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Abstract #1873

SPGRE-to-bSSFP Sequence-Mapping with U-Net and GAN for Data Homogenization improves Comparability of PREFUL MRI

Andreas Voskrebenzev1,2, Jonah Hahn1,2, Maximilian Zubke1,2, Filip Klimeš1,2, Marius Wernz1,2, Robin Müller1,2, Frank Wacker1,2, and Jens Vogel-Claussen1,2
1Institute for Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany, 2Biomedical Research in End-stage and Obstructive Lung Disease Hannover (BREATH), Member of the German Centre for Lung Research (DZL), Hannover, Germany

Synopsis

Keywords: Lung, Lung, PREFUL, U-Net, GAN, Sequence

Motivation: Pulmonary proton MRI in free-breathing typically utilizes either spoiled or balanced steady-state free precession sequence types (SPGRE/bSSFP), which leads to sequence-dependent results variability.

Goal(s): Since deep learning networks are known to be able to translate between different image types, image-mapping from SPGRE to bSSFP is hypothesized to yield more similar results.

Approach: After training a U-Net, alone and part of a generative adversarial network pair (GAN), similarity of results was assessed, including SSIM, MSE, image sharpness and perfusion defect percentage.

Results: Results showed less image sharpness and were significantly more similar after mapping. Both network architectures performed on par.

Impact: As sequence homogenization is limited by vendor standards and hardware-limits the demonstrated sequence-mapping approach via deep learning is viable alternative. It could be specifically used to decrease the variability of perfusion-weighted maps acquired with bSSFP and SPGRE in multicenter settings.

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