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

Abstract

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 (cGAN) and evaluated the performance of synthesized CT images for treatment planning with transcranial focused ultrasound. A dataset of 86 paired CT and T1-weighted MR images were randomly split so that 66 images were used for training, 10 for validation and parameter tuning, and 10 for acoustic testing. We compared the performance of synthetic CT (sCT) to real CT (rCT) images using an open-source treatment planning software, Kranion, and found that the number of active elements, skull density ratio, and skull thickness between rCT and sCT had Pearson's Correlation Coefficients of 0.989, 0.915, and 0.941, respectively, suggesting strong positive linear correlation. Of a total of 990 elements 95.7 ± 1.4% of active and inactive elements overlapped between rCTs and sCTs. Simulations using the acoustic toolbox, k-Wave, resulted in 23.5 ± 6.51% less maximum root-mean-squared (RMS) pressure simulated with sCTs than the corresponding rCT pressure. An average focal shift of 0.96 ± 0.56 mm and 1.07 ± 0.58 mm was observed between the thalamus target and the maximum RMS pressure location in rCTs and sCTs, respectively. Our work demonstrates the feasibility of replacing real CT with the MR-synthesized CT for TcMRgFUS planning.