  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$\pm$8.1%) and smallest with Kranion
phases (7.3$\pm$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
