Accelerated EPR imaging using deep learning denoising.

Journal: Magnetic resonance in medicine

PMID: 40096518

Abstract

PURPOSE: Trityl OXO71-based pulse electron paramagnetic resonance imaging (EPRI) is an excellent technique to obtain partial pressure of oxygen (pO) maps in tissues. In this study, we used deep learning techniques to denoise 3D EPR amplitude and pO maps.

Authors

Irene Canavesi

Oxygen Measurement Core, O2M Technologies, LLC, Chicago, Illinois, USA.
Navin Viswakarma

Oxygen Measurement Core, O2M Technologies, LLC, Chicago, Illinois, USA.
Boris Epel

Oxygen Measurement Core, O2M Technologies, LLC, Chicago, Illinois, USA.
Alan McMillan

Department of Medical Physics, University of Wisconsin at Madison, Madison, WI, USA; Department of Radiology, University of Wisconsin at Madison, Madison, WI, USA; Deparment of Biomedical Engineering, University of Wisconsin at Madison, Madison, WI, USA.
Mrignayani Kotecha

Oxygen Measurement Core, O2M Technologies, LLC, Chicago, Illinois, USA.

Keywords

Algorithms Animals Deep Learning Electron Spin Resonance Spectroscopy Fibrosarcoma Humans Image Processing, Computer-Assisted Imaging, Three-Dimensional Mice Oxygen Phantoms, Imaging Signal-To-Noise Ratio

External Resources

View on PubMed Access via DOI PubMed (40096518)

Accelerated EPR imaging using deep learning denoising.

Abstract

Authors

Keywords

External Resources

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