Abstract #1811

Addressing Bias and Precision in Low SNR Chemical Shift Encoded MRI Proton Density Fat Fraction Estimation using a Deep Learning Reconstruction

Nathan T Roberts, PhD¹, Nikolaos Panagiotopoulos, MD², Ty Cashen, MD, PhD¹, Daiki Tamada, PhD², Diego Hernando, PhD^2,3,4,5, Arnaud Guidon, PhD¹, and Scott B Reeder, MD, PhD^2,3,4,6,7

¹GE HealthCare, Waukesha, WI, United States, ²Radiology, University of Wisconsin - Madison, Madison, WI, United States, ³Medical Physics, University of Wisconsin - Madison, Madison, WI, United States, ⁴Biomedical Engineering, University of Wisconsin - Madison, Madison, WI, United States, ⁵Electrical and Computer Engineering, University of Wisconsin - Madison, Madison, WI, United States, ⁶Medicine, University of Wisconsin - Madison, Madison, WI, United States, ⁷Emergency Medicine, University of Wisconsin - Madison, Madison, WI, United States

Synopsis

Keywords: Quantitative Imaging, Machine Learning/Artificial Intelligence

Low bias and high precision are important for accurate diagnosis, staging, and treatment monitoring of chronic liver disease using chemical shift-encoded (CSE)-MRI. However, CSE-MRI proton density fat fraction (PDFF) measurements are often biased by an asymmetric noise distribution present in PDFF maps acquired with low/moderate signal-to-noise ratio (SNR). This work investigates the use of deep learning de-noising to mitigate this bias in phantoms and in vivo. Results demonstrate that deep learning reconstruction removes or reduces noise-related PDFF estimation bias while maintaining the expected noise distribution characteristic of PDFF.

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.