Deep Learning-based Super-Resolution Reconstruction for Brain Diffusion-weighted MRI

Motivation: A deep learning-constrained algorithm has been integrated into MRI data acquisition and image reconstruction processes, encompassing compressed sensing, image denoising, and resolution upscaling techniques. Nonetheless, limited prospective studies are available that evaluate the application of this algorithm for brain diffusion-weighted imaging. Goal(s): The primary objective of this study was to compare the recently developed deep learning-constrained algorithm with conventional compressed sensing reconstruction. Approach: This study comprehensively assessed images, both qualitatively and quantitatively, employing rigorous methodologies and analytical tools. Results: The results demonstrated that the newly developed deep learning-constrained algorithm significantly enhanced image sharpness while maintaining signal-to-noise ratio, thus advantaging clinical diagnosis. Impact: Deep learning-constrained super-resolution reconstruction leads to a significant increase in image sharpness of brain DWI, which holds potential to improve clinical diagnosis of diseases, such as stroke and tumors.