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Abstract #5223

Unsupervised Deep Learning Framework for Accelerated LGE MRI Acquisition with Enhanced Image Reconstruction

Md Hasibul Husain Hisham1, Shireen Elhabian1, Ganesh Adluru2, Andrew Arai2, Eugene Kholmovski2, Ravi Ranjan2, and Edward Dibella2
1Kahlert School of Computing, University of Utah, Salt Lake City, UT, United States, 2Radiology & Imaging Sciences, University of Utah, Salt Lake City, UT, United States

Synopsis

Keywords: Image Reconstruction, Cardiovascular

Motivation: 3D Late Gadolinium Enhancement (LGE) MRI of the left atrium often has variable quality and can be a very long acquisition.

Goal(s): Lowering the acquisition time of LGE MRI scan, without sacrificing the final reconstruction quality, and while obtaining a high resolution isotropic 1.25mm dataset.

Approach: We used a variable density acquisition and reconstructed the first ~3 minutes of data of the study with a Self-guided Deep Image Prior Network. Reconstructions were compared to a Compressed Sensing iterative reconstruction of a four times longer scan.

Results: The CNN-based Deep Image Prior Network produced clinically diagnostic quality reconstruction with 1/4th of the acquisition time.

Impact: The network enables high-quality cardiac LGE imaging in 2-4 minutes, representing a significant advancement in clinical workflow efficiency and patient comfort. The reduced acquisition time could expand the accessibility of 3D LGE studies of atrial fibrillation patients.

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Keywords