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Abstract #3103

Transformer-based fast image reconstruction for high fidelity MR knee imaging: an evaluation study

Yajing Zhang1, Zhixin Xu2, and Jin Qi2
1GE Healthcare, Beijing, China, 2University of Electronic Science and Technology of China, Chengdu, China

Synopsis

Keywords: Joint, AI/ML Image Reconstruction

Motivation: Address the challenges of prolonged MRI reconstruction, especially in knee imaging, to enhance clinical efficiency.

Goal(s): Develop an efficient knee MRI reconstruction framework utilizing the transformer-based deep learning approach to improve image quality allowing reduced scan time.

Approach: Employ the transformer-based framework to reconstruct under-sampled knee MRI data, leveraging transformer-based deep learning for improved image fidelity.

Results: Demonstrated enhanced image quality and reduced artifacts compared to conventional methods, showcasing the efficacy in knee imaging reconstruction.

Impact: This transformer-based method offers a promising solution for faster MRI reconstruction, potentially improving clinical efficiency and outcomes in musculoskeletal imaging.

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Keywords