Abstract
Background: Diffusion-weighted imaging (DWI) is an important MRI technique for studying tissue microstructure and structural connectivity, but is limited by relatively low spatial resolution, signal-to-noise ratio (SNR), and sensitivity to inter-acquisition motion. A deep-learning framework was developed that jointly performs image registration and super-resolution on repeated low-resolution DWI inputs to generate a single registered, higher apparent spatial-resolution output. Methods: DeepSUM-DWI adapts a DeepSUM multi-image super-resolution architecture for repeated DWI. It comprises a shared 2D super-resolution feature extractor (SISRNet), a registration network (RegNet), and a 3D fusion network (FusionNet). Evaluation used (1) simulated repetitions with synthetic motion, (2) retrospectively downsampled acquisitions from 12 subjects, and (3) a prospective dual-resolution example with matched 1.0 × 1.0 and 2.0 × 2.0 mm² acquisitions. Results: DeepSUM-DWI reconstructed a single high-resolution-appearing DWI image from multiple low-resolution repetitions while preserving structural characteristics of the high-resolution acquisitions. The model remained robust across synthetic motion magnitudes. In retrospectively downsampled data, it improved NRMSE by 71–79% and PSNR by 10.6–13.5 dB, for super-resolution factors of 2–3, relative to averaging interpolated low-resolution images, with a mean SSIM > 0.99. In prospectively acquired data, it recovered finer structures than visible in the acquired low-resolution images while maintaining contrast consistency. Exploratory diffusion analysis suggested that some gains in apparent detail propagated to FA maps. Conclusions: DeepSUM-DWI provides a framework for joint registration and super-resolution of repeated DWI acquisitions, with potential to improve apparent spatial resolution while retaining the SNR benefits of repeated acquisition.