating Diffusion Kurtosis Tensors Using Multiple Prior Information. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:308-319. [PMID: 34520348 DOI: 10.1109/tmi.2021.3112515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Diffusion kurtosis imaging (DKI) has been shown to be valuable in a wide range of neuroscientific and clinical applications. However, reliable estimation of DKI tensors is often compromised by noise, especially for the kurtosis tensor (KT). Here, we propose a joint denoising and estimating framework that integrates multiple sources of prior information, including nonlocal structural self-similarity (NSS), local spatial smoothness (LSS), physical relevance (PR) of the DKI model, and noise characteristics of magnitude diffusion MRI (dMRI) images for improved estimation of DKI tensors. The local and nonlocal spatial smoothing constraints are complementary to each other, making the proposed framework highly effective in reducing the noise fluctuations on DKI tensors, especially KT. As an additional refinement, we propose to impose a physically relevant constraint within our joint denoising and estimation framework. We further adopt the first-moment noise-corrected fitting model (M1NCM) to remove the noncentral χ -distribution noise bias. The effectiveness of integrating multiple sources of priors into the joint framework is verified by comparing the proposed M1NCM-NSS-LSS-PR method with various versions of M1NCM-based estimators and two state-of-the-art methods. Results show that the proposed method outperformed the compared methods in simulations and in-vivo dMRI datasets of both spatially stationary and nonstationary noise distributions. The in-vivo experiments also show that the proposed M1NCM-NSS-LSS-PR method was robust to the number of diffusion directions.
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Shafieizargar B, Jeurissen B, Poot DHJ, den Dekker AJ, Sijbers J. Multi-contrast multi-shot EPI for accelerated diffusion MRI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3869-3872. [PMID: 34890324 DOI: 10.1109/embc46164.2021.9630069] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The clinical application of diffusion MRI is practically hindered by its long scan time. In this work, we introduce a novel imaging and parameter estimation framework for time-efficient diffusion MRI. To improve the scan efficiency, we propose ADEPT (Accelerated Diffusion EPI with multi-contrast shoTs), in which diffusion contrast settings are allowed to change between shots in a multi-shot EPI acquisition (i.e. intra-scan modulation). The framework simultaneously corrects for artifacts related to shot-to-shot phase inconsistencies in multi-shot imaging by iteratively estimating the phase map parameters along with the diffusion model parameters directly from the acquired intra-scan modulated k-space data. Monte Carlo simulation experiments show the effective estimation of diffusion tensor parameters in multi-shot EPI diffusion imaging.
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Hüfken T, Arbogast JM, Bracher AK, Beer M, Neubauer H, Rasche V. Accelerated model-based quantitative diffusion MRI: A feasibility study for musculoskeletal application. Z Med Phys 2021; 32:240-247. [PMID: 34175164 PMCID: PMC9948881 DOI: 10.1016/j.zemedi.2021.04.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 03/17/2021] [Accepted: 04/13/2021] [Indexed: 12/25/2022]
Abstract
PURPOSE To develop a model-based reconstruction technique for diffusion quantification based on accelerated two-dimensional echo planar data, obtained with multiple b-weightings. In combination with a dedicated undersampling pattern, acceleration factors above three were proven feasible in a clinical setting. METHODS The proposed model-based method minimizes a cost function considering the l2-norm of the difference between the Fourier transformation of a synthetic diffusion-model-generated k-space and the measured k-space data. Further regularization is performed by introduction of a total variation (TV) constraint to the cost function. Acceleration is achieved by a non-random undersampling pattern using acceleration factors that correspond to the total number of b-values. A rectangular region of variable size, centered in k-space, remains fully sampled for correction of phase variations, introduced by the different diffusion-encoding strengths. RESULTS Qualitative analysis of the resulting images (S0 and ADC) demonstrates the potential of the suggested undersampling pattern in combination with a model-based iterative reconstruction. An edge analysis highlights the preservation of high-frequency information for all investigated undersampling factors. In comparison to a conventional SENSE-accelerated reconstruction, the quantitative analysis of the ADC maps revealed a significantly (P<0.05) superior performance of the suggested technique, enabling acceleration factors of R=3.65 without compromising diffusion data fidelity. CONCLUSION The presented work shows the potential of model-based ADC quantification, which, in combination with a suited undersampling pattern for multiple b-values, enables more than three-fold acceleration using two-dimensional EPI without sacrificing ADC fidelity.
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Affiliation(s)
- Thomas Hüfken
- Department of Internal Medicine II, Ulm University Medical Center, Ulm, BW, Germany
| | - Jannik M. Arbogast
- Department of Internal Medicine II, Ulm University Medical Center, Ulm, BW, Germany
| | | | - Meinrad Beer
- Department of Radiology, Ulm University Medical Center, Ulm, BW, Germany
| | - Henning Neubauer
- Department of Radiology, Ulm University Medical Center, Ulm, BW, Germany
| | - Volker Rasche
- Department of Internal Medicine II, Ulm University Medical Center, Ulm, BW, Germany.
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Variable flip angle echo planar time-resolved imaging (vFA-EPTI) for fast high-resolution gradient echo myelin water imaging. Neuroimage 2021; 232:117897. [PMID: 33621694 PMCID: PMC8221177 DOI: 10.1016/j.neuroimage.2021.117897] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/01/2021] [Accepted: 02/16/2021] [Indexed: 12/14/2022] Open
Abstract
Myelin water imaging techniques based on multi-compartment relaxometry have been developed as an important tool to measure myelin concentration in vivo, but are limited by the long scan time of multi-contrast multi-echo acquisition. In this work, a fast imaging technique, termed variable flip angle Echo Planar Time-Resolved Imaging (vFA-EPTI), is developed to acquire multi-echo and multi-flip-angle gradient-echo data with significantly reduced acquisition time, providing rich information for multi-compartment analysis of gradient-echo myelin water imaging (GRE-MWI). The proposed vFA-EPTI method achieved 26 folds acceleration with good accuracy by utilizing an efficient continuous readout, optimized spatiotemporal encoding across echoes and flip angles, as well as a joint subspace reconstruction. An approach to estimate off-resonance field changes between different flip-angle acquisitions was also developed to ensure high-quality joint reconstruction across flip angles. The accuracy of myelin water fraction (MWF) estimate under high acceleration was first validated by a retrospective undersampling experiment using a lengthy fully-sampled data as reference. Prospective experiments were then performed where whole-brain MWF and multi-compartment quantitative maps were obtained in 5 min at 1.5 mm isotropic resolution and 24 min at 1 mm isotropic resolution at 3T. Additionally, ultra-high resolution data at 600 μm isotropic resolution were acquired at 7T, which show detailed structures within the cortex such as the line of Gennari, demonstrating the ability of the proposed method for submillimeter GRE-MWI that can be used to study cortical myeloarchitecture in vivo.
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Sagawa H, Fushimi Y, Nakajima S, Fujimoto K, Miyake KK, Numamoto H, Koizumi K, Nambu M, Kataoka H, Nakamoto Y, Saga T. Deep Learning-based Noise Reduction for Fast Volume Diffusion Tensor Imaging: Assessing the Noise Reduction Effect and Reliability of Diffusion Metrics. Magn Reson Med Sci 2020; 20:450-456. [PMID: 32963184 PMCID: PMC8922344 DOI: 10.2463/mrms.tn.2020-0061] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
To assess the feasibility of a denoising approach with deep learning-based reconstruction (dDLR) for fast volume simultaneous multi-slice diffusion tensor imaging of the brain, noise reduction effects and the reliability of diffusion metrics were evaluated with 20 patients. Image noise was significantly decreased with dDLR. Although fractional anisotropy (FA) of deep gray matter was overestimated when the number of image acquisitions was one (NAQ1), FA in NAQ1 with dDLR became closer to that in NAQ5.
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Affiliation(s)
- Hajime Sagawa
- Division of Clinical Radiology Service, Kyoto University Hospital
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University
| | - Satoshi Nakajima
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University
| | - Koji Fujimoto
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University
| | - Kanae Kawai Miyake
- Department of Advanced Medical Imaging Research, Graduate School of Medicine, Kyoto University
| | - Hitomi Numamoto
- Department of Advanced Medical Imaging Research, Graduate School of Medicine, Kyoto University
| | - Koji Koizumi
- Division of Clinical Radiology Service, Kyoto University Hospital
| | | | - Hiroharu Kataoka
- Department of Neurosurgery, Graduate School of Medicine, Kyoto University
| | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University
| | - Tsuneo Saga
- Department of Advanced Medical Imaging Research, Graduate School of Medicine, Kyoto University
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10
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Dong Z, Wang F, Reese TG, Bilgic B, Setsompop K. Echo planar time-resolved imaging with subspace reconstruction and optimized spatiotemporal encoding. Magn Reson Med 2020; 84:2442-2455. [PMID: 32333478 DOI: 10.1002/mrm.28295] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 03/01/2020] [Accepted: 03/31/2020] [Indexed: 12/17/2022]
Abstract
PURPOSE To develop new encoding and reconstruction techniques for fast multi-contrast/quantitative imaging. METHODS The recently proposed Echo Planar Time-resolved Imaging (EPTI) technique can achieve fast distortion- and blurring-free multi-contrast/quantitative imaging. In this work, a subspace reconstruction framework is developed to improve the reconstruction accuracy of EPTI at high encoding accelerations. The number of unknowns in the reconstruction is significantly reduced by modeling the temporal signal evolutions using low-rank subspace. As part of the proposed reconstruction approach, a B0 -update algorithm and a shot-to-shot B0 variation correction method are developed to enable the reconstruction of high-resolution tissue phase images and to mitigate artifacts from shot-to-shot phase variations. Moreover, the EPTI concept is extended to 3D k-space for 3D GE-EPTI, where a new "temporal-variant" of CAIPI encoding is proposed to further improve performance. RESULTS The effectiveness of the proposed subspace reconstruction was demonstrated first in 2D GESE EPTI, where the reconstruction achieved higher accuracy when compared to conventional B0 -informed GRAPPA. For 3D GE-EPTI, a retrospective undersampling experiment demonstrates that the new temporal-variant CAIPI encoding can achieve up to 72× acceleration with close to 2× reduction in reconstruction error when compared to conventional spatiotemporal-CAIPI encoding. In a prospective undersampling experiment, high-quality whole-brain T 2 ∗ and tissue phase maps at 1 mm isotropic resolution were acquired in 52 seconds at 3T using 3D GE-EPTI with temporal-variant CAIPI encoding. CONCLUSION The proposed subspace reconstruction and optimized temporal-variant CAIPI encoding can further improve the performance of EPTI for fast quantitative mapping.
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Affiliation(s)
- Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts, USA
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | - Timothy G Reese
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
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11
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Wáng YXJ, Wang X, Wu P, Wang Y, Chen W, Chen H, Li J. Topics on quantitative liver magnetic resonance imaging. Quant Imaging Med Surg 2019; 9:1840-1890. [PMID: 31867237 DOI: 10.21037/qims.2019.09.18] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Liver magnetic resonance imaging (MRI) is subject to continuous technical innovations through advances in hardware, sequence and novel contrast agent development. In order to utilize the abilities of liver MR to its full extent and perform high-quality efficient exams, it is mandatory to use the best imaging protocol, to minimize artifacts and to select the most adequate type of contrast agent. In this article, we review the routine clinical MR techniques applied currently and some latest developments of liver imaging techniques to help radiologists and technologists to better understand how to choose and optimize liver MRI protocols that can be used in clinical practice. This article covers topics on (I) fat signal suppression; (II) diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) analysis; (III) dynamic contrast-enhanced (DCE) MR imaging; (IV) liver fat quantification; (V) liver iron quantification; and (VI) scan speed acceleration.
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Affiliation(s)
- Yì Xiáng J Wáng
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, New Territories, Hong Kong SAR, China
| | | | - Peng Wu
- Philips Healthcare (Suzhou) Co., Ltd., Suzhou 215024, China
| | - Yajie Wang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Weibo Chen
- Philips Healthcare, Shanghai 200072, China.,Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China
| | - Huijun Chen
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Jianqi Li
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China
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12
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Huang J, Wang L, Chu C, Liu W, Zhu Y. Accelerating cardiac diffusion tensor imaging combining local low-rank and 3D TV constraint. MAGMA (NEW YORK, N.Y.) 2019; 32:407-422. [PMID: 30903326 DOI: 10.1007/s10334-019-00747-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Revised: 03/08/2019] [Accepted: 03/11/2019] [Indexed: 06/09/2023]
Abstract
OBJECTIVE Diffusion tensor magnetic resonance imaging (DT-MRI, or DTI) is a promising technique for invasively probing biological tissue structures. However, DTI is known to suffer from much longer acquisition time with respect to conventional MRI and the problem is worsened when dealing with in vivo acquisitions. Therefore, faster DTI for both ex vivo and in vivo scans is highly desired. MATERIALS AND METHODS This paper proposes a new compressed sensing (CS) reconstruction method that employs local low-rank (LLR) model and three-dimensional (3D) total variation (TV) constraint to reconstruct cardiac diffusion-weighted (DW) images from highly undersampled k-space data. The LLR model takes the set of DW images corresponding to different diffusion gradient directions as a 3D image volume and decomposes the latter into overlapping 3D blocks. Then, the 3D blocks are stacked as two-dimensional (2D) matrix. Finally, low-rank property is applied to each block matrix and the 3D TV constraint to the 3D image volume. The underlying constrained optimization problem is finally solved using the first-order fast method. The proposed method is evaluated on real ex vivo cardiac DTI data as a prerequisite to in vivo cardiac DTI applications. RESULTS The results on real human ex vivo cardiac DTI images demonstrate that the proposed method exhibits lower reconstruction errors for DTI indices, including fractional anisotropy (FA), mean diffusivities (MD), transverse angle (TA), and helix angle (HA), compared to existing CS-based DTI image reconstruction techniques. CONCLUSION The proposed method provides better reconstruction quality and more accurate DTI indices in comparison with the state-of-the-art CS-based DW image reconstruction methods.
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Affiliation(s)
- Jianping Huang
- College of Mechanical and Electrical Engineering, Northeast Forestry University, Heilongjiang, 150040, Harbin, China.
- Metislab, LIA CNRS, Harbin Institute of Technology, Heilongjiang, 150001, Harbin, China.
- CREATIS, CNRS UMR5220, Inserm U1206, INSA Lyon, University of Lyon, Lyon, France.
| | - Lihui Wang
- Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Chunyu Chu
- College of Engineering, Bohai University, Jinzhou, 121013, China
| | - Wanyu Liu
- Metislab, LIA CNRS, Harbin Institute of Technology, Heilongjiang, 150001, Harbin, China
| | - Yuemin Zhu
- Metislab, LIA CNRS, Harbin Institute of Technology, Heilongjiang, 150001, Harbin, China
- CREATIS, CNRS UMR5220, Inserm U1206, INSA Lyon, University of Lyon, Lyon, France
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Xu Z, Huang F, Wu Z, Mei Y, Jeong HK, Fang W, Chen Z, Wang Y, Dong Z, Guo H, Zhang X, Chen W, Feng Q, Feng Y. Technical Note: Clustering-based motion compensation scheme for multishot diffusion tensor imaging. Med Phys 2018; 45:5515-5524. [PMID: 30307624 DOI: 10.1002/mp.13232] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 09/26/2018] [Accepted: 09/28/2018] [Indexed: 12/25/2022] Open
Abstract
PURPOSE To extend image reconstruction using image-space sampling function (IRIS) to address large-scale motion in multishot diffusion-weighted imaging (DWI). METHODS A clustered IRIS (CIRIS) algorithm that would extend IRIS was proposed to correct for large-scale motion. For DWI, CIRIS initially groups the shots into clusters without intracluster large-scale motion and reconstructs each cluster by using IRIS. Then, CIRIS registers these cluster images and combines the registered images by using a weighted average to correct for voxel mismatch caused by intercluster large-scale motion. For diffusion tensor imaging (DTI), CIRIS further reduces the effect of motion on diffusion directions by treating motion-induced direction changes as additional diffusion directions. CIRIS also introduces the detection and rejection of motion-corrupted data to avoid corresponding image degradation. The proposed method was evaluated by simulation and in vivo diffusion datasets. RESULTS Experiments demonstrated that CIRIS can reduce motion-induced blurring and artifacts in DWI and provide more accurate DTI estimations in the presence of large-scale motion, compared with IRIS. CONCLUSION The proposed method presents a novel approach to correct for large-scale in-plane motion for multishot DWI and is expected to benefit the practical application of high-resolution diffusion imaging.
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Affiliation(s)
- Zhongbiao Xu
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China.,Key Laboratory of Mental Health of the Ministry of Education, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Feng Huang
- Neusoft Medical System, Shanghai, 200000, China
| | - Zhigang Wu
- Neusoft Medical System, Shanghai, 200000, China
| | - Yingjie Mei
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China.,Key Laboratory of Mental Health of the Ministry of Education, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.,Philips Healthcare, Guangzhou, 510515, China
| | | | | | - Zhifeng Chen
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China.,Key Laboratory of Mental Health of the Ministry of Education, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Yishi Wang
- Department of Biomedical Engineering, Tsinghua University, Beijing, 100000, China
| | - Zijing Dong
- Department of Biomedical Engineering, Tsinghua University, Beijing, 100000, China
| | - Hua Guo
- Department of Biomedical Engineering, Tsinghua University, Beijing, 100000, China
| | - Xinyuan Zhang
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China.,Key Laboratory of Mental Health of the Ministry of Education, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Wufan Chen
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China.,Key Laboratory of Mental Health of the Ministry of Education, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Qianjin Feng
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China.,Key Laboratory of Mental Health of the Ministry of Education, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Yanqiu Feng
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China.,Key Laboratory of Mental Health of the Ministry of Education, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
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