1
|
Zhou X, Daniel BL, Hargreaves BA, Lee PK. Distortion-free water-fat separated diffusion-weighted imaging using spatiotemporal joint reconstruction. Magn Reson Med 2024. [PMID: 39051729 DOI: 10.1002/mrm.30221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 06/03/2024] [Accepted: 06/25/2024] [Indexed: 07/27/2024]
Abstract
PURPOSE Diffusion-weighted imaging (DWI) suffers from geometric distortion and chemical shift artifacts due to the commonly used Echo Planar Imaging (EPI) trajectory. Even with fat suppression in DWI, severe B0 and B1 variations can result in residual fat, which becomes both a source of image artifacts and a confounding factor in diffusion-weighted contrast in distinguishing benign and malignant tissues. This work presents a method for acquiring distortion-free diffusion-weighted images using spatiotemporal acquisition and joint reconstruction. Water-fat separation is performed by chemical-shift encoding. METHODS Spatiotemporal acquisition is employed to obtain distortion-free images at a series of echo times. Chemical-shift encoding is used for water-fat separation. Reconstruction and separation are performed jointly in the spat-spectral domain. To address the shot-to-shot motion-induced phase in DWI, an Fast Spin Echo (FSE)-based phase navigator is incorporated into the sequence to obtain distortion-free phase information. The proposed method was validated in phantoms and in vivo for the brain, head and neck, and breast. RESULTS The proposed method enables the acquisition of distortion-free diffusion-weighted images in the presence of B0 field inhomogenieties commonly observed in the body. Water and fat components are separated with no obvious spectral leakage artifacts. The estimated Apparent Diffusion Coefficient (ADC) is comparable to that of multishot DW-EPI. CONCLUSION Distortion-free, water-fat separated diffusion-weighted images in body can be obtained through the utilization of spatiotemporal acquisition and joint reconstruction methods.
Collapse
Affiliation(s)
- Xuetong Zhou
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Bruce L Daniel
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Brian A Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Philip K Lee
- Department of Radiology, Stanford University, Stanford, California, USA
| |
Collapse
|
2
|
Lee CY, Thedens DR, Lullmann O, Steinbach EJ, Tamplin MR, Petronek MS, Grumbach IM, Allen BG, Harshman LA, Magnotta VA. An Improved Postprocessing Method to Mitigate the Macroscopic Cross-Slice B0 Field Effect on R2* Measurements in the Mouse Brain at 7T. Tomography 2024; 10:1074-1088. [PMID: 39058053 PMCID: PMC11280969 DOI: 10.3390/tomography10070081] [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: 05/01/2024] [Revised: 06/27/2024] [Accepted: 07/05/2024] [Indexed: 07/28/2024] Open
Abstract
The MR transverse relaxation rate, R2*, has been widely used to detect iron and myelin content in tissue. However, it is also sensitive to macroscopic B0 inhomogeneities. One approach to correct for the B0 effect is to fit gradient-echo signals with the three-parameter model, a sinc function-weighted monoexponential decay. However, such three-parameter models are subject to increased noise sensitivity. To address this issue, this study presents a two-stage fitting procedure based on the three-parameter model to mitigate the B0 effect and reduce the noise sensitivity of R2* measurement in the mouse brain at 7T. MRI scans were performed on eight healthy mice. The gradient-echo signals were fitted with the two-stage fitting procedure to generate R2corr_t*. The signals were also fitted with the monoexponential and three-parameter models to generate R2nocorr* and R2corr*, respectively. Regions of interest (ROIs), including the corpus callosum, internal capsule, somatosensory cortex, caudo-putamen, thalamus, and lateral ventricle, were selected to evaluate the within-ROI mean and standard deviation (SD) of the R2* measurements. The results showed that the Akaike information criterion of the monoexponential model was significantly reduced by using the three-parameter model in the selected ROIs (p = 0.0039-0.0078). However, the within-ROI SD of R2corr* using the three-parameter model was significantly higher than that of the R2nocorr* in the internal capsule, caudo-putamen, and thalamus regions (p = 0.0039), a consequence partially due to the increased noise sensitivity of the three-parameter model. With the two-stage fitting procedure, the within-ROI SD of R2corr* was significantly reduced by 7.7-30.2% in all ROIs, except for the somatosensory cortex region with a fast in-plane variation of the B0 gradient field (p = 0.0039-0.0078). These results support the utilization of the two-stage fitting procedure to mitigate the B0 effect and reduce noise sensitivity for R2* measurement in the mouse brain.
Collapse
Affiliation(s)
- Chu-Yu Lee
- Department of Radiology, University of Iowa, Iowa City, IA 52242, USA; (C.-Y.L.); (D.R.T.)
| | - Daniel R. Thedens
- Department of Radiology, University of Iowa, Iowa City, IA 52242, USA; (C.-Y.L.); (D.R.T.)
| | - Olivia Lullmann
- Medical Scientist Training Program, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA;
- Stead Family Department of Pediatrics, Division of Pediatric Nephrology, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA; (E.J.S.); (L.A.H.)
| | - Emily J. Steinbach
- Stead Family Department of Pediatrics, Division of Pediatric Nephrology, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA; (E.J.S.); (L.A.H.)
| | - Michelle R. Tamplin
- Division of Cardiovascular Medicine, Abboud Cardiovascular Research Center, Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA; (M.R.T.); (I.M.G.)
- Department of Radiation Oncology, Free Radical and Radiation Biology, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA; (M.S.P.); (B.G.A.)
- Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City, IA 52246, USA
| | - Michael S. Petronek
- Department of Radiation Oncology, Free Radical and Radiation Biology, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA; (M.S.P.); (B.G.A.)
| | - Isabella M. Grumbach
- Division of Cardiovascular Medicine, Abboud Cardiovascular Research Center, Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA; (M.R.T.); (I.M.G.)
- Department of Radiation Oncology, Free Radical and Radiation Biology, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA; (M.S.P.); (B.G.A.)
- Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City, IA 52246, USA
| | - Bryan G. Allen
- Department of Radiation Oncology, Free Radical and Radiation Biology, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA; (M.S.P.); (B.G.A.)
| | - Lyndsay A. Harshman
- Stead Family Department of Pediatrics, Division of Pediatric Nephrology, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA; (E.J.S.); (L.A.H.)
| | - Vincent A. Magnotta
- Department of Radiology, University of Iowa, Iowa City, IA 52242, USA; (C.-Y.L.); (D.R.T.)
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA
| |
Collapse
|
3
|
Dong Y, Koolstra K, Li Z, Riedel M, van Osch MJP, Börnert P. Structured low-rank reconstruction for navigator-free water/fat separated multi-shot diffusion-weighted EPI. Magn Reson Med 2024; 91:205-220. [PMID: 37753595 DOI: 10.1002/mrm.29848] [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: 02/22/2023] [Revised: 07/20/2023] [Accepted: 08/11/2023] [Indexed: 09/28/2023]
Abstract
PURPOSE Multi-shot diffusion-weighted EPI allows an increase in image resolution and reduced geometric distortions and can be combined with chemical-shift encoding (Dixon) to separate water/fat signals. However, such approaches suffer from physiological motion-induced shot-to-shot phase variations. In this work, a structured low-rank-based navigator-free algorithm is proposed to address the challenge of simultaneously separating water/fat signals and correcting for physiological motion-induced shot-to-shot phase variations in multi-shot EPI-based diffusion-weighted MRI. THEORY AND METHODS We propose an iterative, model-based reconstruction pipeline that applies structured low-rank regularization to estimate and eliminate the shot-to-shot phase variations in a data-driven way, while separating water/fat images. The algorithm is tested in different anatomies, including head-neck, knee, brain, and prostate. The performance is validated in simulations and in-vivo experiments in comparison to existing approaches. RESULTS In-vivo experiments and simulations demonstrated the effectiveness of the proposed algorithm compared to extra-navigated and an alternative self-navigation approach. The proposed algorithm demonstrates the capability to reconstruct in the multi-shot/Dixon hybrid space domain under-sampled datasets, using the same number of acquired EPI shots compared to conventional fat-suppression techniques but eliminating fat signals through chemical-shift encoding. In addition, partial Fourier reconstruction can also be achieved by using the concept of virtual conjugate coils in conjunction with the proposed algorithm. CONCLUSION The proposed algorithm effectively eliminates the shot-to-shot phase variations and separates water/fat images, making it a promising solution for future DWI on different anatomies.
Collapse
Affiliation(s)
- Yiming Dong
- C.J. Gorter MRI Center, Department of Radiology, LUMC, Leiden, The Netherlands
| | | | - Ziyu Li
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | | | - Peter Börnert
- C.J. Gorter MRI Center, Department of Radiology, LUMC, Leiden, The Netherlands
- Philips Research Hamburg, Hamburg, Germany
| |
Collapse
|
4
|
Yoon D, Lutz AM. Diffusion Tensor Imaging of Peripheral Nerves: Current Status and New Developments. Semin Musculoskelet Radiol 2023; 27:641-648. [PMID: 37935210 DOI: 10.1055/s-0043-1775742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
Diffusion tensor imaging (DTI) is an emerging technique for peripheral nerve imaging that can provide information about the microstructural organization and connectivity of these nerves and complement the information gained from anatomical magnetic resonance imaging (MRI) sequences. With DTI it is possible to reconstruct nerve pathways and visualize the three-dimensional trajectory of nerve fibers, as in nerve tractography. More importantly, DTI allows for quantitative evaluation of peripheral nerves by the calculation of several important parameters that offer insight into the functional status of a nerve. Thus DTI has a high potential to add value to the work-up of peripheral nerve pathologies, although it is more technically demanding. Peripheral nerves pose specific challenges to DTI due to their small diameter and DTI's spatial resolution, contrast, location, and inherent field inhomogeneities when imaging certain anatomical locations. Numerous efforts are underway to resolve these technical challenges and thus enable wider acceptance of DTI in peripheral nerve MRI.
Collapse
Affiliation(s)
- Daehyun Yoon
- Department of Radiology and Biomedical Imaging, School of Medicine, University of California at San Francisco, San Francisco, California
| | - Amelie M Lutz
- Department of Radiology, Kantonal Hospital Thurgau, Muensterlingen, Switzerland
| |
Collapse
|
5
|
Dong Y, Riedel M, Koolstra K, van Osch MJP, Börnert P. Water/fat separation for self-navigated diffusion-weighted multishot echo-planar imaging. NMR IN BIOMEDICINE 2023; 36:e4822. [PMID: 36031585 PMCID: PMC10078174 DOI: 10.1002/nbm.4822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/25/2022] [Accepted: 08/21/2022] [Indexed: 06/15/2023]
Abstract
The purpose of this study was to develop a self-navigation strategy to improve scan efficiency and image quality of water/fat-separated, diffusion-weighted multishot echo-planar imaging (ms-EPI). This is accomplished by acquiring chemical shift-encoded diffusion-weighted data and using an appropriate water-fat and diffusion-encoded signal model to enable reconstruction directly from k-space data. Multishot EPI provides reduced geometric distortion and improved signal-to-noise ratio in diffusion-weighted imaging compared with single-shot approaches. Multishot acquisitions require corrections for physiological motion-induced shot-to-shot phase errors using either extra navigators or self-navigation principles. In addition, proper fat suppression is important, especially in regions with large B0 inhomogeneity. This makes the use of chemical shift encoding attractive. However, when combined with ms-EPI, shot-to-shot phase navigation can be challenging because of the spatial displacement of fat signals along the phase-encoding direction. In this work, a new model-based, self-navigated water/fat separation reconstruction algorithm is proposed. Experiments in legs and in the head-neck region of 10 subjects were performed to validate the algorithm. The results are compared with an image-based, two-dimensional (2D) navigated water/fat separation approach for ms-EPI and with a conventional fat saturation approach. Compared with the 2D navigated method, the use of self-navigation reduced the shot duration time by 30%-35%. The proposed algorithm provided improved diffusion-weighted water images in both leg and head-neck regions compared with the 2D navigator-based approach. The proposed algorithm also produced better fat suppression compared with the conventional fat saturation technique in the B0 inhomogeneous regions. In conclusion, the proposed self-navigated reconstruction algorithm can produce superior water-only diffusion-weighted EPI images with less artefacts compared with the existing methods.
Collapse
Affiliation(s)
- Yiming Dong
- C. J. Gorter Center for High Field MRI, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Malte Riedel
- Institute for Biomedical EngineeringETH Zurich and University of ZurichZurichSwitzerland
| | - Kirsten Koolstra
- Division of Image Processing, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Matthias J. P. van Osch
- C. J. Gorter Center for High Field MRI, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Peter Börnert
- C. J. Gorter Center for High Field MRI, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
- Philips Research HamburgHamburgGermany
| |
Collapse
|
6
|
Chen X, Wang W, Huang J, Wu J, Chen L, Cai C, Cai S, Chen Z. Ultrafast water–fat separation using deep learning–based single‐shot MRI. Magn Reson Med 2022; 87:2811-2825. [DOI: 10.1002/mrm.29172] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 12/13/2021] [Accepted: 01/07/2022] [Indexed: 12/16/2022]
Affiliation(s)
- Xinran Chen
- Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen Fujian People’s Republic of China
| | - Wei Wang
- Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen Fujian People’s Republic of China
| | - Jianpan Huang
- Department of Biomedical Engineering City University of Hong Kong Hong Kong People’s Republic of China
| | - Jian Wu
- Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen Fujian People’s Republic of China
| | - Lin Chen
- Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen Fujian People’s Republic of China
| | - Congbo Cai
- Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen Fujian People’s Republic of China
| | - Shuhui Cai
- Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen Fujian People’s Republic of China
| | - Zhong Chen
- Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen Fujian People’s Republic of China
| |
Collapse
|