AI-Enhanced Super-Resolution for Metabolite MRI Imaging

Motivation: Metabolite images from Magnetic Resonance Spectroscopic Imaging (MRSI) suffer from lower quality and reduced detail due to larger voxel sizes compared to anatomical MRI. Goal(s): To improve the visual quality of MRSI by using a deep learning-based super-resolution approach to enhance spatial resolution. Approach: Synthetic metabolic maps were generated using anatomical images from 350 patients. Our CNN-transformer model was trained on 70% of the dataset and tested on the remaining 30%, with performance compared to spline and nearest-neighbor methods. Results: Our model significantly upscaled MRSI resolution to 128×128, achieving significantly higher PSNR, SSIM, and LPIPS scores than spline and nearest-neighbor (p<.01). Impact: This improved SR approach significantly enhances metabolite map quality, offering clinicians a valuable tool for detailed neurological assessment.