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Transcranial MRI-guided focused ultrasound (TcMRgFUS) is a therapeuticultrasound method that focuses sound through the skull to a small regionnoninvasively under MRI guidance. It is clinically approved to thermally ablateregions of the thalamus and is being explored for other therapies, such asblood brain barrier opening and neuromodulation. To accurately targetultrasound through the skull, the transmitted waves must constructivelyinterfere at the target region. However, heterogeneity of the sound speed,density, and ultrasound attenuation in different individuals' skulls requirespatient-specific estimates of these parameters for optimal treatment planning.CT imaging is currently the gold standard for estimating acoustic properties ofan individual skull during clinical procedures, but CT imaging exposes patientsto radiation and increases the overall number of imaging procedures requiredfor therapy. A method to estimate acoustic parameters in the skull without theneed for CT would be desirable. Here, we synthesized CT images from routinelyacquired T1-weighted MRI by using a 3D patch-based conditional generativeadversarial network and evaluated the performance of synthesized CT images fortreatment planning with transcranial focused ultrasound. We compared theperformance of synthetic CT to real CT images using Kranion and k-Wave acousticsimulation. Our work demonstrates the feasibility of replacing real CT with theMR-synthesized CT for TcMRgFUS planning.

Synthetic CT Skull Generation for Transcranial MR Imaging-Guided Focused Ultrasound Interventions with Conditional Adversarial Networks