Assessment of Multi-modal MR Imaging for Glioma Based on a Deep Learning Reconstruction Approach with the Denoising Method

Deep learning reconstruction (DLR) approach with denoising can improve image quality of magnetic resonance (MR) images. However, its applications on multi-modal glioma imaging have not been assessed. Multi-modal images of 107 glioma patients were evaluated by signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge sharpness, visual assessment, diagnosis accuracy and efficiency. Contrasted with conventionally reconstructed images, the DLR images showed higher tumor/residual tumor SNR, higher tumor to white/gray matter CNR, better results of the visual assessment, and a trend of improved diagnosis efficiency and comparable accuracy. DLR can improve image quality of multi-modal glioma images which should benefit the glioma diagnosis.