Synthetic CT Skull Generation for Transcranial MR Imaging-Guided Focused Ultrasound Interventions with Conditional Adversarial Networks
This addresses the problem of reducing radiation exposure and imaging procedures for patients undergoing TcMRgFUS therapy, though it is incremental as it builds on existing generative methods.
The study tackled the need for CT imaging to estimate skull acoustic properties for transcranial MRI-guided focused ultrasound interventions by synthesizing CT images from T1-weighted MRI using a 3D patch-based conditional generative adversarial network, demonstrating feasibility for treatment planning.
Transcranial MRI-guided focused ultrasound (TcMRgFUS) is a therapeutic ultrasound method that focuses sound through the skull to a small region noninvasively under MRI guidance. It is clinically approved to thermally ablate regions of the thalamus and is being explored for other therapies, such as blood brain barrier opening and neuromodulation. To accurately target ultrasound through the skull, the transmitted waves must constructively interfere at the target region. However, heterogeneity of the sound speed, density, and ultrasound attenuation in different individuals' skulls requires patient-specific estimates of these parameters for optimal treatment planning. CT imaging is currently the gold standard for estimating acoustic properties of an individual skull during clinical procedures, but CT imaging exposes patients to radiation and increases the overall number of imaging procedures required for therapy. A method to estimate acoustic parameters in the skull without the need for CT would be desirable. Here, we synthesized CT images from routinely acquired T1-weighted MRI by using a 3D patch-based conditional generative adversarial network and evaluated the performance of synthesized CT images for treatment planning with transcranial focused ultrasound. We compared the performance of synthetic CT to real CT images using Kranion and k-Wave acoustic simulation. Our work demonstrates the feasibility of replacing real CT with the MR-synthesized CT for TcMRgFUS planning.