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Abstract #1297

A practical application of generative models for MR image synthesis: from post- to pre-contrast imaging   

Gian Franco Piredda1,2,3, Virginie Piskin1, Vincent Dunet2, Gibran Manasseh2, Mário J Fartaria1,2,3, Till Huelnhagen1,2,3, Jean-Philippe Thiran2,3, Tobias Kober1,2,3, and Ricardo Corredor-Jerez1,2,3
1Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 2Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 3LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Multiple sclerosis studies following the widely accepted MAGNIMS protocol guidelines might lack non-contrast-enhanced T1-weighted acquisitions as they are only considered optional. Most existing automated tools to perform morphological brain analyses are, however, tuned to non-contrast T1-weighted images. This work investigates the use of deep learning architectures for the generation of pre-Gadolinium from post-Gadolinium image volumes. Two generative models were tested for this purpose. Both were found to yield similar contrast information as the original non-contrast T1-weighted images. Quantitative comparison using an automated brain segmentation on original and synthesized non-contrast T1-weighted images showed good correlation (r=0.99) and low bias (<0.7 ml).

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