Enhanced Real-Time iCMR Device Visualization Using GRAPPA and DnCNN Denoising at Low Field

Motivation: It is challenging to achieve the spatial and temporal resolutions required for real-time interventional device visualization using low-field scanners with limited gradient performance. Goal(s): The objective of this study is to develop a novel post-processing method capable of delivering high temporal and spatial resolution images in real-time. Approach: We developed a method that sequentially combines GRAPPA with a DnCNN denoising network to provide highly accelerated acquisition with low-latency reconstruction. The network was trained using the OCMR dataset and evaluated using pre-clinical data. Results: The network effectively suppressed noise with minimal latency, while preserving the original features of the image. Impact: This method could enhance the speed and quality of real-time interventional imaging at low field, making it easier for the interventionalist to visually track devices and deploy stents and other devices.