Abstract #0226

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

Derk J. Slotman^1,2, Lambertus W. Bartels², Aylene Zijlstra¹, Inez M. Verpalen¹, Jochen A.C. van Osch¹, Ingrid M. Nijholt¹, Edwin Heijman^3,4, 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

Synopsis

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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