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Abstract #3831

Self-supervised multi-instance contrastive learning for reduction of cardiac bSSFP off-resonance artifacts

Zhuo Chen1, Yixin Emu1, Juan Gao1, Haiyang Chen1, Xin Tang2, and Chenxi Hu1
1Shanghai Jiao Tong University, Shanghai, China, 2United Imaging Healthcare Co., Ltd, Shanghai, China

Synopsis

Keywords: Analysis/Processing, Artifacts, Contrastive learning, Cardiac function, Cine, off-resonance artifacts

Motivation: Off-resonance artifacts in bSSFP cine images preclude the assessment of cardiac function. Acquiring corrupted-clean image pairs for supervised methods is challenging.

Goal(s): To develop a self-supervised adversarial framework to reduce off-resonance artifacts without corrupted-clean image pairs.

Approach: We employ multi-instance contrastive learning during the generator’s training to enforce point-wise consistency between outputs of artifact-affected bSSFP images with altered RF phase increments. Additionally, we design a rectangle-window spatial-temporal transformer for longer-range dependencies without increasing computational complexity.

Results: Experiments on ACDC and in-house datasets show that our method outperforms existing unpaired self-supervised approaches and generates comparable results against several fully-supervised models.

Impact: Obtaining corrupted-clean bSSFP image pairs is challenging, particularly with cardiac devices or high-field MR. Our proposed self-supervised approach mitigates off-resonance artifacts and provides a practical solution for reliable bSSFP cine imaging.

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Keywords