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Abstract #0462

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

Yixuan Liu1, Yu Ding1, Yingmin Liu1, Chong Chen1, Ning Jin2, Axel Joachim Krafft3, Florian Maier3, Aimee K. Armstrong4, Rizwan Ahmad1, and Orlando Paul Simonetti1
1The Ohio State University, Columbus, OH, United States, 2Siemens Medical Solutions USA, Inc., Columbus, OH, United States, 3Siemens Healthcare, Erlangen, Germany, 4Nationwide Childrens Hospital, Columbus, OH, United States

Synopsis

Keywords: MR-Guided Interventions, Low-Field MRI, Cardiovascular Intevention

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.

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Keywords