  Model-based treatment planning for transcranial ultrasound therapy typically
involves mapping the acoustic properties of the skull from an x-ray computed
tomography (CT) image of the head. Here, three methods for generating pseudo-CT
images from magnetic resonance (MR) images were compared as an alternative to
CT. A convolutional neural network (U-Net) was trained on paired MR-CT images
to generate pseudo-CT images from either T1-weighted or zero-echo time (ZTE) MR
images (denoted tCT and zCT, respectively). A direct mapping from ZTE to
pseudo-CT was also implemented (denoted cCT). When comparing the pseudo-CT and
ground truth CT images for the test set, the mean absolute error was 133, 83,
and 145 Hounsfield units (HU) across the whole head, and 398, 222, and 336 HU
within the skull for the tCT, zCT, and cCT images, respectively. Ultrasound
simulations were also performed using the generated pseudo-CT images and
compared to simulations based on CT. An annular array transducer was used
targeting the visual or motor cortex. The mean differences in the simulated
focal pressure, focal position, and focal volume were 9.9%, 1.5 mm, and 15.1%
for simulations based on the tCT images, 5.7%, 0.6 mm, and 5.7% for the zCT,
and 6.7%, 0.9 mm, and 12.1% for the cCT. The improved results for images mapped
from ZTE highlight the advantage of using imaging sequences which improve
contrast of the skull bone. Overall, these results demonstrate that acoustic
simulations based on MR images can give comparable accuracy to those based on
CT.

Medical Physics;Machine Learning