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Michael ES, Hennel F, Pruessmann KP. Motion-compensated diffusion encoding in multi-shot human brain acquisitions: Insights using high-performance gradients. Magn Reson Med 2024; 92:556-572. [PMID: 38441339 DOI: 10.1002/mrm.30069] [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: 09/13/2023] [Revised: 12/12/2023] [Accepted: 02/09/2024] [Indexed: 06/02/2024]
Abstract
PURPOSE To evaluate the utility of up to second-order motion-compensated diffusion encoding in multi-shot human brain acquisitions. METHODS Experiments were performed with high-performance gradients using three forms of diffusion encoding motion-compensated through different orders: conventional zeroth-order-compensated pulsed gradients (PG), first-order-compensated gradients (MC1), and second-order-compensated gradients (MC2). Single-shot acquisitions were conducted to correlate the order of motion compensation with resultant phase variability. Then, multi-shot acquisitions were performed at varying interleaving factors. Multi-shot images were reconstructed using three levels of shot-to-shot phase correction: no correction, channel-wise phase correction based on FID navigation, and correction based on explicit phase mapping (MUSE). RESULTS In single-shot acquisitions, MC2 diffusion encoding most effectively suppressed phase variability and sensitivity to brain pulsation, yielding residual variations of about 10° and of low spatial order. Consequently, multi-shot MC2 images were largely satisfactory without phase correction and consistently improved with the navigator correction, which yielded repeatable high-quality images; contrarily, PG and MC1 images were inadequately corrected using the navigator approach. With respect to MUSE reconstructions, the MC2 navigator-corrected images were in close agreement for a standard interleaving factor and considerably more reliable for higher interleaving factors, for which MUSE images were corrupted. Finally, owing to the advanced gradient hardware, the relative SNR penalty of motion-compensated diffusion sensitization was substantially more tolerable than that faced previously. CONCLUSION Second-order motion-compensated diffusion encoding mitigates and simplifies shot-to-shot phase variability in the human brain, rendering the multi-shot acquisition strategy an effective means to circumvent limitations of retrospective phase correction methods.
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Affiliation(s)
- Eric Seth Michael
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Franciszek Hennel
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Klaas Paul Pruessmann
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
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Chen X, Wu W, Chiew M. Motion compensated structured low-rank reconstruction for 3D multi-shot EPI. Magn Reson Med 2024; 91:2443-2458. [PMID: 38361309 DOI: 10.1002/mrm.30019] [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: 09/15/2023] [Revised: 12/08/2023] [Accepted: 01/05/2024] [Indexed: 02/17/2024]
Abstract
PURPOSE The 3D multi-shot EPI imaging offers several benefits including higher SNR and high isotropic resolution compared to 2D single shot EPI. However, it suffers from shot-to-shot inconsistencies arising from physiologically induced phase variations and bulk motion. This work proposed a motion compensated structured low-rank (mcSLR) reconstruction method to address both issues for 3D multi-shot EPI. METHODS Structured low-rank reconstruction has been successfully used in previous work to deal with inter-shot phase variations for 3D multi-shot EPI imaging. It circumvents the estimation of phase variations by reconstructing an individual image for each phase state which are then sum-of-squares combined, exploiting their linear interdependency encoded in structured low-rank constraints. However, structured low-rank constraints become less effective in the presence of inter-shot motion, which corrupts image magnitude consistency and invalidates the linear relationship between shots. Thus, this work jointly models inter-shot phase variations and motion corruptions by incorporating rigid motion compensation for structured low-rank reconstruction, where motion estimates are obtained in a fully data-driven way without relying on external hardware or imaging navigators. RESULTS Simulation and in vivo experiments at 7T have demonstrated that the mcSLR method can effectively reduce image artifacts and improve the robustness of 3D multi-shot EPI, outperforming existing methods which only address inter-shot phase variations or motion, but not both. CONCLUSION The proposed mcSLR reconstruction compensates for rigid motion, and thus improves the validity of structured low-rank constraints, resulting in improved robustness of 3D multi-shot EPI to both inter-shot motion and phase variations.
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Affiliation(s)
- Xi Chen
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Wenchuan Wu
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Mark Chiew
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
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McTavish S, Van AT, Peeters JM, Weiss K, Harder FN, Makowski MR, Braren RF, Karampinos DC. Partial Fourier in the presence of respiratory motion in prostate diffusion-weighted echo planar imaging. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01162-x. [PMID: 38743376 DOI: 10.1007/s10334-024-01162-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 03/05/2024] [Accepted: 04/24/2024] [Indexed: 05/16/2024]
Abstract
PURPOSE To investigate the effect of respiratory motion in terms of signal loss in prostate diffusion-weighted imaging (DWI), and to evaluate the usage of partial Fourier in a free-breathing protocol in a clinically relevant b-value range using both single-shot and multi-shot acquisitions. METHODS A controlled breathing DWI acquisition was first employed at 3 T to measure signal loss from deep breathing patterns. Single-shot and multi-shot (2-shot) acquisitions without partial Fourier (no pF) and with partial Fourier (pF) factors of 0.75 and 0.65 were employed in a free-breathing protocol. The apparent SNR and ADC values were evaluated in 10 healthy subjects to measure if low pF factors caused low apparent SNR or overestimated ADC. RESULTS Controlled breathing experiments showed a difference in signal coefficient of variation between shallow and deep breathing. In free-breathing single-shot acquisitions, the pF 0.65 scan showed a significantly (p < 0.05) higher apparent SNR than pF 0.75 and no pF in the peripheral zone (PZ) of the prostate. In the multi-shot acquisitions in the PZ, pF 0.75 had a significantly higher apparent SNR than 0.65 pF and no pF. The single-shot pF 0.65 scan had a significantly lower ADC than single-shot no pF. CONCLUSION Deep breathing patterns can cause intravoxel dephasing in prostate DWI. For single-shot acquisitions at a b-value of 800 s/mm2, any potential risks of motion-related artefacts at low pF factors (pF 0.65) were outweighed by the increase in signal from a lower TE, as shown by the increase in apparent SNR. In multi-shot acquisitions however, the minimum pF factor should be larger, as shown by the lower apparent SNR at low pF factors.
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Affiliation(s)
- Sean McTavish
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.
| | - Anh T Van
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | | | | | - Felix N Harder
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Marcus R Makowski
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Rickmer F Braren
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Dimitrios C Karampinos
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
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Shao H, Liu Q, Saeed A, Liu C, Liu WV, Zhang Q, Huang S, Zhang G, Li L, Zhang J, Zhu W, Tang X. Feasibility of diffusion tensor imaging in cervical spondylotic myelopathy using MUSE sequence. Spine J 2024:S1529-9430(24)00156-6. [PMID: 38556218 DOI: 10.1016/j.spinee.2024.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 03/03/2024] [Accepted: 03/24/2024] [Indexed: 04/02/2024]
Abstract
BACKGROUND CONTEXT The most frequent type of spinal cord injury is cervical spondylotic myelopathy (CSM). Conventional structural magnetic resonance imaging (MRI) is the gold diagnosis standard for CSM. Diffusion tensor imaging (DTI) could reflect microstructural changes in the spinal cord by tracing water molecular diffusion in early stages of CSM. However, due to the complex local anatomical structure and small field of view of the spinal cord, the imaging effect of traditional DTI imaging on the spinal cord is limited. MUSE (MUltiplexed Sensitivity-Encoding) -DTI is a novel diffusion-weighted imaging (DWI) sequence that achieves higher signal intensity through multiple excitation acquisition. MUSE sequence may improve the quality of spinal cord DTI imaging. STUDY DESIGN Prospective study. PURPOSE This study aimed to investigate the clinical diagnosis value of a novel protocol of MUSE-DTI in patients with cervical spondylotic myelopathy (CSM). PATIENT SAMPLE From August 2021 to March 2022, a total of 60 subjects (22-71 years) were enrolled, including 51 CSM patients (22 males, 29 females) and 9 healthy subjects (4 males and 5 females). Each subject underwent a MUSE-DTI examination and a clinical Japanese Orthopedic Association (JOA) scale. OUTCOME MEASURES We measured values of FA (Fractional Anisotropy), MD (Mean Diffusivity), AD (Axial Diffusivity), and RD (Radial Diffusivity), and collected the clinical JOA scores of each subject before the MR examination. METHODS A 3.0T MR scanner (Signa Architect, GE Healthcare) performed the MUSE-DTI sequence on each subject. The cervical canal stenosis of subjects was classified from grade 0 to grade Ⅲ according to the method of an MRI grading system. FA, MD, AD, and RD maps were generated by postprocessing MUSE-DTI data on the GE workstation. Regions of interest (ROIs) were manually drawn at the C2 vertebral body level and C2/3-C6/7 intervertebral disc levels by covering the whole spinal cord. The clinical severity of myelopathy of subjects was assessed by the clinical Japanese Orthopedic Association scale (JOA). RESULTS MUSE-DTI can acquire a high-resolution diffusion image compared to traditional DTI. The FAMCL values showed a decreasing trend from grade 0 to grade Ⅲ, while the MDMCL, ADMCL, and RDMCL values showed an overall increasing trend. Significant differences in MDMCL, ADMCL, and RDMCL values were found between adjacent groups among grades Ⅰ-Ⅲ (p<.05). The ADC2 values in CSM patients (grade I-Ⅲ) were significantly lower than in healthy individuals (grade 0) (p=.019). The clinical JOA score has a significant correlation with FAMCL (p=.035), MDMCL (p<.001), ADMCL (p<.001), and RDMCL (p<.001) values. CONCLUSIONS MUSE-DTI displayed a better image quality compared to traditional DTI. MUSE-DTI parameters displayed a grade-dependent trend. All the MUSE-DTI parameters at MCL were correlated with the clinical JOA scores. The ADC2 values can reflect the secondary damage of distal spinal cord. Therefore, MUSE-DTI could be a reliable biomarker for clinical auxiliary diagnosis of spinal cord injury severity in cervical spondylotic myelopathy.
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Affiliation(s)
- Haoyue Shao
- Department of Radiology, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qiufeng Liu
- Department of Radiology, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Azzam Saeed
- Department of Radiology, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chaoxu Liu
- Department of Orthopedics, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | | | - Qiya Zhang
- Department of Radiology, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shuting Huang
- Department of Radiology, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Guiling Zhang
- Department of Radiology, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Li Li
- Department of Radiology, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiaxuan Zhang
- Department of Radiology, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiangyu Tang
- Department of Radiology, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Chen S, Chu ML, Liang L, Liu YJ, Chen NK, Wang H, Juan CJ, Chang HC. Highly accelerated multi-shot intravoxel incoherent motion diffusion-weighted imaging in brain enabled by parametric POCS-based multiplexed sensitivity encoding. NMR IN BIOMEDICINE 2024; 37:e5063. [PMID: 37871617 DOI: 10.1002/nbm.5063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 10/25/2023]
Abstract
Recently, intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) has also been demonstrated as an imaging tool for applications in neurological and neurovascular diseases. However, the use of single-shot diffusion-weighted echo-planar imaging for IVIM DWI acquisition leads to suboptimal data quality: for instance, geometric distortion and deteriorated image quality at high spatial resolution. Although the recently commercialized multi-shot acquisition methods, such as multiplexed sensitivity encoding (MUSE), can attain high-resolution and high-quality DWI with signal-to-noise ratio (SNR) performance superior to that of the conventional parallel imaging method, the prolonged scan time associated with multi-shot acquisition is impractical for routine IVIM DWI. This study proposes an acquisition and reconstruction framework based on parametric-POCSMUSE to accelerate the four-shot IVIM DWI with 70% reduction of total scan time (13 min 8 s versus 4 min 8 s). First, the four-shot IVIM DWI scan with 17 b values was accelerated by acquiring only one segment per b value except for b values of 0 and 600 s/mm2 . Second, an IVIM-estimation scheme was integrated into the parametric-POCSMUSE to enable joint reconstruction of multi-b images from under-sampled four-shot IVIM DWI data. In vivo experiments on both healthy subjects and patients show that the proposed framework successfully produced multi-b DW images with significantly higher SNRs and lower reconstruction errors than did the conventional acceleration method based on parallel imaging. In addition, the IVIM quantitative maps estimated from the data produced by the proposed framework showed quality comparable to that of fully sampled MUSE-reconstructed images, suggesting that the proposed framework can enable highly accelerated multi-shot IVIM DWI without sacrificing data quality. In summary, the proposed framework can make multi-shot IVIM DWI feasible in a routine MRI examination, with reasonable scan time and improved geometric fidelity.
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Affiliation(s)
- Shihui Chen
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Mei-Lan Chu
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Liyuan Liang
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Yi-Jui Liu
- Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan
| | - Nan-Kuei Chen
- Department of Biomedical Engineering, University of Arizona, Tucson, Arizona, USA
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, North Carolina, USA
| | - He 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, Shanghai, China
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Chun-Jung Juan
- Department of Medical Imaging, China Medical University Hsinchu Hospital, Hsinchu, Taiwan
- Department of Radiology, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan
- Department of Medical Imaging, China Medical University Hospital, Taichung, Taiwan
| | - Hing-Chiu Chang
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
- Multi-Scale Medical Robotics Center, Shatin, Hong Kong
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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.
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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
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Sun K, Chen Z, Dan G, Luo Q, Yan L, Liu F, Zhou XJ. Three-dimensional echo-shifted EPI with simultaneous blip-up and blip-down acquisitions for correcting geometric distortion. Magn Reson Med 2023; 90:2375-2387. [PMID: 37667533 PMCID: PMC10903279 DOI: 10.1002/mrm.29828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 07/08/2023] [Accepted: 07/25/2023] [Indexed: 09/06/2023]
Abstract
PURPOSE EPI with blip-up/down acquisition (BUDA) can provide high-quality images with minimal distortions by using two readout trains with opposing phase-encoding gradients. Because of the need for two separate acquisitions, BUDA doubles the scan time and degrades the temporal resolution when compared to single-shot EPI, presenting a major challenge for many applications, particularly fMRI. This study aims at overcoming this challenge by developing an echo-shifted EPI BUDA (esEPI-BUDA) technique to acquire both blip-up and blip-down datasets in a single shot. METHODS A 3D esEPI-BUDA pulse sequence was designed by using an echo-shifting strategy to produce two EPI readout trains. These readout trains produced a pair of k-space datasets whose k-space trajectories were interleaved with opposite phase-encoding gradient directions. The two k-space datasets were separately reconstructed using a 3D SENSE algorithm, from which time-resolved B0 -field maps were derived using TOPUP in FSL and then input into a forward model of joint parallel imaging reconstruction to correct for geometric distortion. In addition, Hankel structured low-rank constraint was incorporated into the reconstruction framework to improve image quality by mitigating the phase errors between the two interleaved k-space datasets. RESULTS The 3D esEPI-BUDA technique was demonstrated in a phantom and an fMRI study on healthy human subjects. Geometric distortions were effectively corrected in both phantom and human brain images. In the fMRI study, the visual activation volumes and their BOLD responses were comparable to those from conventional 3D echo-planar images. CONCLUSION The improved imaging efficiency and dynamic distortion correction capability afforded by 3D esEPI-BUDA are expected to benefit many EPI applications.
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Affiliation(s)
- Kaibao Sun
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States
| | - Zhifeng Chen
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC, Australia
| | - Guangyu Dan
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States
| | - Qingfei Luo
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States
| | - Lirong Yan
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Feng Liu
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Xiaohong Joe Zhou
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States
- Departments of Radiology and Neurosurgery, University of Illinois at Chicago, Chicago, IL, United States
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Liao C, Yarach U, Cao X, Iyer SS, Wang N, Kim TH, Tian Q, Bilgic B, Kerr AB, Setsompop K. High-fidelity mesoscale in-vivo diffusion MRI through gSlider-BUDA and circular EPI with S-LORAKS reconstruction. Neuroimage 2023; 275:120168. [PMID: 37187364 PMCID: PMC10451786 DOI: 10.1016/j.neuroimage.2023.120168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/27/2023] [Accepted: 05/12/2023] [Indexed: 05/17/2023] Open
Abstract
PURPOSE To develop a high-fidelity diffusion MRI acquisition and reconstruction framework with reduced echo-train-length for less T2* image blurring compared to typical highly accelerated echo-planar imaging (EPI) acquisitions at sub-millimeter isotropic resolution. METHODS We first proposed a circular-EPI trajectory with partial Fourier sampling on both the readout and phase-encoding directions to minimize the echo-train-length and echo time. We then utilized this trajectory in an interleaved two-shot EPI acquisition with reversed phase-encoding polarity, to aid in the correction of off-resonance-induced image distortions and provide complementary k-space coverage in the missing partial Fourier regions. Using model-based reconstruction with structured low-rank constraint and smooth phase prior, we corrected the shot-to-shot phase variations across the two shots and recover the missing k-space data. Finally, we combined the proposed acquisition/reconstruction framework with an SNR-efficient RF-encoded simultaneous multi-slab technique, termed gSlider, to achieve high-fidelity 720 µm and 500 µm isotropic resolution in-vivo diffusion MRI. RESULTS Both simulation and in-vivo results demonstrate the effectiveness of the proposed acquisition and reconstruction framework to provide distortion-corrected diffusion imaging at the mesoscale with markedly reduced T2*-blurring. The in-vivo results of 720 µm and 500 µm datasets show high-fidelity diffusion images with reduced image blurring and echo time using the proposed approaches. CONCLUSIONS The proposed method provides high-quality distortion-corrected diffusion-weighted images with ∼40% reduction in the echo-train-length and T2* blurring at 500µm-isotropic-resolution compared to standard multi-shot EPI.
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Affiliation(s)
- Congyu Liao
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Uten Yarach
- Radiologic Technology Department, Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand
| | - Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
| | - Siddharth Srinivasan Iyer
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nan Wang
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Tae Hyung Kim
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Department of Computer Engineering, Hongik University, Seoul, South Korea
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Adam B Kerr
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Stanford Center for Cognitive and Neurobiological Imaging, Stanford University, Stanford, CA, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
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Chen Z, Liao C, Cao X, Poser BA, Xu Z, Lo WC, Wen M, Cho J, Tian Q, Wang Y, Feng Y, Xia L, Chen W, Liu F, Bilgic B. 3D-EPI blip-up/down acquisition (BUDA) with CAIPI and joint Hankel structured low-rank reconstruction for rapid distortion-free high-resolution T 2 * mapping. Magn Reson Med 2023; 89:1961-1974. [PMID: 36705076 PMCID: PMC10072851 DOI: 10.1002/mrm.29578] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 12/19/2022] [Accepted: 12/22/2022] [Indexed: 01/28/2023]
Abstract
PURPOSE This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitativeT 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping. METHODS 3D Blip-up and -down acquisition (3D-BUDA) sequence is designed for both single- and multi-echo 3D gradient recalled echo (GRE)-EPI imaging using multiple shots with blip-up and -down readouts to encode B0 field map information. Complementary k-space coverage is achieved using controlled aliasing in parallel imaging (CAIPI) sampling across the shots. For image reconstruction, an iterative hard-thresholding algorithm is employed to minimize the cost function that combines field map information informed parallel imaging with the structured low-rank constraint for multi-shot 3D-BUDA data. Extending 3D-BUDA to multi-echo imaging permitsT 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping. For this, we propose constructing a joint Hankel matrix along both echo and shot dimensions to improve the reconstruction. RESULTS Experimental results on in vivo multi-echo data demonstrate that, by performing joint reconstruction along with both echo and shot dimensions, reconstruction accuracy is improved compared to standard 3D-BUDA reconstruction. CAIPI sampling is further shown to enhance image quality. ForT 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping, parameter values from 3D-Joint-CAIPI-BUDA and reference multi-echo GRE are within limits of agreement as quantified by Bland-Altman analysis. CONCLUSIONS The proposed technique enables rapid 3D distortion-free high-resolution imaging andT 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping. Specifically, 3D-BUDA enables 1-mm isotropic whole-brain imaging in 22 s at 3T and 9 s on a 7T scanner. The combination of multi-echo 3D-BUDA with CAIPI acquisition and joint reconstruction enables distortion-free whole-brainT 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping in 47 s at 1.1 × 1.1 × 1.0 mm3 resolution.
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Affiliation(s)
- Zhifeng Chen
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Charlestown, MA, USA
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC, Australia
| | - Congyu Liao
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Benedikt A. Poser
- Maastricht Brain Imaging Center, Faculty of Psychology and Neuroscience, University of Maastricht, the Netherlands
| | - Zhongbiao Xu
- Department of Radiotherapy, Cancer Center, Guangdong Provincial People’s Hospital & Guangdong Academy of Medical Science, Guangzhou, China
| | | | - Manyi Wen
- Department of Chemical Pathology, The Chinese University of Hong Kong, Hong Kong, China
| | - Jaejin Cho
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Yaohui Wang
- Division of Superconducting Magnet Science and Technology, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China
| | - Yanqiu Feng
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
| | - Ling Xia
- Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Wufan Chen
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Feng Liu
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Charlestown, MA, USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
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10
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Li G, Ma X, Li S, Ye X, Börnert P, Zhou XJ, Guo H. Comparison of uniform-density, variable-density, and dual-density spiral samplings for multi-shot DWI. Magn Reson Med 2023; 90:133-149. [PMID: 36883748 DOI: 10.1002/mrm.29633] [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: 09/03/2022] [Revised: 02/17/2023] [Accepted: 02/21/2023] [Indexed: 03/09/2023]
Abstract
PURPOSE To compare the performances of uniform-density spiral (UDS), variable-density spiral (VDS), and dual-density spiral (DDS) samplings in multi-shot diffusion imaging, and determine a sampling strategy that balances reliability of shot navigator and overall DWI image quality. THEORY AND METHODS UDS, VDS, and DDS trajectories were implemented to achieve four-shot diffusion-weighted spiral imaging. First, the static B0 off-resonance effects in UDS, VDS, and DDS acquisitions were analyzed based on a signal model. Then, in vivo experiments were performed to verify the theoretical analyses, and fractional anisotropy (FA) fitting residuals were used to quantitatively assess the quality of spiral diffusion data for tensor estimation. Finally, the SNR performances and g-factor behavior of the three spiral samplings were evaluated using a Monte Carlo-based pseudo multiple replica method. RESULTS Among the three spiral trajectories with the same readout duration, UDS sampling exhibited the least off-resonance artifacts. This was most evident when the static B0 off-resonance effect was severe. The UDS diffusion images had higher anatomical fidelity and lower FA fitting residuals than the other two counterparts. Furthermore, the four-shot UDS acquisition achieved the best SNR performance in diffusion imaging with 12.11% and 40.85% improvements over the VDS and DDS acquisitions with the same readout duration, respectively. CONCLUSION UDS sampling is an efficient spiral acquisition scheme for high-resolution diffusion imaging with reliable navigator information. It provides superior off-resonance performance and SNR efficiency over the VDS and DDS samplings for the tested scenarios.
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Affiliation(s)
- Guangqi Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Xiaodong Ma
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Sisi Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Xinyu Ye
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Peter Börnert
- Radiology, C.J. Gorter Center for High-Field MRI, Leiden University Medical Center, Leiden, The Netherlands.,Philips Research, Hamburg, Germany
| | - Xiaohong Joe Zhou
- Center for MR Research and Departments of Radiology, Neurosurgery, and Biomedical Engineering, University of Illinois College of Medicine at Chicago, Chicago, Illinois, USA
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
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11
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Hu Z, Zhang Z, Ma X, Jing J, Guo H. Technical note: Revised projections onto convex sets reconstruction of multi-shot diffusion-weighted imaging. Med Phys 2023; 50:980-992. [PMID: 36464912 DOI: 10.1002/mp.16146] [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: 02/23/2022] [Revised: 09/26/2022] [Accepted: 11/18/2022] [Indexed: 12/07/2022] Open
Abstract
BACKGROUND High-resolution diffusion-weighted imaging (DWI) is usually achieved through multi-shot acquisitions and parallel imaging-based reconstructions. Multiple POCS (projections onto convex sets) based algorithms have been proposed for DWI reconstructions. However, the slow convergence of POCS and the suboptimal quality of the reconstructed images limit their applications. PURPOSE In this study, a revised POCS algorithm for multi-shot DWI reconstruction is proposed based on FISTA (fast iterative shrinkage-thresholding algorithm) to achieve faster convergence and higher accuracy. METHODS In FISTA, the next iteration is computed based on two previous iterations, instead of only the previous one, to improve the convergence speed. This scheme is adopted into the relevant POCS-based algorithms, including POCSENSE (POCS-based sensitivity-encoding), POCSMUSE (POCS-based multiplexed sensitivity-encoding), iPOCSMUSE (iterative POCSMUSE), and POCS-ICE (POCS-enhanced inherent correction of motion-induced phase errors) to address the slow convergence problem. Simulations and in vivo experiments were performed to evaluate the performance of the proposed method. RESULTS Experimental results show that the proposed method enables faster convergence compared to the original POCS. For example, for a spiral DWI simulation using eight-shot interleaves and having SNR of 20 dB, the iteration number needed for the revised POCS-ICE decreases by about 70% to achieve approximately the same nRMSE level as POCS-ICE. Additionally, it improves image quality in terms of fewer artifacts compared with the original POCS. CONCLUSIONS The revised DWI reconstruction methods can achieve higher convergence rates than the original POCS-based algorithms and higher image quality with the same iteration numbers. As such, the proposed method can serve as a practical and efficient reconstruction method for multi-shot DWI.
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Affiliation(s)
- Zhangxuan Hu
- MR Research China, GE Healthcare, Beijing, China.,Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Zhe Zhang
- Tiantan Neuroimaging Center of Excellence, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Xiaodong Ma
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Jing Jing
- Tiantan Neuroimaging Center of Excellence, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
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12
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Dai E, Mani M, McNab JA. Multi-band multi-shot diffusion MRI reconstruction with joint usage of structured low-rank constraints and explicit phase mapping. Magn Reson Med 2023; 89:95-111. [PMID: 36063492 PMCID: PMC9887994 DOI: 10.1002/mrm.29422] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 08/01/2022] [Accepted: 08/02/2022] [Indexed: 02/02/2023]
Abstract
PURPOSE To develop a joint reconstruction method for multi-band multi-shot diffusion MRI. THEORY AND METHODS Multi-band multi-shot EPI acquisition is an effective approach for high-resolution diffusion MRI, but requires specific algorithms to correct the inter-shot phase variations. The phase correction can be done by first estimating the explicit phase map and then feeding it into the k-space signal formulation model. Alternatively, the phase information can be used indirectly as structured low-rank constraints in k-space. The 2 methods differ in reconstruction accuracy and efficiency. We aim to combine the 2 different approaches for improved image quality and reconstruction efficiency simultaneously, termed "joint usage of structured low-rank constraints and explicit phase mapping" (JULEP). The proposed JULEP reconstruction is tested on both single-band and multi-band, multi-shot diffusion data, with different resolutions and b values. The results of JULEP are compared with conventional methods with explicit phase mapping (i.e., multiplexed sensitivity-encoding [MUSE]) and structured low-rank constraints (i.e., MUSSELS), and another joint reconstruction method (i.e., network estimated artifacts for tempered reconstruction [NEATR]). RESULTS JULEP improves the quality of the navigator and subsequently facilitates the reconstruction of final diffusion images. Compared with all 3 other methods (MUSE, MUSSELS, and NEATR), JULEP mitigates residual structural bias and improves temporal SNRs in the final diffusion image, particularly at high multi-band factors. Compared with MUSSELS, JULEP also improves computational efficiency. CONCLUSION The proposed JULEP method significantly improves the image quality and reconstruction efficiency of multi-band multi-shot diffusion MRI, which can promote a broader application of high-resolution diffusion MRI.
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Affiliation(s)
- Erpeng Dai
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Merry Mani
- Department of Radiology, University of Iowa, Iowa City, IA, United States
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States
| | - Jennifer A McNab
- Department of Radiology, Stanford University, Stanford, CA, United States
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13
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Lee PK, Hargreaves BA. A joint linear reconstruction for multishot diffusion weighted non-Carr-Purcell-Meiboom-Gill fast spin echo with full signal. Magn Reson Med 2022; 88:2139-2156. [PMID: 35906924 PMCID: PMC9732866 DOI: 10.1002/mrm.29393] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 06/25/2022] [Accepted: 06/27/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE Diffusion weighted Fast Spin Echo (DW-FSE) is a promising approach for distortionless DW imaging that is robust to system imperfections such as eddy currents and off-resonance. Due to non-Carr-Purcell-Meiboom-Gill (CPMG) magnetization, most DW-FSE sequences discard a large fraction of the signal ( 2 - 2 × $$ \sqrt{2}-2\times $$ ), reducing signal-to-noise ratio (SNR) efficiency compared to DW-EPI. The full FSE signal can be preserved by quadratically incrementing the transmit phase of the refocusing pulses, but this method of resolving non-CPMG magnetization has only been applied to single-shot DW-FSE due to challenges associated with image reconstruction. We present a joint linear reconstruction for multishot quadratic phase increment data that addresses these challenges and corrects ghosting from both shot-to-shot phase and intrashot signal oscillations. Multishot imaging reduces T2 blur and joint reconstruction of shots improves g-factor performance. A thorough analysis on the condition number of the proposed linear system is described. METHODS A joint multishot reconstruction is derived from the non-CPMG signal model. Multishot quadratic phase increment DW-FSE was tested in a standardized diffusion phantom and compared to single-shot DW-FSE and DW-EPI in vivo in the brain, cervical spine, and prostate. The pseudo multiple replica technique was applied to generate g-factor and SNR maps. RESULTS The proposed joint shot reconstruction eliminates ghosting from shot-to-shot phase and intrashot oscillations. g-factor performance is improved compared to previously proposed reconstructions, permitting efficient multishot imaging. apparent diffusion coefficient estimates in phantom experiments and in vivo are comparable to those obtained with conventional methods. CONCLUSION Multi-shot non-CPMG DW-FSE data with full signal can be jointly reconstructed using a linear model.
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Affiliation(s)
- Philip K. Lee
- Radiology, Stanford University, Stanford, CA, 94305, USA,Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Brian A. Hargreaves
- Radiology, Stanford University, Stanford, CA, 94305, USA,Electrical Engineering, Stanford University, Stanford, CA, 94305, USA,Bioengineering, Stanford University, Stanford, CA, 94305, USA
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14
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Zhao Y, Yi Z, Xiao L, Lau V, Liu Y, Zhang Z, Guo H, Leong AT, Wu EX. Joint denoising of
diffusion‐weighted
images via structured
low‐rank
patch matrix approximation. Magn Reson Med 2022; 88:2461-2474. [DOI: 10.1002/mrm.29407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 06/02/2022] [Accepted: 07/18/2022] [Indexed: 11/06/2022]
Affiliation(s)
- 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
| | - 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
| | - 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
| | - Vick Lau
- 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
| | - 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
| | - Zhe Zhang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering School of Medicine, Tsinghua University Beijing People's Republic of China
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering School of Medicine, Tsinghua University Beijing People's Republic of China
| | - Alex T. 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
| | - 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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15
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Zhang Z, Cho J, Wang L, Liao C, Shin HG, Cao X, Lee J, Xu J, Zhang T, Ye H, Setsompop K, Liu H, Bilgic B. Blip up-down acquisition for spin- and gradient-echo imaging (BUDA-SAGE) with self-supervised denoising enables efficient T 2 , T 2 *, para- and dia-magnetic susceptibility mapping. Magn Reson Med 2022; 88:633-650. [PMID: 35436357 DOI: 10.1002/mrm.29219] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 02/14/2022] [Accepted: 02/15/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE To rapidly obtain high resolution T2 , T2 *, and quantitative susceptibility mapping (QSM) source separation maps with whole-brain coverage and high geometric fidelity. METHODS We propose Blip Up-Down Acquisition for Spin And Gradient Echo imaging (BUDA-SAGE), an efficient EPI sequence for quantitative mapping. The acquisition includes multiple T2 *-, T2 '-, and T2 -weighted contrasts. We alternate the phase-encoding polarities across the interleaved shots in this multi-shot navigator-free acquisition. A field map estimated from interim reconstructions was incorporated into the joint multi-shot EPI reconstruction with a structured low rank constraint to eliminate distortion. A self-supervised neural network (NN), MR-Self2Self (MR-S2S), was used to perform denoising to boost SNR. Using Slider encoding allowed us to reach 1 mm isotropic resolution by performing super-resolution reconstruction on volumes acquired with 2 mm slice thickness. Quantitative T2 (=1/R2 ) and T2 * (=1/R2 *) maps were obtained using Bloch dictionary matching on the reconstructed echoes. QSM was estimated using nonlinear dipole inversion on the gradient echoes. Starting from the estimated R2 /R2 * maps, R2 ' information was derived and used in source separation QSM reconstruction, which provided additional para- and dia-magnetic susceptibility maps. RESULTS In vivo results demonstrate the ability of BUDA-SAGE to provide whole-brain, distortion-free, high-resolution, multi-contrast images and quantitative T2 /T2 * maps, as well as yielding para- and dia-magnetic susceptibility maps. Estimated quantitative maps showed comparable values to conventional mapping methods in phantom and in vivo measurements. CONCLUSION BUDA-SAGE acquisition with self-supervised denoising and Slider encoding enables rapid, distortion-free, whole-brain T2 /T2 * mapping at 1 mm isotropic resolution under 90 s.
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Affiliation(s)
- Zijing Zhang
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Jaejin Cho
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Long Wang
- Subtle Medical Inc, Menlo Park, CA, USA
| | - Congyu Liao
- Radiological Sciences Laboratory, Stanford University, Stanford, CA, USA
| | - Hyeong-Geol Shin
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Xiaozhi Cao
- Radiological Sciences Laboratory, Stanford University, Stanford, CA, USA
| | - Jongho Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Jinmin Xu
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Tao Zhang
- Subtle Medical Inc, Menlo Park, CA, USA
| | - Huihui Ye
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Kawin Setsompop
- Radiological Sciences Laboratory, Stanford University, Stanford, CA, USA
| | - Huafeng Liu
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
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16
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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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17
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Deep learning based multiplexed sensitivity-encoding (DL-MUSE) for high-resolution multi-shot DWI. Neuroimage 2021; 244:118632. [PMID: 34627977 DOI: 10.1016/j.neuroimage.2021.118632] [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: 07/19/2021] [Revised: 09/27/2021] [Accepted: 10/01/2021] [Indexed: 11/22/2022] Open
Abstract
PURPOSE A phase correction method for high-resolution multi-shot (MSH) diffusion weighted imaging (DWI) is proposed. The efficacy and generalization capability of the method were validated on both healthy volunteers and patients. THEORY AND METHODS Conventionally, inter-shot phase variations for MSH echo-planar imaging (EPI) DWI are corrected by model-based algorithms. However, many acquisition imperfections are hard to measure accurately for conventional model-based methods, making the phase estimation and artifacts suppression unreliable. We propose a deep learning multiplexed sensitivity-encoding (DL-MUSE) framework to improve the phase estimations based on convolutional neural network (CNN) reconstruction. Aliasing-free single-shot (SSH) DW images, which have been used routinely in clinical settings, were used for training before the aliasing correction of MSH-DWI images. A dual-channel U-net comprising multiple convolutional layers was used for the phase estimation of MSH-EPI. The network was trained on a dataset containing 30 healthy volunteers and tested on another dataset of 52 healthy subjects and 15 patients with lesions or tumors with different shot numbers (4, 6 and 8). To further validate the generalization capability of our network, we acquired a dataset with different numbers of shots, TEs, partial Fourier factors, resolutions, ETLs, FOVs, coil numbers, and image orientations from two sites. We also compared the reconstruction performance of our proposed method with that of the conventional MUSE and SSH-EPI qualitatively and quantitatively. RESULTS Our results show that DL-MUSE is capable of correcting inter-shot phase errors with high and robust performance. Compared to conventional model-based MUSE, our method, by applying deep learning-based phase corrections, showed reduced distortion, noise level, and signal loss in high b-value DWIs. The improvements of image quality become more evident as the shot number increases from 4 to 8, especially in those central regions of the images, where g-factor artifacts are severe. Furthermore, the proposed method could provide the information about the orientation of the white matter with better consistency and achieve finer fibers delineation compared to the SSH-EPI method. Besides, the experiments on volunteers and patients from two different sites demonstrated the generalizability of our proposed method preliminarily. CONCLUSION A deep learning-based reconstruction algorithm for MSH-EPI images, which helps improve image quality greatly, was proposed. Results from healthy volunteers and tumor patients demonstrated the feasibility and generalization performances of our method for high-resolution MSH-EPI DWI, which can be used for routine clinical applications as well as neuroimaging research.
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18
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Zhang J, Liu S, Dai E, Ye X, Shi D, Wu Y, Lu J, Guo H. Slab boundary artifact correction in multislab imaging using convolutional-neural-network-enabled inversion for slab profile encoding. Magn Reson Med 2021; 87:1546-1560. [PMID: 34655095 DOI: 10.1002/mrm.29047] [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: 05/21/2021] [Revised: 09/23/2021] [Accepted: 09/25/2021] [Indexed: 11/06/2022]
Abstract
PURPOSE This study aims to propose a novel algorithm for slab boundary artifact correction in both single-band multislab imaging and simultaneous multislab (SMSlab) imaging. THEORY AND METHODS In image domain, the formation of slab boundary artifacts can be regarded as modulating the artifact-free images using the slab profiles and introducing aliasing along the slice direction. Slab boundary artifact correction is the inverse problem of this process. An iterative algorithm based on convolutional neural networks (CNNs) is proposed to solve the problem, termed CNN-enabled inversion for slab profile encoding (CPEN). Diffusion-weighted SMSlab images and reference images without slab boundary artifacts were acquired in 7 healthy subjects for training. Images of 5 healthy subjects were acquired for testing, including single-band multislab and SMSlab images with 1.3-mm or 1-mm isotropic resolution. CNN-enabled inversion for slab profile encoding was compared with a previously reported method (i.e., nonlinear inversion for slab profile encoding [NPEN]). RESULTS CNN-enabled inversion for slab profile encoding reduces the slab boundary artifacts in both single-band multislab and SMSlab images. It also suppresses the slab boundary artifacts in the diffusion metric maps. Compared with NPEN, CPEN shows fewer residual artifacts in different acquisition protocols and more significant improvements in quantitative assessment, and it also accelerates the computation by more than 35 times. CONCLUSION CNN-enabled inversion for slab profile encoding can reduce the slab boundary artifacts in multislab acquisitions. It shows better slab boundary artifact correction capacity, higher robustness, and computation efficiency when compared with NPEN. It has the potential to improve the accuracy of multislab acquisitions in high-resolution DWI and functional MRI.
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Affiliation(s)
- Jieying Zhang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China
| | - Simin Liu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China
| | - Erpeng Dai
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China.,Department of Radiology, Stanford University, Stanford, California, USA
| | - Xinyu Ye
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China
| | - Diwei Shi
- Center for Nano and Micro Mechanics, Tsinghua University, Beijing, People's Republic of China
| | - Yuhsuan Wu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China
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19
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Cao X, Wang K, Liao C, Zhang Z, Srinivasan Iyer S, Chen Z, Lo WC, Liu H, He H, Setsompop K, Zhong J, Bilgic B. Efficient T 2 mapping with blip-up/down EPI and gSlider-SMS (T 2 -BUDA-gSlider). Magn Reson Med 2021; 86:2064-2075. [PMID: 34046924 DOI: 10.1002/mrm.28872] [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: 09/19/2020] [Revised: 05/10/2021] [Accepted: 05/10/2021] [Indexed: 12/22/2022]
Abstract
PURPOSE To rapidly obtain high isotropic-resolution T2 maps with whole-brain coverage and high geometric fidelity. METHODS A T2 blip-up/down EPI acquisition with generalized slice-dithered enhanced resolution (T2 -BUDA-gSlider) is proposed. A RF-encoded multi-slab spin-echo (SE) EPI acquisition with multiple TEs was developed to obtain high SNR efficiency with reduced TR. This was combined with an interleaved 2-shot EPI acquisition using blip-up/down phase encoding. An estimated field map was incorporated into the joint multi-shot EPI reconstruction with a structured low rank constraint to achieve distortion-free and robust reconstruction for each slab without navigation. A Bloch simulated subspace model was integrated into gSlider reconstruction and used for T2 quantification. RESULTS In vivo results demonstrated that the T2 values estimated by the proposed method were consistent with gold standard spin-echo acquisition. Compared to the reference 3D fast spin echo (FSE) images, distortion caused by off-resonance and eddy current effects were effectively mitigated. CONCLUSION BUDA-gSlider SE-EPI acquisition and gSlider-subspace joint reconstruction enabled distortion-free whole-brain T2 mapping in 2 min at ~1 mm3 isotropic resolution, which could bring significant benefits to related clinical and neuroscience applications.
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Affiliation(s)
- Xiaozhi Cao
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Kang Wang
- Department of Neurology, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Zijing Zhang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Siddharth Srinivasan Iyer
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Zhifeng Chen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA.,School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| | - Wei-Ching Lo
- Siemens Medical Solutions, Boston, Massachusetts, USA
| | - Huafeng Liu
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA.,Harvard-MIT Department of Health Sciences and Technology, Cambridge, Massachusetts, USA
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Imaging Sciences, University of Rochester, Rochester, New York, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA.,Harvard-MIT Department of Health Sciences and Technology, Cambridge, Massachusetts, USA
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20
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Riedel Né Steinhoff M, Setsompop K, Mertins A, Börnert P. Segmented simultaneous multi-slice diffusion-weighted imaging with navigated 3D rigid motion correction. Magn Reson Med 2021; 86:1701-1717. [PMID: 33955588 DOI: 10.1002/mrm.28813] [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: 11/02/2020] [Revised: 03/29/2021] [Accepted: 03/29/2021] [Indexed: 11/09/2022]
Abstract
PURPOSE To improve the robustness of diffusion-weighted imaging (DWI) data acquired with segmented simultaneous multi-slice (SMS) echo-planar imaging (EPI) against in-plane and through-plane rigid motion. THEORY AND METHODS The proposed algorithm incorporates a 3D rigid motion correction and wavelet denoising into the image reconstruction of segmented SMS-EPI diffusion data. Low-resolution navigators are used to estimate shot-specific diffusion phase corruptions and 3D rigid motion parameters through SMS-to-volume registration. The shot-wise rigid motion and phase parameters are integrated into a SENSE-based full-volume reconstruction for each diffusion direction. The algorithm is compared to a navigated SMS reconstruction without gross motion correction in simulations and in vivo studies with four-fold interleaved 3-SMS diffusion tensor acquisitions. RESULTS Simulations demonstrate high fidelity was achieved in the SMS-to-volume registration, with submillimeter registration errors and improved image reconstruction quality. In vivo experiments validate successful artifact reduction in 3D motion-compromised in vivo scans with a temporal motion resolution of approximately 0.3 s. CONCLUSION This work demonstrates the feasibility of retrospective 3D rigid motion correction from shot navigators for segmented SMS DWI.
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Affiliation(s)
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.,Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | - Alfred Mertins
- Institute for Signal Processing, University of Luebeck, Luebeck, Germany
| | - Peter Börnert
- Philips Research, Hamburg, Germany.,Radiology, C.J. Gorter Center for High-Field MRI, Leiden University Medical Center, Leiden, The Netherlands
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21
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Dai E, Liu S, Guo H. High-resolution whole-brain diffusion MRI at 3T using simultaneous multi-slab (SMSlab) acquisition. Neuroimage 2021; 237:118099. [PMID: 33940144 DOI: 10.1016/j.neuroimage.2021.118099] [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: 12/12/2020] [Revised: 03/20/2021] [Accepted: 04/24/2021] [Indexed: 01/23/2023] Open
Abstract
High-resolution diffusion MRI (dMRI) is a crucial tool in neuroscience studies to detect fine fiber structure, depict complex fiber architecture and analyze cortical anisotropy. However, high-resolution dMRI is limited by its intrinsically low SNR due to diffusion attenuation. A series of techniques have been proposed to improve the SNR performance, but most of them are at the cost of long scan time, which in turn sacrifice the SNR efficiency, especially for large FOV imaging, such as whole-brain imaging. Recently, a combination of 3D multi-slab acquisition and simultaneous multi-slice (SMS) excitation, namely simultaneous multi-slab (SMSlab), has been demonstrated to have potential for high-resolution diffusion imaging with high SNR and SNR efficiency. In our previous work, we have proposed a 3D Fourier encoding and reconstruction framework for SMSlab acquisition. In this study, we extend this 3D k-space framework to diffusion imaging, by developing a novel navigator acquisition strategy and exploring a k-space-based phase correction method. In vivo brain data are acquired using the proposed SMSlab method and compared with a series of different acquisitions, including the traditional 3D multi-slab, 2D SMS and 2D single-shot EPI (ss-EPI) acquisitions. The results demonstrate that SMSlab has a better SNR performance compared with 3D multi-slab and 2D SMS. The detection capacity of fine fiber structures is improved using SMSlab, compared with the low-resolution diffusion imaging using conventional 2D ss-EPI.
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Affiliation(s)
- Erpeng Dai
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China; Department of Radiology, Stanford University, Stanford, CA, United States
| | - Simin Liu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
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22
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Liao C, Bilgic B, Tian Q, Stockmann JP, Cao X, Fan Q, Iyer SS, Wang F, Ngamsombat C, Lo WC, Manhard MK, Huang SY, Wald LL, Setsompop K. Distortion-free, high-isotropic-resolution diffusion MRI with gSlider BUDA-EPI and multicoil dynamic B 0 shimming. Magn Reson Med 2021; 86:791-803. [PMID: 33748985 DOI: 10.1002/mrm.28748] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 01/10/2021] [Accepted: 02/04/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE We combine SNR-efficient acquisition and model-based reconstruction strategies with newly available hardware instrumentation to achieve distortion-free in vivo diffusion MRI of the brain at submillimeter-isotropic resolution with high fidelity and sensitivity on a clinical 3T scanner. METHODS We propose blip-up/down acquisition (BUDA) for multishot EPI using interleaved blip-up/blip-down phase encoding and incorporate B0 forward-modeling into structured low-rank reconstruction to enable distortion-free and navigator-free diffusion MRI. We further combine BUDA-EPI with an SNR-efficient simultaneous multislab acquisition (generalized slice-dithered enhanced resolution ["gSlider"]), to achieve high-isotropic-resolution diffusion MRI. To validate gSlider BUDA-EPI, whole-brain diffusion data at 860-μm and 780-μm data sets were acquired. Finally, to improve the conditioning and minimize noise penalty in BUDA reconstruction at very high resolutions where B0 inhomogeneity can have a detrimental effect, the level of B0 inhomogeneity was reduced by incorporating slab-by-slab dynamic shimming with a 32-channel AC/DC coil into the acquisition. Whole-brain 600-μm diffusion data were then acquired with this combined approach of gSlider BUDA-EPI with dynamic shimming. RESULTS The results of 860-μm and 780-μm datasets show high geometry fidelity with gSlider BUDA-EPI. With dynamic shimming, the BUDA reconstruction's noise penalty was further alleviated. This enables whole-brain 600-μm isotropic resolution diffusion imaging with high image quality. CONCLUSIONS The gSlider BUDA-EPI method enables high-quality, distortion-free diffusion imaging across the whole brain at submillimeter resolution, where the use of multicoil dynamic B0 shimming further improves reconstruction performance, which can be particularly useful at very high resolutions.
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Affiliation(s)
- Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Jason P Stockmann
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Xiaozhi Cao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Siddharth Srinivasan Iyer
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Chanon Ngamsombat
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA.,Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | | | - Mary Kate Manhard
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
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23
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Hu Y, Xu Y, Tian Q, Chen F, Shi X, Moran CJ, Daniel BL, Hargreaves BA. RUN-UP: Accelerated multishot diffusion-weighted MRI reconstruction using an unrolled network with U-Net as priors. Magn Reson Med 2021; 85:709-720. [PMID: 32783339 PMCID: PMC8095163 DOI: 10.1002/mrm.28446] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 06/17/2020] [Accepted: 07/06/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE To accelerate and improve multishot diffusion-weighted MRI reconstruction using deep learning. METHODS An unrolled pipeline containing recurrences of model-based gradient updates and neural networks was introduced for accelerating multishot DWI reconstruction with shot-to-shot phase correction. The network was trained to predict results of jointly reconstructed multidirection data using single-direction data as input. In vivo brain and breast experiments were performed for evaluation. RESULTS The proposed method achieves a reconstruction time of 0.1 second per image, over 100-fold faster than a shot locally low-rank reconstruction. The resultant image quality is comparable to the target from the joint reconstruction with a peak signal-to-noise ratio of 35.3 dB, a normalized root-mean-square error of 0.0177, and a structural similarity index of 0.944. The proposed method also improves upon the locally low-rank reconstruction (2.9 dB higher peak signal-to-noise ratio, 29% lower normalized root-mean-square error, and 0.037 higher structural similarity index). With training data from the brain, this method also generalizes well to breast diffusion-weighted imaging, and fine-tuning further reduces aliasing artifacts. CONCLUSION A proposed data-driven approach enables almost real-time reconstruction with improved image quality, which improves the feasibility of multishot DWI in a wide range of clinical and neuroscientific studies.
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Affiliation(s)
- Yuxin Hu
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Yunyingying Xu
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Qiyuan Tian
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Feiyu Chen
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Xinwei Shi
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, 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 Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
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24
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Steinhoff M, Nehrke K, Mertins A, Börnert P. Segmented diffusion imaging with iterative motion-corrected reconstruction (SEDIMENT) for brain echo-planar imaging. NMR IN BIOMEDICINE 2020; 33:e4185. [PMID: 31814181 DOI: 10.1002/nbm.4185] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 07/23/2019] [Accepted: 08/14/2019] [Indexed: 06/10/2023]
Abstract
Multi-shot techniques offer improved resolution and signal-to-noise ratio for diffusion- weighted imaging, but make the acquisition vulnerable to shot-specific phase variations and inter-shot macroscopic motion. Several model-based reconstruction approaches with iterative phase correction have been proposed, but robust macroscopic motion estimation is still challenging. Segmented diffusion imaging with iterative motion-corrected reconstruction (SEDIMENT) uses iteratively refined data-driven shot navigators based on sensitivity encoding to cure phase and rigid in-plane motion artifacts. The iterative scheme is compared in simulations and in vivo with a non-iterative reference algorithm for echo-planar imaging with up to sixfold segmentation. The SEDIMENT framework supports partial Fourier acquisitions and furthermore includes options for data rejection and learning-based modules to improve robustness and convergence.
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Affiliation(s)
- Malte Steinhoff
- Institute for Signal Processing, University of Lübeck, Lübeck, Germany
| | - Kay Nehrke
- Philips Research Hamburg, Hamburg, Germany
| | - Alfred Mertins
- Institute for Signal Processing, University of Lübeck, Lübeck, Germany
| | - Peter Börnert
- Philips Research Hamburg, Hamburg, Germany
- Department of Radiology, LUMC, Leiden, The Netherlands
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25
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Yang L, Liu Y, Kong X, Guo X, Liu X, Qi Q, Wang J. Diffusion tensor magnetic resonance imaging of the postoperative spine with metallic implants. NMR IN BIOMEDICINE 2020; 33:e4321. [PMID: 32348023 DOI: 10.1002/nbm.4321] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 04/14/2020] [Accepted: 04/14/2020] [Indexed: 06/11/2023]
Abstract
There has been a growing need to understand the mechanism of development of acute spinal cord injury (SCI) and to optimize treatment. The paramagnetic nature of metallic implants has hampered the application of diffusion tensor imaging (DTI) in postsurgical SCI monitoring. We describe here a successful implementation of spinal DTI in postsurgical SCI patients. Data were acquired using a single-shot turbo-spin-echo sequence, where an extra gradient is applied before the refocusing pulse train to eliminate contributions from the non-Carr-Purcell-Meiboom-Gill components following a diffusion preparation block where a single-spin echo scheme is deployed. The DTI images were acquired in axial orientation with a 2 x 2 x 4 mm3 resolution and a total of 18 slices. Diffusion gradients were applied in six directions with b values of 0 and 600 seconds/mm2 . The whole scan took ~10 minutes. The sequence was compared with SENSE-DW-EPI and ZOOM-DW-EPI on a phantom, eight patients with either anterior or posterior titanium alloy implants, and a pork loin with a similar implant. The protocol resulted in dramatically reduced geometric distortions compared with routine imaging sequences, however, the SNR efficiency was compromised. The spinal cord signal displacement was 0.68±1.00 mm (mean±SD, n = 8) for the proposed protocol, and 5.14±3.07 and 2.82±1.60 mm for the SENSE-DW-EPI and ZOOM-DW-EPI sequences, respectively. Fiber tracking was achieved in the presence of implants, which in one case was accompanied by central spinal cord caviation. Mathematical analysis concluded that the proposed protocol would be generally applicable in the spinal cord when the titanium alloy implant is ~15 mm away (<0.5 kHz B0 field drift). The protocol described is capable of DTI in postsurgery SCI patients with metallic implants at sufficient resolution and SNR.
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Affiliation(s)
- Lian Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuan Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiangchuang Kong
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaodong Guo
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoming Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qian Qi
- MSC Clinical & Technical Solutions, Philips Healthcare, Beijing, China
| | - Jiazheng Wang
- MSC Clinical & Technical Solutions, Philips Healthcare, Beijing, China
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26
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Aggarwal HK, Mani MP, Jacob M. MoDL-MUSSELS: Model-Based Deep Learning for Multishot Sensitivity-Encoded Diffusion MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1268-1277. [PMID: 31603819 PMCID: PMC7894613 DOI: 10.1109/tmi.2019.2946501] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
We introduce a model-based deep learning architecture termed MoDL-MUSSELS for the correction of phase errors in multishot diffusion-weighted echo-planar MR images. The proposed algorithm is a generalization of the existing MUSSELS algorithm with similar performance but significantly reduced computational complexity. In this work, we show that an iterative re-weighted least-squares implementation of MUSSELS alternates between a multichannel filter bank and the enforcement of data consistency. The multichannel filter bank projects the data to the signal subspace, thus exploiting the annihilation relations between shots. Due to the high computational complexity of the self-learned filter bank, we propose replacing it with a convolutional neural network (CNN) whose parameters are learned from exemplary data. The proposed CNN is a hybrid model involving a multichannel CNN in the k-space and another CNN in the image space. The k-space CNN exploits the annihilation relations between the shot images, while the image domain network is used to project the data to an image manifold. The experiments show that the proposed scheme can yield reconstructions that are comparable to state-of-the-art methods while offering several orders of magnitude reduction in run-time.
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27
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Mani M, Aggarwal HK, Magnotta V, Jacob M. Improved MUSSELS reconstruction for high-resolution multi-shot diffusion weighted imaging. Magn Reson Med 2019; 83:2253-2263. [PMID: 31789440 DOI: 10.1002/mrm.28090] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Revised: 10/21/2019] [Accepted: 10/30/2019] [Indexed: 12/13/2022]
Abstract
PURPOSE MUSSELS is a one-step iterative reconstruction method for multishot diffusion weighted (msDW) imaging. The current work presents an efficient implementation, termed IRLS MUSSELS, that enables faster reconstruction to enhance its utility for high-resolution diffusion MRI studies. METHODS The recently proposed MUSSELS reconstruction belongs to a new class of parallel imaging-based methods that recover artifact-free DWIs from msDW data without needing phase compensation. The reconstruction is achieved via structured low-rank matrix completion algorithms, which are computationally demanding due to the large size of the Hankel matrices and their associated computations involving singular value decompositions. Because of this, computational demands of the MUSSELS reconstruction scales as the matrix size and the number of shots increases, which hinders its practical utility for high-resolution applications. In this work, we derive a computationally efficient MUSSELS formulation by modifying the iterative reweighted least squares (IRLS) method that were proposed earlier to solve such problems. Using whole-brain in vivo data, we show the utility of the IRLS MUSSELS for routine high-resolution studies with reduced computational burden. RESULTS IRLS MUSSELS provides about five times faster reconstruction for matrix sizes 192 × 192 and 256 × 256 compared to the earlier MUSSELS implementation. The widely employed conjugate symmetry priors can also be incorporated into IRLS MUSSELS to reduce blurring of the partial Fourier acquisitions, without incurring much computational burden. CONCLUSIONS The proposed method is observed to be computationally efficient to enable routine high-resolution studies. The computational complexity matches the traditional msDWI reconstruction methods and provides improved reconstruction results with the additional constraints.
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Affiliation(s)
- Merry Mani
- Department of Radiology, University of Iowa, Iowa City, Iowa, USA
| | - Hemant Kumar Aggarwal
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA
| | - Vincent Magnotta
- Department of Radiology, University of Iowa, Iowa City, Iowa, USA
| | - Mathews Jacob
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA
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28
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Tian R, Hennel F, Pruessmann KP. Low-distortion diffusion tensor MRI with improved phaseless encoding. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2019; 309:106602. [PMID: 31614304 DOI: 10.1016/j.jmr.2019.106602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 09/13/2019] [Accepted: 09/16/2019] [Indexed: 06/10/2023]
Abstract
Due to the motion-related instability of the signal phase, diffusion MRI is usually performed with single-shot techniques such as the echo-planar imaging (EPI), which are resolution-limited and suffer from distortions caused by resonance offsets. Multi-shot methods may improve the images but require time-consuming navigators or a trade-off of the sensitivity encoding to measure shot-dependent phase errors. We have recently introduced an alternative approach to multi-shot MRI called phaseless encoding, which, by analogy to optical super-resolution methods, relies on the magnitude value of images taken in different shots thus discarding the phase error without navigators, and demonstrated its capability to perform diffusion MRI at sub-millimeter scale on a standard 3T scanner. In this work, we apply phaseless encoding in a routine diffusion tensor imaging (DTI) protocol with a moderately high resolution that is still within reach of single-shot EPI with the same hardware, and compare both techniques with respect to image distortions. A qualitative comparison of the phaseless encoding with the established navigator-based readout-segmented EPI is also presented. Several technical improvements are proposed to make phaseless encoding compatible with the routine scanning mode. The tagging radiofrequency pulses used in the encoding sequence are made slice-selective to avoid artefacts caused by saturation effects in multi-slice scans and their flip angle is optimized to reduce the intrinsic SNR loss. The super-resolution reconstruction algorithm is also improved to better suppress Gibbs ringing and to correct for possible signal amplitude fluctuations. Our study shows that the phaseless encoding is a promising approach to diffusion weighted imaging. It can easily be implemented in multi-slice sequences and produces less distorted images than the single-shot EPI at the same resolution and hardware parameters. It provides similar results to readout-segmented EPI but without the need of navigators.
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Affiliation(s)
- Rui Tian
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland.
| | - Franciszek Hennel
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Klaas P Pruessmann
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
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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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Hu Y, Wang X, Tian Q, Yang G, Daniel B, McNab J, Hargreaves B. Multi-shot diffusion-weighted MRI reconstruction with magnitude-based spatial-angular locally low-rank regularization (SPA-LLR). Magn Reson Med 2019; 83:1596-1607. [PMID: 31593337 DOI: 10.1002/mrm.28025] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 09/08/2019] [Accepted: 09/09/2019] [Indexed: 11/05/2022]
Abstract
PURPOSE To resolve the motion-induced phase variations in multi-shot multi-direction diffusion-weighted imaging (DWI) by applying regularization to magnitude images. THEORY AND METHODS A nonlinear model was developed to estimate phase and magnitude images separately. A locally low-rank regularization (LLR) term was applied to the magnitude images from all diffusion-encoding directions to exploit the spatial and angular correlation. In vivo experiments with different resolutions and b-values were performed to validate the proposed method. RESULTS The proposed method significantly reduces the noise level compared to the conventional reconstruction method and achieves submillimeter (0.8mm and 0.9mm isotropic resolutions) DWI with a b-value of 1,000 s / mm 2 and 1-mm isotropic DWI with a b-value of 2,000 s / mm 2 without modification of the sequence. CONCLUSIONS A joint reconstruction method with spatial-angular LLR regularization on magnitude images substantially improves multi-direction DWI reconstruction, simultaneously removes motion-induced phase artifacts, and denoises images.
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Affiliation(s)
- Yuxin Hu
- Department of Electrical Engineering, Stanford University, Stanford, California.,Department of Radiology, Stanford University, Stanford, California
| | - Xiaole Wang
- Department of Electrical Engineering, Stanford University, Stanford, California
| | - Qiyuan Tian
- Department of Electrical Engineering, Stanford University, Stanford, California.,Department of Radiology, Stanford University, Stanford, California
| | - Grant Yang
- Department of Electrical Engineering, Stanford University, Stanford, California.,Department of Radiology, Stanford University, Stanford, California
| | - Bruce Daniel
- Department of Radiology, Stanford University, Stanford, California.,Department of Bioengineering, Stanford University, Stanford, California
| | - Jennifer McNab
- Department of Radiology, Stanford University, Stanford, California
| | - Brian Hargreaves
- Department of Electrical Engineering, Stanford University, Stanford, California.,Department of Radiology, Stanford University, Stanford, California.,Department of Bioengineering, Stanford University, Stanford, California
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31
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Bilgic B, Chatnuntawech I, Manhard MK, Tian Q, Liao C, Iyer SS, Cauley SF, Huang SY, Polimeni JR, Wald LL, Setsompop K. Highly accelerated multishot echo planar imaging through synergistic machine learning and joint reconstruction. Magn Reson Med 2019; 82:1343-1358. [PMID: 31106902 PMCID: PMC6626584 DOI: 10.1002/mrm.27813] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 04/22/2019] [Accepted: 04/22/2019] [Indexed: 12/13/2022]
Abstract
PURPOSE To introduce a combined machine learning (ML)- and physics-based image reconstruction framework that enables navigator-free, highly accelerated multishot echo planar imaging (msEPI) and demonstrate its application in high-resolution structural and diffusion imaging. METHODS Single-shot EPI is an efficient encoding technique, but does not lend itself well to high-resolution imaging because of severe distortion artifacts and blurring. Although msEPI can mitigate these artifacts, high-quality msEPI has been elusive because of phase mismatch arising from shot-to-shot variations which preclude the combination of the multiple-shot data into a single image. We utilize deep learning to obtain an interim image with minimal artifacts, which permits estimation of image phase variations attributed to shot-to-shot changes. These variations are then included in a joint virtual coil sensitivity encoding (JVC-SENSE) reconstruction to utilize data from all shots and improve upon the ML solution. RESULTS Our combined ML + physics approach enabled Rinplane × multiband (MB) = 8- × 2-fold acceleration using 2 EPI shots for multiecho imaging, so that whole-brain T2 and T2 * parameter maps could be derived from an 8.3-second acquisition at 1 × 1 × 3-mm3 resolution. This has also allowed high-resolution diffusion imaging with high geometrical fidelity using 5 shots at Rinplane × MB = 9- × 2-fold acceleration. To make these possible, we extended the state-of-the-art MUSSELS reconstruction technique to simultaneous multislice encoding and used it as an input to our ML network. CONCLUSION Combination of ML and JVC-SENSE enabled navigator-free msEPI at higher accelerations than previously possible while using fewer shots, with reduced vulnerability to poor generalizability and poor acceptance of end-to-end ML approaches.
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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
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Itthi Chatnuntawech
- National Nanotechnology Center, National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Mary Kate Manhard
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Siddharth S. Iyer
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stephen F. Cauley
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Susie Y. Huang
- 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
| | - Jonathan R. Polimeni
- 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
| | - 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
| | - 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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Mani M, Jacob M, McKinnon G, Yang B, Rutt B, Kerr A, Magnotta V. SMS MUSSELS: A navigator-free reconstruction for simultaneous multi-slice-accelerated multi-shot diffusion weighted imaging. Magn Reson Med 2019; 83:154-169. [PMID: 31403223 DOI: 10.1002/mrm.27924] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Revised: 06/08/2019] [Accepted: 07/08/2019] [Indexed: 12/22/2022]
Abstract
PURPOSE To introduce a novel reconstruction method for simultaneous multi-slice (SMS)-accelerated multi-shot diffusion weighted imaging (ms-DWI). METHODS SMS acceleration using blipped-CAIPI schemes have been proposed to speed up the acquisition of ms-DWIs. The reconstruction of the data requires (a) phase compensation to combine data from different shots and (b) slice unfolding to separate the data of different slices. The traditional approaches first estimate the phase maps corresponding to each shot and slice which are then employed to iteratively recover the slice unfolded DWIs without phase artifacts. In contrast, the proposed reconstruction directly recovers the slice-unfolded k-space data of the multiple shots for each slice in a single-step recovery scheme. The proposed method is enabled by the low-rank property inherent in the k-space samples of ms-DW acquisition. This enabled to formulate a joint recovery scheme that simultaneously (a) unfolds the k-space data of each slice using a SENSE-based scheme and (b) recover the missing k-space samples in each slice of the multi-shot acquisition employing a structured low-rank matrix completion. Additional smoothness regularization is also utilized for higher acceleration factors. The proposed joint recovery is tested on simulated and in vivo data and compared to similar un-navigated methods. RESULTS Our experiments show effective slice unfolding and successful recovery of DWIs with minimal phase artifacts using the proposed method. The performance is comparable to existing methods at low acceleration factors and better than existing methods for higher acceleration factors. CONCLUSIONS For the slice accelerations considered in this study, the proposed method can successfully recover DWIs from SMS-accelerated ms-DWI acquisitions.
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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
| | - Graeme McKinnon
- Global Applied Science Laboratory, GE Healthcare, Waukesha, Wisconsin
| | - Baolian Yang
- Global Applied Science Laboratory, GE Healthcare, Waukesha, Wisconsin
| | - Brian Rutt
- Department of Radiology, Stanford University, Stanford, California
| | - Adam Kerr
- Department of Radiology, Stanford University, Stanford, California
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33
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Hu Y, Levine EG, Tian Q, Moran CJ, Wang X, Taviani V, Vasanawala S, McNab JA, Daniel BL, Hargreaves BA. Motion-robust reconstruction of multishot diffusion-weighted images without phase estimation through locally low-rank regularization. Magn Reson Med 2019; 81:1181-1190. [PMID: 30346058 PMCID: PMC6289606 DOI: 10.1002/mrm.27488] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Revised: 07/11/2018] [Accepted: 07/18/2018] [Indexed: 11/12/2022]
Abstract
PURPOSE The goal of this work is to propose a motion robust reconstruction method for diffusion-weighted MRI that resolves shot-to-shot phase mismatches without using phase estimation. METHODS Assuming that shot-to-shot phase variations are slowly varying, spatial-shot matrices can be formed using a local group of pixels to form columns, in which each column is from a different shot (excitation). A convex model with a locally low-rank constraint on the spatial-shot matrices is proposed. In vivo brain and breast experiments were performed to evaluate the performance of the proposed method. RESULTS The proposed method shows significant benefits when the motion is severe, such as for breast imaging. Furthermore, the resulting images can be used for reliable phase estimation in the context of phase-estimation-based methods to achieve even higher image quality. CONCLUSION We introduced the shot-locally low-rank method, a reconstruction technique for multishot diffusion-weighted MRI without explicit phase estimation. In addition, its motion robustness can be beneficial to neuroimaging and body imaging.
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Affiliation(s)
- Yuxin Hu
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Evan G. Levine
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Qiyuan Tian
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
| | | | - Xiaole Wang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | | | | | - Jennifer A. McNab
- Department of Radiology, 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 Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
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34
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Gao Y, Han F, Zhou Z, Zhong X, Bi X, Neylon J, Santhanam A, Yang Y, Hu P. Multishot diffusion‐prepared magnitude‐stabilized balanced steady‐state free precession sequence for distortion‐free diffusion imaging. Magn Reson Med 2018; 81:2374-2384. [DOI: 10.1002/mrm.27565] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 09/14/2018] [Accepted: 09/19/2018] [Indexed: 11/11/2022]
Affiliation(s)
- Yu Gao
- Department of Radiological Sciences University of California Los Angeles California
- Physics and Biology in Medicine IDP University of California Los Angeles California
| | - Fei Han
- Department of Radiological Sciences University of California Los Angeles California
- MR R&D Collaborations, Siemens Healthineers Los Angeles California
| | - Ziwu Zhou
- Department of Radiological Sciences University of California Los Angeles California
| | - Xiaodong Zhong
- MR R&D Collaborations, Siemens Healthineers Los Angeles California
| | - Xiaoming Bi
- MR R&D Collaborations, Siemens Healthineers Los Angeles California
| | - John Neylon
- Department of Radiation Oncology University of California Los Angeles California
| | - Anand Santhanam
- Department of Radiation Oncology University of California Los Angeles California
| | - Yingli Yang
- Physics and Biology in Medicine IDP University of California Los Angeles California
- Department of Radiation Oncology University of California Los Angeles California
| | - Peng Hu
- Physics and Biology in Medicine IDP University of California Los Angeles California
- Department of Radiation Oncology University of California Los Angeles California
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35
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Hennel F, Tian R, Engel M, Pruessmann KP. In-plane "superresolution" MRI with phaseless sub-pixel encoding. Magn Reson Med 2018; 80:2384-2392. [PMID: 29656440 DOI: 10.1002/mrm.27209] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Revised: 03/13/2018] [Accepted: 03/13/2018] [Indexed: 12/13/2022]
Abstract
PURPOSE Acquisition of high-resolution imaging data using multiple excitations without the sensitivity to fluctuations of the transverse magnetization phase, which is a major problem of multi-shot MRI. THEORY AND METHODS The concept of superresolution MRI based on microscopic tagging is analyzed using an analogy with the optical method of structured illumination. Sinusoidal tagging is shown to provide subpixel resolution by mixing of neighboring spatial frequency (k-space) bands. It represents a phaseless modulation added on top of the standard Fourier encoding, which allows the phase fluctuations to be discarded at an intermediate reconstruction step. Improvements are proposed to correct for tag distortions due to magnetic field inhomogeneity and to avoid the propagation of Gibbs ringing from intermediate low-resolution images to the final image. The method was applied to diffusion-weighted EPI. RESULTS Artifact-free superresolution images can be obtained despite a finite duration of the tagging sequence and related pattern distortions by a field map based phase correction of band-wise reconstructed images. The ringing effect present in the intermediate images can be suppressed by partial overlapping of the mixed k-space bands in combination with an adapted filter. High-resolution diffusion-weighted images of the human head were obtained with a three-shot EPI sequence despite motion-related phase fluctuations between the shots. CONCLUSION Due to its phaseless character, tagging-based sub-pixel encoding is an alternative to k-space segmenting in the presence of unknown phase fluctuations, in particular those due to motion under strong diffusion gradients. Proposed improvements render the method practicable in realistic conditions.
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Affiliation(s)
- Franciszek Hennel
- Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Rui Tian
- Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Maria Engel
- Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Klaas P Pruessmann
- Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
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36
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Guo L, Huang F, Xu Z, Mei Y, Fang W, Ma X, Dai E, Guo H, Feng Q, Chen W, Feng Y. eIRIS: Eigen-analysis approach for improved spine multi-shot diffusion MRI. Magn Reson Imaging 2018; 50:134-140. [PMID: 29626517 DOI: 10.1016/j.mri.2018.04.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Revised: 03/30/2018] [Accepted: 04/03/2018] [Indexed: 01/28/2023]
Abstract
Image reconstruction using image-space sampling function (IRIS) corrects motion-induced inter-shot phase variations using phase maps from navigator-echo for multi-shot diffusion MRI. However, the bandwidth along the phase-encoding direction of navigator-echo is usually lower than that of image-echo, and thus their geometric distortions may be different. This geometric mismatch is corrected in IRIS by using the B0 map from an additional scan. In this paper, we present an enhanced IRIS (eIRIS) method that remove the requirement of B0 map. eIRIS treats shots as virtual coils, and then uses an eigen-analysis-based approach, which is insensitive to geometric mismatch, to estimates coil sensitivity maps containing the inter-shot phase variations. The final image is reconstructed under the framework of SENSE. Simulation, phantom, and cervical spine experiments were performed to evaluate the eIRIS method. The images generated by IRIS without B0 correction contain severe artifacts. eIRIS obtains results without noticeable artifacts and comparable to those of IRIS with B0 correction and GRAPPA with a compact kernel (GRAPPA-CK) method. eIRIS slightly outperforms GRAPPA-CK in the terms of normalized root-mean-square error and signal-to-noise ratio. eIRIS has the potential to obtain high-quality diffusion-weighted images and will benefit the research and clinical diagnosis of spinal cord.
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Affiliation(s)
- Li Guo
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, No. 1838, Guangzhou Road North, Guangzhou, China
| | - Feng Huang
- Neusoft Medical System, No. 10001, Ziyue Road, Shanghai, China
| | - Zhongbiao Xu
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, No. 1838, Guangzhou Road North, Guangzhou, China
| | - Yingjie Mei
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, No. 1838, Guangzhou Road North, Guangzhou, China; Philips, Healthcare, No. 33, Zhongshan San Road, Guangzhou, China
| | - Wenxing Fang
- Philips, Healthcare, No. 258, Zhongyuan Road, Suzhou, China
| | - Xiaodong Ma
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, No. 30, Shuangqing Road, Beijing, China
| | - Erpeng Dai
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, No. 30, Shuangqing Road, Beijing, China
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, No. 30, Shuangqing Road, Beijing, China
| | - Qianjin Feng
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, No. 1838, Guangzhou Road North, Guangzhou, China
| | - Wufan Chen
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, No. 1838, Guangzhou Road North, Guangzhou, China.
| | - Yanqiu Feng
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, No. 1838, Guangzhou Road North, Guangzhou, China.
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Dai E, Zhang Z, Ma X, Dong Z, Li X, Xiong Y, Yuan C, Guo H. The effects of navigator distortion and noise level on interleaved EPI DWI reconstruction: a comparison between image‐ and k‐space‐based method. Magn Reson Med 2018; 80:2024-2032. [DOI: 10.1002/mrm.27190] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Revised: 02/08/2018] [Accepted: 03/03/2018] [Indexed: 11/06/2022]
Affiliation(s)
- Erpeng Dai
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of MedicineTsinghua UniversityBeijing China
| | - Zhe Zhang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of MedicineTsinghua UniversityBeijing China
| | - Xiaodong Ma
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of MedicineTsinghua UniversityBeijing China
| | - Zijing Dong
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of MedicineTsinghua UniversityBeijing China
| | - Xuesong Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of MedicineTsinghua UniversityBeijing China
- School of Computer Science and TechnologyBeijing Institute of TechnologyBeijing China
| | - Yuhui Xiong
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of MedicineTsinghua UniversityBeijing China
| | - Chun Yuan
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of MedicineTsinghua UniversityBeijing China
- Department of RadiologyUniversity of WashingtonSeattle Washington
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of MedicineTsinghua UniversityBeijing China
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Chu ML, Chang HC, Chung HW, Bashir MR, Cai J, Zhang L, Sun D, Chen NK. Free-breathing abdominal MRI improved by repeated k-t-subsampling and artifact-minimization (ReKAM). Med Phys 2017; 45:178-190. [PMID: 29148576 DOI: 10.1002/mp.12674] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 11/02/2017] [Accepted: 11/03/2017] [Indexed: 11/08/2022] Open
Abstract
PURPOSE We report an approach, termed Repeated k-t-subsampling and artifact-minimization (ReKAM), for removing motion artifacts in free-breathing abdominal MRI. The method is particularly valuable for challenging patients who may not hold their breath for a long time or have irregular respiratory rate. METHODS The ReKAM framework comprises one acquisition module and two reconstruction modules. A fast MRI sequence is used to repeatedly acquire multiple sets of k-t space data. Motion artifacts are then minimized by two reconstruction modules: (a) a bootstrapping module in k-t-space is used to identify a low-artifact image; (b) a constrained reconstruction module that integrates projection onto convex set (POCS) and multiplexed sensitivity encoding (MUSE), termed POCSMUSE, is applied to further remove residual artifact. The ReKAM framework is compatible with different pulse sequences, and generally applicable to irregular data sampling patterns in k-space. Free-breathing fast spin-echo MRI data, acquired from healthy volunteers and patients, were used to evaluate the developed ReKAM method. RESULTS Experimental results show that the ReKAM technique can produce high-quality free-breathing images with the artifact levels comparable to that of breath-holding MRI. CONCLUSION The ReKAM framework improves the quality of free-breathing abdominal MRI data, and is compatible with various MRI pulse sequences.
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Affiliation(s)
- Mei-Lan Chu
- Department of Biomedical Engineering, University of Arizona, 1127 E. James E. Rogers Way, P.O. Box 210020, Tucson, AZ, 85721-0020, USA
| | - Hing-Chiu Chang
- Department of Diagnostic Radiology, The University of Hong Kong, Room 406, Block K, Queen Mary Hospital, Pok Fu Lam Road, Hong Kong, China
| | - Hsiao-Wen Chung
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No.1, Sec. 4, Roosevelt Road, Taipei, Taiwan, 106
| | - Mustafa R Bashir
- Department of Radiology, Duke University Medical Center, 2301 Erwin Road, Durham, NC, 27710, USA.,Center for Advanced Magnetic Resonance Development, Duke University Medical Center, 2301 Erwin Road, Durham, NC, 27710, USA
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Room Y934, 9/F, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong, China.,Department of Radiation Oncology, Duke University Medical Center, 2301 Erwin Road, Durham, NC, 27710, USA
| | - Lei Zhang
- Medical Physics Graduate Program, Duke University, 2424 Erwin Road, Hock Plaza, Suite 101, Durham, NC, 27705, USA
| | - Duohua Sun
- Department of Biomedical Engineering, University of Arizona, 1127 E. James E. Rogers Way, P.O. Box 210020, Tucson, AZ, 85721-0020, USA
| | - Nan-Kuei Chen
- Department of Biomedical Engineering, University of Arizona, 1127 E. James E. Rogers Way, P.O. Box 210020, Tucson, AZ, 85721-0020, USA.,Department of Radiology, Duke University Medical Center, 2301 Erwin Road, Durham, NC, 27710, USA.,Medical Physics Graduate Program, Duke University, 2424 Erwin Road, Hock Plaza, Suite 101, Durham, NC, 27705, USA.,Brain Imaging and Analysis Center, Duke University Medical Center, 40 Duke Medicine Circle, Room 414, Durham, NC, 27710, USA
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Tan ET, Hardy CJ, Shu Y, In MH, Guidon A, Huston J, Bernstein MA, K F Foo T. Reduced acoustic noise in diffusion tensor imaging on a compact MRI system. Magn Reson Med 2017; 79:2902-2911. [PMID: 28971512 DOI: 10.1002/mrm.26949] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Revised: 09/05/2017] [Accepted: 09/06/2017] [Indexed: 01/08/2023]
Abstract
PURPOSE To investigate the feasibility of substantially reducing acoustic noise while performing diffusion tensor imaging (DTI) on a compact 3T (C3T) MRI scanner equipped with a 42-cm inner-diameter asymmetric gradient. METHODS A-weighted acoustic measurements were made using 10 mT/m-amplitude sinusoidal waveforms, corresponding to echo-planar imaging (EPI) echo spacing of 0.25 to 5.0 ms, on a conventional, whole-body 3T MRI and on the C3T. Acoustic measurements of DTI with trapezoidal EPI waveforms were then made at peak gradient performance on the C3T (80 mT/m amplitude, 700 T/m/s slew rate) and at derated performance (33 mT/m, 10 to 50 T/m/s) for acoustic noise reduction. DTI was acquired in two different phantoms and in seven human subjects, with and without gradient-derating corresponding to multi- and single-shot acquisitions, respectively. RESULTS Sinusoidal waveforms on the C3T were quieter by 8.5 to 15.6 A-weighted decibels (dBA) on average as compared to the whole-body MRI. The derated multishot DTI acquisition noise level was only 8.7 dBA (at 13 T/m/s slew rate) above ambient, and was quieter than non-derated, single-shot DTI by 22.3 dBA; however, the scan time was almost quadrupled. Although derating resulted in negligible diffusivity differences in the phantoms, small biases in diffusivity measurements were observed in human subjects (apparent diffusion coefficient = +9.3 ± 8.8%, fractional anisotropy = +3.2 ± 11.2%, radial diffusivity = +9.4 ± 16.8%, parallel diffusivity = +10.3 ± 8.4%). CONCLUSION The feasibility of achieving reduced acoustic noise levels with whole-brain DTI on the C3T MRI was demonstrated. Magn Reson Med 79:2902-2911, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Ek T Tan
- GE Global Research, Niskayuna, New York, USA
| | | | - Yunhong Shu
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Myung-Ho In
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - John Huston
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
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40
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Mani M, Jacob M, Kelley D, Magnotta V. Multi-shot sensitivity-encoded diffusion data recovery using structured low-rank matrix completion (MUSSELS). Magn Reson Med 2017; 78:494-507. [PMID: 27550212 PMCID: PMC5336529 DOI: 10.1002/mrm.26382] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2016] [Revised: 07/12/2016] [Accepted: 07/23/2016] [Indexed: 12/13/2022]
Abstract
PURPOSE To introduce a novel method for the recovery of multi-shot diffusion weighted (MS-DW) images from echo-planar imaging (EPI) acquisitions. METHODS Current EPI-based MS-DW reconstruction methods rely on the explicit estimation of the motion-induced phase maps to recover artifact-free images. In the new formulation, the k-space data of the artifact-free DWI is recovered using a structured low-rank matrix completion scheme, which does not require explicit estimation of the phase maps. The structured matrix is obtained as the lifting of the multi-shot data. The smooth phase-modulations between shots manifest as null-space vectors of this matrix, which implies that the structured matrix is low-rank. The missing entries of the structured matrix are filled in using a nuclear-norm minimization algorithm subject to the data-consistency. The formulation enables the natural introduction of smoothness regularization, thus enabling implicit motion-compensated recovery of the MS-DW data. RESULTS Our experiments on in-vivo data show effective removal of artifacts arising from inter-shot motion using the proposed method. The method is shown to achieve better reconstruction than the conventional phase-based methods. CONCLUSION We demonstrate the utility of the proposed method to effectively recover artifact-free images from Cartesian fully/under-sampled and partial Fourier acquired data without the use of explicit phase estimates. Magn Reson Med 78:494-507, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Merry Mani
- Department of Psychiatry, University of Iowa, Iowa City, Iowa, USA
| | - Mathews Jacob
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA
| | | | - Vincent Magnotta
- Department of Psychiatry, University of Iowa, Iowa City, Iowa, USA
- Department of Radiology, University of Iowa, Iowa City, Iowa, USA
- Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
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41
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Chu ML, Chang HC, Oshio K, Chen NK. A single-shot T2
mapping protocol based on echo-split gradient-spin-echo acquisition and parametric multiplexed sensitivity encoding based on projection onto convex sets reconstruction. Magn Reson Med 2017; 79:383-393. [DOI: 10.1002/mrm.26696] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2017] [Revised: 02/24/2017] [Accepted: 03/12/2017] [Indexed: 11/11/2022]
Affiliation(s)
- Mei-Lan Chu
- Brain Imaging and Analysis Center; Duke University Medical Center; Durham North Carolina USA
| | - Hing-Chiu Chang
- Department of Diagnostic Radiology; The University of Hong Kong; Hong Kong
| | - Koichi Oshio
- Department of Diagnostic Radiology; Keio University School of Medicine; Tokyo Japan
| | - Nan-kuei Chen
- Brain Imaging and Analysis Center; Duke University Medical Center; Durham North Carolina USA
- Department of Biomedical Engineering; University of Arizona; Tucson Arizona USA
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42
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A New Joint-Blade SENSE Reconstruction for Accelerated PROPELLER MRI. Sci Rep 2017; 7:42602. [PMID: 28205602 PMCID: PMC5311996 DOI: 10.1038/srep42602] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2016] [Accepted: 01/11/2017] [Indexed: 12/24/2022] Open
Abstract
PROPELLER technique is widely used in MRI examinations for being motion insensitive, but it prolongs scan time and is restricted mainly to T2 contrast. Parallel imaging can accelerate PROPELLER and enable more flexible contrasts. Here, we propose a multi-step joint-blade (MJB) SENSE reconstruction to reduce the noise amplification in parallel imaging accelerated PROPELLER. MJB SENSE utilizes the fact that PROPELLER blades contain sharable information and blade-combined images can serve as regularization references. It consists of three steps. First, conventional blade-combined images are obtained using the conventional simple single-blade (SSB) SENSE, which reconstructs each blade separately. Second, the blade-combined images are employed as regularization for blade-wise noise reduction. Last, with virtual high-frequency data resampled from the previous step, all blades are jointly reconstructed to form the final images. Simulations were performed to evaluate the proposed MJB SENSE for noise reduction and motion correction. MJB SENSE was also applied to both T2-weighted and T1-weighted in vivo brain data. Compared to SSB SENSE, MJB SENSE greatly reduced the noise amplification at various acceleration factors, leading to increased image SNR in all simulation and in vivo experiments, including T1-weighted imaging with short echo trains. Furthermore, it preserved motion correction capability and was computationally efficient.
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43
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Chao TC, Chiou JYG, Maier SE, Madore B. Fast diffusion imaging with high angular resolution. Magn Reson Med 2017; 77:696-706. [PMID: 26899270 PMCID: PMC4992669 DOI: 10.1002/mrm.26163] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Revised: 01/07/2016] [Accepted: 01/21/2016] [Indexed: 11/07/2022]
Abstract
PURPOSE High angular resolution diffusion imaging (HARDI) is a well-established method to help reveal the architecture of nerve bundles, but long scan times and geometric distortions inherent to echo planar imaging (EPI) have limited its integration into clinical protocols. METHODS A fast imaging method is proposed here that combines accelerated multishot diffusion imaging (AMDI), multiplexed sensitivity encoding (MUSE), and crossing fiber angular resolution of intravoxel structure (CFARI) to reduce spatial distortions and reduce total scan time. A multishot EPI sequence was used to improve geometrical fidelity as compared to a single-shot EPI acquisition, and acceleration in both k-space and diffusion sampling enabled reductions in scan time. The method is regularized and self-navigated for motion correction. Seven volunteers were scanned in this study, including four with volumetric whole brain acquisitions. RESULTS The average similarity of microstructural orientations between undersampled datasets and their fully sampled counterparts was above 85%, with scan times below 5 min for whole-brain acquisitions. Up to 2.7-fold scan time acceleration along with four-fold distortion reduction was achieved. CONCLUSION The proposed imaging strategy can generate HARDI results with relatively good geometrical fidelity and low scan duration, which may help facilitate the transition of HARDI from a successful research tool to a practical clinical one. Magn Reson Med 77:696-706, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Tzu-Cheng Chao
- Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan, Taiwan
- Institute of Medical Informatics, National Cheng-Kung University, Tainan, Taiwan
| | - Jr-yuan George Chiou
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Stephan E. Maier
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg University, Gothenburg, Sweden
| | - Bruno Madore
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Wilm BJ, Barmet C, Gross S, Kasper L, Vannesjo SJ, Haeberlin M, Dietrich BE, Brunner DO, Schmid T, Pruessmann KP. Single‐shot spiral imaging enabled by an expanded encoding model:
D
emonstration in diffusion
MRI. Magn Reson Med 2016; 77:83-91. [DOI: 10.1002/mrm.26493] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Revised: 08/29/2016] [Accepted: 09/14/2016] [Indexed: 11/08/2022]
Affiliation(s)
- Bertram J. Wilm
- Institute for Biomedical EngineeringUniversity of Zurich and ETH ZurichZurich Switzerland
| | - Christoph Barmet
- Institute for Biomedical EngineeringUniversity of Zurich and ETH ZurichZurich Switzerland
- Skope Magnetic Resonance Technologies IncZurich Switzerland
| | - Simon Gross
- Institute for Biomedical EngineeringUniversity of Zurich and ETH ZurichZurich Switzerland
| | - Lars Kasper
- Institute for Biomedical EngineeringUniversity of Zurich and ETH ZurichZurich Switzerland
- Translational Neuromodeling Unit, Institute for Biomedical EngineeringUniversity of Zurich and ETH ZurichZurich Switzerland
| | - S. Johanna Vannesjo
- Institute for Biomedical EngineeringUniversity of Zurich and ETH ZurichZurich Switzerland
| | - Max Haeberlin
- Institute for Biomedical EngineeringUniversity of Zurich and ETH ZurichZurich Switzerland
| | - Benjamin E. Dietrich
- Institute for Biomedical EngineeringUniversity of Zurich and ETH ZurichZurich Switzerland
| | - David O. Brunner
- Institute for Biomedical EngineeringUniversity of Zurich and ETH ZurichZurich Switzerland
| | - Thomas Schmid
- Institute for Biomedical EngineeringUniversity of Zurich and ETH ZurichZurich Switzerland
| | - Klaas P. Pruessmann
- Institute for Biomedical EngineeringUniversity of Zurich and ETH ZurichZurich Switzerland
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Zhang Z, Zhang B, Li M, Liang X, Chen X, Liu R, Zhang X, Guo H. Multishot cartesian turbo spin-echo diffusion imaging using iterative POCSMUSE Reconstruction. J Magn Reson Imaging 2016; 46:167-174. [PMID: 27766699 DOI: 10.1002/jmri.25522] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2016] [Accepted: 10/07/2016] [Indexed: 11/08/2022] Open
Affiliation(s)
- Zhe Zhang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering; Tsinghua University; Beijing China
| | - Bing Zhang
- Department of Radiology; Affiliated Drum Tower Hospital of Nanjing University Medical School; Nanjing China
| | - Ming Li
- Department of Radiology; Affiliated Drum Tower Hospital of Nanjing University Medical School; Nanjing China
| | - Xue Liang
- Department of Radiology; Affiliated Drum Tower Hospital of Nanjing University Medical School; Nanjing China
| | - Xiaodong Chen
- Center for Biomedical Imaging Research, Department of Biomedical Engineering; Tsinghua University; Beijing China
- Department of Radiology; Affiliated Hospital of Guangdong Medical College; Guangdong China
| | - Renyuan Liu
- Department of Radiology; Affiliated Drum Tower Hospital of Nanjing University Medical School; Nanjing China
| | - Xin Zhang
- Department of Radiology; Affiliated Drum Tower Hospital of Nanjing University Medical School; Nanjing China
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering; Tsinghua University; Beijing China
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46
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Improved multi-shot diffusion imaging using GRAPPA with a compact kernel. Neuroimage 2016; 138:88-99. [DOI: 10.1016/j.neuroimage.2016.05.079] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Revised: 05/29/2016] [Accepted: 05/30/2016] [Indexed: 11/20/2022] Open
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Joint correction of Nyquist artifact and minuscule motion-induced aliasing artifact in interleaved diffusion weighted EPI data using a composite two-dimensional phase correction procedure. Magn Reson Imaging 2016; 34:974-9. [PMID: 27114342 DOI: 10.1016/j.mri.2016.04.017] [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] [Received: 12/15/2015] [Revised: 04/06/2016] [Accepted: 04/17/2016] [Indexed: 11/22/2022]
Abstract
Diffusion-weighted imaging (DWI) obtained with interleaved echo-planar imaging (EPI) pulse sequence has great potential of characterizing brain tissue properties at high spatial-resolution. However, interleaved EPI based DWI data may be corrupted by various types of aliasing artifacts. First, inconsistencies in k-space data obtained with opposite readout gradient polarities result in Nyquist artifact, which is usually reduced with 1D phase correction in post-processing. When there exist eddy current cross terms (e.g., in oblique-plane EPI), 2D phase correction is needed to effectively reduce Nyquist artifact. Second, minuscule motion induced phase inconsistencies in interleaved DWI scans result in image-domain aliasing artifact, which can be removed with reconstruction procedures that take shot-to-shot phase variations into consideration. In existing interleaved DWI reconstruction procedures, Nyquist artifact and minuscule motion-induced aliasing artifact are typically removed subsequently in two stages. Although the two-stage phase correction generally performs well for non-oblique plane EPI data obtained from well-calibrated system, the residual artifacts may still be pronounced in oblique-plane EPI data or when there exist eddy current cross terms. To address this challenge, here we report a new composite 2D phase correction procedure, which effective removes Nyquist artifact and minuscule motion induced aliasing artifact jointly in a single step. Our experimental results demonstrate that the new 2D phase correction method can much more effectively reduce artifacts in interleaved EPI based DWI data as compared with the existing two-stage artifact correction procedures. The new method robustly enables high-resolution DWI, and should prove highly valuable for clinical uses and research studies of DWI.
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48
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Dai E, Ma X, Zhang Z, Yuan C, Guo H. Simultaneous multislice accelerated interleaved EPI DWI using generalized blipped-CAIPI acquisition and 3D K-space reconstruction. Magn Reson Med 2016; 77:1593-1605. [DOI: 10.1002/mrm.26249] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2015] [Revised: 03/08/2016] [Accepted: 03/29/2016] [Indexed: 12/22/2022]
Affiliation(s)
- Erpeng Dai
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University; Beijing China
| | - Xiaodong Ma
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University; Beijing China
| | - Zhe Zhang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University; Beijing China
| | - Chun Yuan
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University; Beijing China
- Department of Radiology; University of Washington; Seattle WA USA
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University; Beijing China
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49
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Liao C, Chen Y, Cao X, Chen S, He H, Mani M, Jacob M, Magnotta V, Zhong J. Efficient parallel reconstruction for high resolution multishot spiral diffusion data with low rank constraint. Magn Reson Med 2016; 77:1359-1366. [PMID: 26968846 DOI: 10.1002/mrm.26199] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Revised: 01/25/2016] [Accepted: 02/12/2016] [Indexed: 01/18/2023]
Abstract
PURPOSE To propose a novel reconstruction method using parallel imaging with low rank constraint to accelerate high resolution multishot spiral diffusion imaging. THEORY AND METHODS The undersampled high resolution diffusion data were reconstructed based on a low rank (LR) constraint using similarities between the data of different interleaves from a multishot spiral acquisition. The self-navigated phase compensation using the low resolution phase data in the center of k-space was applied to correct shot-to-shot phase variations induced by motion artifacts. The low rank reconstruction was combined with sensitivity encoding (SENSE) for further acceleration. The efficiency of the proposed joint reconstruction framework, dubbed LR-SENSE, was evaluated through error quantifications and compared with ℓ1 regularized compressed sensing method and conventional iterative SENSE method using the same datasets. RESULTS It was shown that with a same acceleration factor, the proposed LR-SENSE method had the smallest normalized sum-of-squares errors among all the compared methods in all diffusion weighted images and DTI-derived index maps, when evaluated with different acceleration factors (R = 2, 3, 4) and for all the acquired diffusion directions. CONCLUSION Robust high resolution diffusion weighted image can be efficiently reconstructed from highly undersampled multishot spiral data with the proposed LR-SENSE method. Magn Reson Med 77:1359-1366, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Congyu Liao
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ying Chen
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaozhi Cao
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Song Chen
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Merry Mani
- Department of Psychiatry, University of Iowa, Iowa City, Iowa, USA
| | - Mathews Jacob
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA
| | - Vincent Magnotta
- Department of Radiology, University of Iowa, Iowa City, Iowa, USA
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China.,Center for Innovative and Collaborative Detection and Treatment of Infectious Diseases, Zhejiang University, Hangzhou, Zhejiang, China
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50
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Chang HC, Sundman M, Petit L, Guhaniyogi S, Chu ML, Petty C, Song AW, Chen NK. Human brain diffusion tensor imaging at submillimeter isotropic resolution on a 3Tesla clinical MRI scanner. Neuroimage 2015; 118:667-75. [PMID: 26072250 DOI: 10.1016/j.neuroimage.2015.06.016] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2014] [Revised: 05/28/2015] [Accepted: 06/04/2015] [Indexed: 12/26/2022] Open
Abstract
The advantages of high-resolution diffusion tensor imaging (DTI) have been demonstrated in a recent post-mortem human brain study (Miller et al., NeuroImage 2011;57(1):167-181), showing that white matter fiber tracts can be much more accurately detected in data at a submillimeter isotropic resolution. To our knowledge, in vivo human brain DTI at a submillimeter isotropic resolution has not been routinely achieved yet because of the difficulty in simultaneously achieving high resolution and high signal-to-noise ratio (SNR) in DTI scans. Here we report a 3D multi-slab interleaved EPI acquisition integrated with multiplexed sensitivity encoded (MUSE) reconstruction, to achieve high-quality, high-SNR and submillimeter isotropic resolution (0.85×0.85×0.85mm(3)) in vivo human brain DTI on a 3Tesla clinical MRI scanner. In agreement with the previously reported post-mortem human brain DTI study, our in vivo data show that the structural connectivity networks of human brains can be mapped more accurately and completely with high-resolution DTI as compared with conventional DTI (e.g., 2×2×2mm(3)).
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Affiliation(s)
- Hing-Chiu Chang
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Mark Sundman
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Laurent Petit
- Groupe d'Imagerie Neurofonctionnelle (GIN) - UMR5296, CNRS, CEA, Université de Bordeaux, Bordeaux, France
| | - Shayan Guhaniyogi
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Mei-Lan Chu
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Christopher Petty
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Allen W Song
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Nan-kuei Chen
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA.
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