Here we propose a plan to develop and integrate novel strategies to effectively eliminate motion-related artifact, which is the major bottleneck in achieving high-quality clinical MRI for challenging patient populations such as children, tremor-dominant Parkinson's patients and seriously ill patients among others. The impact of our project is expected to be immediate and significant across multiple clinical areas. For example, 1) for abdominal MR imaging, the proposed methods enable free-breathing data acquisition of high-quality and high- resolution MRI even when the respiratory frequency changes significantly over time; 2) for neuro MRI, our strategies make it possible to achieve high-quality imaging even when subjects have continual intra-scan head tremor (e.g., Parkinson's patients). The proposed motion-immune MRI strategies are novel, and superior to existing approaches in multiple ways. First, our motion artifact correction technique can more effectively address nonlinear and local motions (e.g., in free-breathing abdominal MRI), which have been limitations for existing methods that rely on either low-resolution navigator echo signals or external non-MRI based motion measurement (e.g., with infra-red sensors). Second, our method can address motion-related artifacts of different time scales (e.g., ranging from ~ 3 Hz head tremor to infrequent intra-scan movement), which may not always be corrected by methods that rely on real-time system updating (e.g., dynamically changing the slice location after 1 or 2 TRs). Third, our approach can be applied to both Cartesian and non-Cartesian imaging pulse sequences, and thus can be translated to various clinical applications more easily and quickly than methods only applicable to non-Cartesian imaging (e.g., radial-sampling; PROPELLER imaging). Fourth, our integrated technique can simultaneously and effectively address multiple forms of motion-related artifacts, ranging from phase errors in multi-shot diffusion-weighted imaging to signal loss due to large-scale position changes. Our motion-immune MRI is achieved through uniquely integrating and optimizing two novel approaches: 1) aliasing-artifact removal with multiplexed sensitivity encoding (MUSE), and 2) motion-immune structural MRI based on repeated k-t-subsampling and artifact-minimization (REKAM). We plan to build motion-immune MRI methods, and assess the developed methods in healthy volunteers and two challenging populations: un-sedated children and tremor-dominant Parkinson's patients.

Public Health Relevance

The proposed imaging techniques enable high-quality and motion-immune clinical MRI in challenging patient populations (e.g., children; seriously ill patients; tremor-dominant Parkinson's patients; patients with cognitive impairment; uncooperative subjects) without using risky sedation / anesthesia procedure, which is currently necessary for these challenging patients to complete conventional lengthy MRI examinations. In addition, the proposed high-performance clinical MRI, with significantly improved speed and motion tolerance, can play a transformative role in reducing the cost and increasing the accessibility of MRI, which in turn make a positive economic impact on our healthcare system.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21EB018419-02
Application #
8934098
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Liu, Guoying
Project Start
2014-09-25
Project End
2017-06-30
Budget Start
2015-07-01
Budget End
2017-06-30
Support Year
2
Fiscal Year
2015
Total Cost
Indirect Cost
Name
Duke University
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
044387793
City
Durham
State
NC
Country
United States
Zip Code
27705
Chu, Mei-Lan; Chang, Hing-Chiu; Chung, Hsiao-Wen et al. (2018) Free-breathing abdominal MRI improved by repeated k-t-subsampling and artifact-minimization (ReKAM). Med Phys 45:178-190
Chen, Nan-Kuei; Chang, Hing-Chiu; Bilgin, Ali et al. (2018) A diffusion-matched principal component analysis (DM-PCA) based two-channel denoising procedure for high-resolution diffusion-weighted MRI. PLoS One 13:e0195952
Guhaniyogi, Shayan; Chu, Mei-Lan; Chang, Hing-Chiu et al. (2016) Motion immune diffusion imaging using augmented MUSE for high-resolution multi-shot EPI. Magn Reson Med 75:639-52
Liu, Yilin; Yin, Fang-Fang; Chen, Nan-kuei et al. (2015) Four dimensional magnetic resonance imaging with retrospective k-space reordering: a feasibility study. Med Phys 42:534-41
Chu, Mei-Lan; Chang, Hing-Chiu; Chung, Hsiao-Wen et al. (2015) POCS-based reconstruction of multiplexed sensitivity encoded MRI (POCSMUSE): A general algorithm for reducing motion-related artifacts. Magn Reson Med 74:1336-48
Wu, Ming-Long; Chang, Hing-Chiu; Chao, Tzu-Cheng et al. (2015) Efficient imaging of midbrain nuclei using inverse double-echo steady-state acquisition. Med Phys 42:4367-74
Chang, Hing-Chiu; Sundman, Mark; Petit, Laurent et al. (2015) Human brain diffusion tensor imaging at submillimeter isotropic resolution on a 3Tesla clinical MRI scanner. Neuroimage 118:667-75