Improved DCE-MRI for Diffuse Gliomas: Clinical Application of Deep Learning-based Super-resolution and Denoising Algorithm

Motivation: Dynamic contrast-enhanced MRI (DCE-MRI) is invaluable for non-invasive assessment of tissue perfusion and microcirculation dynamics. However, unreliability of DCE-MRI discourages clinical application. Goal(s): To evaluate the image quality and diagnostic performance of enhanced DCE-MRI using a deep learning-based super-resolution and denoising algorithm. Approach: Deep learning-based super-resolution and denoising (DLSD) algorithm was applied to DCE-MRI obtained from 306 patients with adult-type diffuse gliomas to reduce noise and increase resolution. Results: DLSD significantly enhanced image quality without compromising diagnostic accuracy in distinguishing low- and high-grade tumors and IDH mutation, and it also improved the reliability of arterial input functions. Impact: Improving DCE-MRI image quality and reliability through deep learning-based super-resolution and denoising algorithm can help address previous reliability issues and offer clinical applicability not only in the field of diffuse glioma but also in other areas utilizing DCE-MRI.