Abstract #2136

Application of Deep Learning Reconstruction for Denoising of Compressed Sensing non-contrast coronary MRA images to achieve improved Diagnostic Confidence.

Yoko Kato¹, Bharath Ambale-Venkatesh², Yoshimori Kassai³, John Pitts⁴, Larry Kasuboski⁴, Jason Ortman¹, Shelton Caruthers⁴, and Joao A.C. Lima¹

¹Cardiology, Johns Hopkins University, Baltimore, MD, United States, ²Radiology, Johns Hopkins University, Baltimore, MD, United States, ³Canon Medical Systems Corporation, Otawara, Japan, ⁴Canon Medical Research USA, Inc., Cleveland, OH, United States

Non-contrast Magnetic resonance coronary artery (MRCA) image acquisition has technical limitations of long acquisition time or reduced image resolution. We explore the use of a denoising approach with deep learning image reconstruction (dDLR) from k-space data. We investigate the effect of various levels of dDLR on Compressed Sensing non-contrast MRCA (CS-MRCA) images and optimize dDLR algorithms that achieve the best diagnostic confidence (DC) and a high signal-to-noise-ratio (SNR).

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.