Super-resolution neural networks improve the spatiotemporal resolution of adaptive MRI-guided radiation therapy.

ABSTRACT:

Background

Magnetic resonance imaging (MRI) offers superb non-invasive, soft tissue imaging of the human body. However, extensive data sampling requirements severely restrict the spatiotemporal resolution achievable with MRI. This limits the modality's utility in real-time guidance applications, particularly for the rapidly growing MRI-guided radiation therapy approach to cancer treatment. Recent advances in artificial intelligence (AI) could reduce the trade-off between the spatial and the temporal resolution of MRI, thus increasing the clinical utility of the imaging modality.

Methods

We trained deep learning-based super-resolution neural networks to increase the spatial resolution of real-time MRI. We developed a framework to integrate neural networks directly onto a 1.0 

SUBMITTER: Grover J 

PROVIDER: S-EPMC10994938 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature