Adaptive Weighted-Rosette Trajectories Based on Sparse Models and Nuclear Norm Regularization for Fast MRI Restoration
Authors: 
Wen Xu, Qiaoyu Tian, Qiuyi Xu, JunMei Liao, Jin XuAuthors Info & Claims
Wen Xu, Qiaoyu Tian, Qiuyi Xu, JunMei Liao, Jin XuAuthors Info & ClaimsPages 53 - 61
Abstract
Non-Cartesian k-space MRI trajectories are faster and more stable in motion than Cartesian trajectories. However, MRI reconstruction is limited by their sampling speed and various artifacts, resulting in slow and prone to blur problems. To enhance MRI reconstruction speed and quality by generalizing the adaptive weighted rosette trajectories for fast, under-sampled k-space data acquisition and minimizing off-resonance artifacts. In this paper, we propose modifications to adaptive rosette trajectory sampling rates and employ adaptive total variations and nuclear norm methods to eliminate off-resonance artifacts, facilitating faster MRI reconstruction. The adaptive weighted rosette trajectory, coupled with the ATVNN algorithm, demonstrated superior performance in reducing acquisition time, improving off-resonance behavior, and minimizing blur in MRI reconstructions. The ATVNN algorithm significantly enhances MRI reconstruction efficiency and quality, making it a promising alternative to CT scans for applications like monitoring COVID-19 pneumonia, with reduced exposure to ionizing radiation yields outstanding performance.
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- Adaptive Weighted-Rosette Trajectories Based on Sparse Models and Nuclear Norm Regularization for Fast MRI Restoration
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November 2024
131 pages
ISBN:9798400709586
DOI:10.1145/3705927
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Published: 22 January 2025
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DMIP '24
DMIP '24: 2024 7th International Conference on Digital Medicine and Image Processing
November 8 - 11, 2024
Osaka, Japan
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