Abstract #4258

Deep learning based synthetic CT skull for transcranial MRgFUS interventions using 3D V-net–Transfer learning implications

Pan Su^1,2, Sijia Guo^2,3, Steven Roys^2,3, Florian Maier⁴, Thomas Benkert⁴, Himanshu Bhat¹, Elias R. Melhem², Dheeraj Gandhi², Rao P. Gullapalli^2,3, and Jiachen Zhuo^2,3

¹Siemens Medical Solutions USA, Inc., Malvern, PA, United States, ²Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, School of Medicine, Baltimore, MD, United States, ³Center for Metabolic Imaging and Therapeutics (CMIT), University of Maryland Medical Center, Baltimore, MD, United States, ⁴Siemens Healthcare GmbH, Erlangen, Germany

Transcranial MRI-guided focused ultrasound (tcMRgFUS) is a promising technique for treating multiple diseases. It is desirable to simplify the clinical workflow of tcMRgFUS treatment planning. Previously, feasibility of leveraging deep learning to generate synthetic CT skull from ultra-short echo time (UTE) MRI has been demonstrated for tcMRgFUS planning. In this study, 3D V-Net was used for skull estimation, by taking advantage of 3D volumetric images. Furthermore, feasibility of applying pre-trained model in new dataset was studied, demonstrating the possibility of generalization across various sequences/protocols and scanners.

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