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Abstract #1869

Dynamic imaging of the heart from scattering parameters using deep learning – an MR based feasibility study

E.F. Meliado1,2,3, C.A. Louka2, C.A.T. van den Berg2,4, and B.R. Steensma1,2
1Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands, 2Computational Imaging Group for MR diagnostics & therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands, 3Tesla Dynamic Coils BV, Zaltbommel, Netherlands, 4Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands

Synopsis

Keywords: Image Reconstruction, Image Reconstruction, Cardiac motion, RF Arrays & Systems,Machine Learning/Artificial Intelligence

Motivation: To enable more accessible and less costly monitoring of cardiovascular mechanical function.

Goal(s): Perform a feasibility study into the potential of imaging the heart based on scattering parameters of an RF antenna array

Approach: An MRI inspired reconstruction network was trained based on 150 in silico simulations of MRI segmented heart models. The method predicts 2D-maps of dielectric property changes and was tested in silico and in vivo on a healthy control.

Results: In silico validation shows that it is feasible to reconstruct the shape and size of the heart, as well as left and right ventricular volumes, based on RF scattering measurements.

Impact: This work shows the feasibility of imaging the heart from differential scattering parameter measurements and by using MRI inspired reconstruction. Preliminary results warrant further investigation into acquiring paired MRI and RF scattering measurements in human subjects.

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Keywords