Papers › Evaluation of Synthetically Generated CT for use in Transcranial Focused Ultrasound Procedures
Evaluation of Synthetically Generated CT for use in Transcranial Focused Ultrasound Procedures
Han Liu, Michelle K. Sigona, Thomas J. Manuel, Li Min Chen, Benoit M. Dawant, Charles F. Caskey
Transcranial focused ultrasound (tFUS) is a therapeutic ultrasound method that focuses sound through the skull to a small region noninvasively and often under MRI guidance. CT imaging is used to estimate the acoustic properties that vary between individual skulls to enable effective focusing during tFUS procedures, exposing patients to potentially harmful radiation. 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 (sCT) images for treatment planning with tFUS. We compared the performance of sCT to real CT (rCT) images for tFUS planning using Kranion and simulations using the acoustic toolbox, k-Wave. Simulations were performed for 3 tFUS scenarios: 1) no aberration correction, 2) correction with phases calculated from Kranion, and 3) phase shifts calculated from time-reversal. From Kranion, skull density ratio, skull thickness, and number of active elements between rCT and sCT had Pearson's Correlation Coefficients of 0.94, 0.92, and 0.98, respectively. Among 20 targets, differences in simulated peak pressure between rCT and sCT were largest without phase correction (12.4±8.1%) and smallest with Kranion phases (7.3±6.0%). The distance between peak focal locations between rCT and sCT was less than 1.3 mm for all simulation cases. Real and synthetically generated skulls had comparable image similarity, skull measurements, and acoustic simulation metrics. Our work demonstrates the feasibility of replacing real CTs with the MR-synthesized CT for tFUS planning. Source code and a docker image with the trained model are available at https://github.com/han-liu/SynCT_TcMRgFUS
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