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Abstract #0226

Diffusion-weighted MRI with deep learning for visualizing treatment results of MR-guided HIFU ablation of uterine fibroids.

Derk J. Slotman1,2, Lambertus W. Bartels2, Aylene Zijlstra1, Inez M. Verpalen1, Jochen A.C. van Osch1, Ingrid M. Nijholt1, Edwin Heijman3,4, Miranda van 't Veer-ten Kate1, Erwin de Boer1, Rolf D. van den Hoed1, Martijn Froeling2, and Martijn F. Boomsma1
1Radiology, Isala Zwolle, Zwolle, Netherlands, 2Image Sciences Institute, Imaging & Oncology Division, University Medical Center Utrecht, Utrecht, Netherlands, 3Faculty of Medicine and University Hospital of Cologne, Institute of Diagnostic and Interventional Radiology, University of Cologne, Cologne, Germany, 4High Tech Campus, Philips Research Eindhoven, Eindhoven, Netherlands


The non-perfused volume (NPV) cannot be assessed repeatedly with contrast-enhanced imaging during MR-HIFU ablations of uterine fibroids, due to contrast agent dose constraints and safety concerns. In this study, synthetic contrast-enhanced (CE)-T1w scans were generated from diffusion weighted imaging (DWI) using deep learning-based image-to-image translation. A significant linear association was found between the NPV-ratios based on synthetic and paired reference CE-T1w scans (r=0.80, p<0.001). Radiologists agreed in 83% on treatment success based on synthetic and reference CE-T1w scans. This indicates that translation of DWI into synthetic CE-T1w scans has potential as method for gadolinium-free imaging of the NPV.

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