Self-Supervised Weighted Image Guided Quantitative MRI Super-Resolution

Object: To present and evaluate Self-supervised Weighted Image Guided quantitative MRI Super-Resolution (SWIG qMRI SR), a physics-informed framework recovering high-resolution (HR) qMRI from a rapid low-resolution (LR) acquisition guided by routine weighted images (wMRI), without HR training targets. Materials and Methods: A CNN matches acquired wMRI to images synthesized from predicted maps through forward signal models, while anchoring those maps to the acquired LR qMRI. Training and ablation used synthetic data (n = 27); cross-sequence generalizability was tested in three volunteers scanned with silent 3D MuPa-ZTE at 1 and 5min, with clinical T1w/T2w guides. Results: In synthetic data, dual-guide SR reduced the T1 high-frequency error norm 45.4% below baseline; removing the LR qMRI anchor raised gray-matter T1 error from 63 to 110ms. In vivo, super-resolved 1-min maps synthesized T1w images (SSIM 0.93, PSNR 27.3dB) surpassing synthesis from 5-min maps (SSIM 0.83), and improved synthesis of T2-FLAIR, never used as a guide (SSIM 0.69 to 0.75). Discussion: HR detail was recovered without HR supervision, and the network transferred across a different qMRI sequence. Because the guides are already acquired, adding a 1-min qMRI scan to a standard exam offers a practical route to routine clinical qMRI integration.