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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