Deep learning super-resolution magnetic resonance spectroscopic imaging of brain metabolism and mutant isocitrate dehydrogenase glioma.

ABSTRACT:

Background

Magnetic resonance spectroscopic imaging (MRSI) can be used in glioma patients to map the metabolic alterations associated with IDH1,2 mutations that are central criteria for glioma diagnosis. The aim of this study was to achieve super-resolution (SR) MRSI using deep learning to image tumor metabolism in patients with mutant IDH glioma.

Methods

We developed a deep learning method based on generative adversarial network (GAN) using Unet as generator network to upsample MRSI by a factor of 4. Neural networks were trained on simulated metabolic images from 75 glioma patients. The performance of deep neuronal networks was evaluated on MRSI data measured in 20 glioma patients and 10 healthy controls at 3T with a whole-brain 3D MRSI protocol optimized for detecti

SUBMITTER: Li X 

PROVIDER: S-EPMC9332900 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature