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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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Wu D, Kang L, Li H, Ba R, Cao Z, Liu Q, Tan Y, Zhang Q, Li B, Yuan J. Developing an AI-empowered head-only ultra-high-performance gradient MRI system for high spatiotemporal neuroimaging. Neuroimage 2024; 290:120553. [PMID: 38403092 DOI: 10.1016/j.neuroimage.2024.120553] [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: 07/03/2023] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 02/27/2024] Open
Abstract
Recent advances in neuroscience requires high-resolution MRI to decipher the structural and functional details of the brain. Developing a high-performance gradient system is an ongoing effort in the field to facilitate high spatial and temporal encoding. Here, we proposed a head-only gradient system NeuroFrontier, dedicated for neuroimaging with an ultra-high gradient strength of 650 mT/m and 600 T/m/s. The proposed system features in 1) ultra-high power of 7MW achieved by running two gradient power amplifiers using a novel paralleling method; 2) a force/torque balanced gradient coil design with a two-step mechanical structure that allows high-efficiency and flexible optimization of the peripheral nerve stimulation; 3) a high-density integrated RF system that is miniaturized and customized for the head-only system; 4) an AI-empowered compressed sensing technique that enables ultra-fast acquisition of high-resolution images and AI-based acceleration in q-t space for diffusion MRI (dMRI); and 5) a prospective head motion correction technique that effectively corrects motion artifacts in real-time with 3D optical tracking. We demonstrated the potential advantages of the proposed system in imaging resolution, speed, and signal-to-noise ratio for 3D structural MRI (sMRI), functional MRI (fMRI) and dMRI in neuroscience applications of submillimeter layer-specific fMRI and dMRI. We also illustrated the unique strength of this system for dMRI-based microstructural mapping, e.g., enhanced lesion contrast at short diffusion-times or high b-values, and improved estimation accuracy for cellular microstructures using diffusion-time-dependent dMRI or for neurite microstructures using q-space approaches.
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Affiliation(s)
- Dan Wu
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China; Innovation Center for Smart Medical Technologies & Devices, Binjiang Institute of Zhejiang University, Hangzhou, China.
| | - Liyi Kang
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China; Innovation Center for Smart Medical Technologies & Devices, Binjiang Institute of Zhejiang University, Hangzhou, China
| | - Haotian Li
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Ruicheng Ba
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Zuozhen Cao
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Qian Liu
- United Imaging Healthcare Co., Ltd, Shanghai, China
| | - Yingchao Tan
- United Imaging Healthcare Co., Ltd, Shanghai, China
| | - Qinwei Zhang
- Beijing United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - Bo Li
- United Imaging Healthcare Co., Ltd, Shanghai, China
| | - Jianmin Yuan
- United Imaging Healthcare Co., Ltd, Shanghai, China
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Zeng T, Lu Y, Jiang W, Zheng J, Zhang J, Gravel P, Wan Q, Fontaine K, Mulnix T, Jiang Y, Yang Z, Revilla EM, Naganawa M, Toyonaga T, Henry S, Zhang X, Cao T, Hu L, Carson RE. Markerless head motion tracking and event-by-event correction in brain PET. Phys Med Biol 2023; 68:245019. [PMID: 37983915 PMCID: PMC10713921 DOI: 10.1088/1361-6560/ad0e37] [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: 09/02/2023] [Revised: 10/29/2023] [Accepted: 11/20/2023] [Indexed: 11/22/2023]
Abstract
Objective.Head motion correction (MC) is an essential process in brain positron emission tomography (PET) imaging. We have used the Polaris Vicra, an optical hardware-based motion tracking (HMT) device, for PET head MC. However, this requires attachment of a marker to the subject's head. Markerless HMT (MLMT) methods are more convenient for clinical translation than HMT with external markers. In this study, we validated the United Imaging Healthcare motion tracking (UMT) MLMT system using phantom and human point source studies, and tested its effectiveness on eight18F-FPEB and four11C-LSN3172176 human studies, with frame-based region of interest (ROI) analysis. We also proposed an evaluation metric, registration quality (RQ), and compared it to a data-driven evaluation method, motion-corrected centroid-of-distribution (MCCOD).Approach.UMT utilized a stereovision camera with infrared structured light to capture the subject's real-time 3D facial surface. Each point cloud, acquired at up to 30 Hz, was registered to the reference cloud using a rigid-body iterative closest point registration algorithm.Main results.In the phantom point source study, UMT exhibited superior reconstruction results than the Vicra with higher spatial resolution (0.35 ± 0.27 mm) and smaller residual displacements (0.12 ± 0.10 mm). In the human point source study, UMT achieved comparable performance as Vicra on spatial resolution with lower noise. Moreover, UMT achieved comparable ROI values as Vicra for all the human studies, with negligible mean standard uptake value differences, while no MC results showed significant negative bias. TheRQevaluation metric demonstrated the effectiveness of UMT and yielded comparable results to MCCOD.Significance.We performed an initial validation of a commercial MLMT system against the Vicra. Generally, UMT achieved comparable motion-tracking results in all studies and the effectiveness of UMT-based MC was demonstrated.
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Affiliation(s)
- Tianyi Zeng
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America
| | - Yihuan Lu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America
- United Imaging Healthcare, Houston, TX, United States of America
| | - Weize Jiang
- United Imaging Healthcare, Houston, TX, United States of America
| | - Jiaxu Zheng
- United Imaging Healthcare, Houston, TX, United States of America
| | - Jiazhen Zhang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America
| | - Paul Gravel
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America
| | - Qianqian Wan
- United Imaging Healthcare, Houston, TX, United States of America
| | - Kathryn Fontaine
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America
| | - Tim Mulnix
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America
| | - Yulin Jiang
- United Imaging Healthcare, Houston, TX, United States of America
| | - Zhaohui Yang
- United Imaging Healthcare, Houston, TX, United States of America
| | - Enette Mae Revilla
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America
| | - Mika Naganawa
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America
| | - Takuya Toyonaga
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America
| | - Shannan Henry
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America
| | - Xinyue Zhang
- United Imaging Healthcare, Houston, TX, United States of America
| | - Tuoyu Cao
- United Imaging Healthcare, Houston, TX, United States of America
| | - Lingzhi Hu
- United Imaging Healthcare, Houston, TX, United States of America
| | - Richard E Carson
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America
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