SELFIE: Self-Supervised Learning for Fast Dynamic Golden-Angle Radial MRI

Accelerated golden-angle radial acquisitions are widely used for dynamic MRI, but compressed sensing (CS)-based reconstruction presents residual artifacts at high acceleration and long computation times. Supervised deep learning (DL) enables fast reconstruction with improved image quality but usually relies on CS because fully-sampled references are unavailable. The proposed SELFIE (SElf-supervised Learning for Fast dynamIc golden-anglE radial MRI) technique enables self-supervised reconstruction that neither requires fully-sampled nor CS training references. SELFIE operates slice-by-slice in the image domain and thus avoids computationally expensive k-space data-consistency operations, allowing for a single forward-pass inference. Self-supervision is achieved by leveraging two properties of dynamic imaging with golden-angle radial sampling: (i) variable temporal resolution to form data-derived self-references at multiple temporal resolutions and (ii) temporal sparsity. SELFIE was evaluated on dynamic contrast-enhanced (DCE)-MRI in patients with gynecologic cancer and on motion-resolved free-breathing abdominal MRI in patients with liver cancer. Reconstructions were compared against CS and supervised DL using quantitative image-quality metrics and qualitative assessment from radiologists. Across both applications, SELFIE achieved image quality and dynamic fidelity (contrast enhancement and motion depiction) comparable to CS and supervised DL, while reconstructing 3D time series in seconds per case, substantially faster than CS. Ranking analysis and reader study favored SELFIE over CS and were comparable to supervised DL, with no statistically significant differences. Overall, SELFIE provides a fast, reference-free reconstruction framework for dynamic golden-angle radial MRI with competitive performance, representing a viable alternative to existing methods.