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Motion-immune neuro and body MRI for challenging patient populations

$196,250R21FY2014EBNIH

Duke University, Durham NC

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Abstract

DESCRIPTION (provided by applicant): 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.

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