Miscouridou, Maria;
Pineda-Pardo, Jose A;
Stagg, Charlotte J;
Treeby, Bradley E;
Stanziola, Antonio;
(2022)
Classical and learned MR to pseudo-CT mappings for accurate transcranial ultrasound simulation.
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
, 46
(10)
pp. 2896-2905.
10.1109/TUFFC.2022.3198522.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Classical and learned MR to pseudo-CT mappings for accurate transcranial ultrasound simulation |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TUFFC.2022.3198522 |
| Publisher version: | https://doi.org/10.1109/TUFFC.2022.3198522 |
| Language: | English |
| Additional information: | This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) grant number EP/S026371/1 and the UKRI CDT in AI-enabled Healthcare Systems, grant number EP/S021612/1. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising from this submission. |
| Keywords: | deep learning, convolutional neural network, MRI, CT, pseudo-CT, transcranial ultrasound stimulation, acoustic simulation |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10154345 |
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