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Abstract #4297

Leveraging transfer learning for the super-resolution reconstruction of QSM with limited data for the study of the cerebrovasculature

Stefano Zappalà1, Eleonora Patitucci2, Ian Driver3, Daniel Gallichan4, Richard Wise5, and Michael Germuska6
1CUBRIC - School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom, 2CUBRIC - School of Psychology, Cardiff University, Cardiff, United Kingdom, 3CUBRIC - School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom, 4CUBRIC - School of Engineering, Cardiff University, Cardiff, United Kingdom, 5Department of Neurosciences, Imaging and Clinical Sciences, University "G. D'Annunzio" of Chieti-Pescara, CHIETI, Italy, 6Department of Radiology, University of California Davis Medical Center, Sacramento, CA, United States

Synopsis

Keywords: Analysis/Processing, Vessels

Motivation: Lengthy acquisitions are needed to produce quantitative susceptibility mapping (QSM) containing enough details to reliably identify the vascular network and capture accurate values of oxygen saturation.

Goal(s): To fine-tune a deep learning model for single image super resolution reconstruction o f QSM maps from 1mm to 0.5mm resolution.

Approach: Transfer learning was applied on a previously trained 3D densely-connected super resolution network (DCSRN) model, and the vascular network was segmented from the reconstructed susceptibility maps.

Results: Transfer learning applied on a DCSRN model demonstrated substantial improvements in the reconstruction of small vessels and reduced partial volume effects.

Impact: By demonstrating the effectiveness of transfer learning with a 3D Densely Connected Super-Resolution Network (DCSRN) model, this study provides a practical approach for researchers to improve the resolution of their own QSM data, even with limited resources.

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Keywords