Abstract #4437

Self-navigated motion correction in multi-contrast intracranial vascular imaging using 3D radial trajectory

Xiaoqian Chao¹, Xiaodong Ma², Kaiyu Zhang³, Niranjan Balu³, Lu Han⁴, Peng Wu⁴, He Wang^1,4, and Zhensen Chen^1,5

¹Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China, ²Department of Radiology And Imaging Sciences, University of Utah, Salt Lake City, UT, United States, ³Vascular Imaging Lab, Department of Radiology, University of Washington, Seattle, WA, United States, ⁴Philips Healthcare, Shanghai, China, ⁵Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China

Synopsis

Keywords: Motion Correction, Motion Correction, 3D radial trajectory,Self-navigated,Blood vessels

Motivation: iSNAP is a time-efficient multi-contrast intracranial vascular imaging sequence that uses 3D radial acquisition trajectory. Motion, commonly occurs in cerebrovascular patients, decreases quality of iSNAP images.

Goal(s): To develop a motion correction approach for iSNAP sequence.

Approach: Self-navigated motion correction, in which a series of high temporal-resoluaiton images were reconstructed for motion estimation, was implemented by taking advantage of radial trajectory. Performance of the approach was assessed with simulation and in-vivo experiments.

Results: The proposed approach allows detection and correction of large abrupt rotations and translations in steady states.

Impact: The proposed retrospective motion correction approach improves image quality of iSNAP, and enhances its clinical value.

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