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Abstract #1269

A neural network approach for estimating muscle perfusion from DCE-MRI data

Christopher C Conlin1, Xiaowan Li1, Stephen Decker2, Christopher J Hanrahan1, Gwenael Layec2, Nan Hu3, Vivian S Lee4, and Jeff L Zhang1

1Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, United States, 2School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, United States, 3Division of Biostatistics, Department of Internal Medicine, University of Utah, Salt Lake City, UT, United States, 4Verily Life Sciences, Cambridge, MA, United States

Perfusion is an important aspect of calf muscle function that can be measured with dynamic contrast-enhanced (DCE) MRI. However, conventional methods for quantifying perfusion from DCE-MRI data require an appropriate tracer-kinetic model, which may not be available clinically. In this study, we examined the feasibility of neural networks (NNs) for quantifying calf-muscle perfusion from DCE-MRI data. We found that NNs estimate perfusion with accuracy comparable to conventional methods, without the need for a tracer-kinetic model. NNs like those developed in this study can be readily incorporated into ordinary MRI scanner software, facilitating routine quantitative perfusion analysis with DCE-MRI.

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