Meeting Banner
Abstract #3837

Accelerated EPR Imaging Using Deep Learning Denoising

Irene Canavesi1, Navin Viswakarma1, Boris Epel1,2, Alan McMillan3, and Mrignayani Kotecha1
1O2M Technologies, LLC, Chicago, IL, United States, 2Radiation and Cellular Oncology, University of Chicago, Chicago, IL, United States, 3Department of Radiology, University of Wisconsin-Madison, Madison, WI 53792, USA, Madison, WI, United States

Synopsis

Keywords: Analysis/Processing, Oxygenation, EPRI, Hypoxia imaging

Motivation: Pulse EPR imaging (EPRI) is an excellent method to obtain pO2 maps in tissues, however, suffers from low signal-to-noise ratio (SNR) and image artifacts.

Goal(s): In this project, our goal was to implement deep learning-based techniques to improve SNR and reduce artifacts.

Approach: All EPRI experiments were performed using a 25 mT EPRI instrument, JIVA-25Ò. A UNet model, combined with Joint Bilateral Filters (JBF), was tested.

Results: We demonstrate that UNet with 2-filter JBF provided the best outcome. Results showed that the model enabled a 10-fold faster acquisition. We demonstrate that the trained algorithm improves SNR in pO2 maps of mouse fibrosarcoma and kidneys.

Impact: EPR images with physically enhanced deep learning techniques improve image SNR and reduce artifacts. This advancement can be translated to reduce acquisition time, reduce deposited power, and enable large object oxygen imaging, bringing EPRI one step closer to clinical translation.

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.

Keywords