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Abstract #3828

1H-Guided Unsupervised Super-Resolution Reconstruction of 23Na in Simulcast X-nuclei MRI

Pengcheng Cheng1, Chunsheng Yang1,2, Chuanxing Wu1, Kai Wang1, Lili Yang1, Lingzhi Hu3, Jianmin Yuan3, and Xilin Sun1
1NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC) of Harbin Medical University, Harbin, China, 2Zhongyuan University of Technology, Zhengzhou, China, 3United Imaging Healthcare, Shanghai, China

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Unsupervised, Super-Resolution Reconstruction

Motivation: Simulcast multi-nuclei MRI, when acquiring images from multiple nuclei simultaneously, face challenges with non-proton nuclei displaying lower resolution compared to proton nuclei. Conventional methods enhance resolution via k-space zero-padding and cropping, albeit with detail loss.

Goal(s): To enhance the resolution of non-proton nuclear images in Simulcast X-nuclei MRI without compromising texture details.

Approach: We proposed a deep learning-based method for unsupervised super-resolution reconstruction of X-nuclei images, guided by 1H.

Results: Our method enhance the resolution of non-proton nuclear images while preserving texture details as much as possible.

Impact: Our deep learning approach markedly improves non-proton nuclei image quality in simulcast multi-nuclei MRI, advancing rigorous scientific research in simulcast multi-nuclei MRI, especially in settings with limited access to advanced multi-nuclei simultaneous imaging technologies.

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Keywords