Novel Toolbox Generates CT Images from MR for Transcranial Ultrasound Stimulation Studies

A novel toolbox has been created to generate computed tomography (CT) images from T1-weighted magnetic resonance (MR) images for use in planning transcranial ultrasound stimulation (TUS) studies in humans.

This validated open-source toolbox allows for the creation of bespoke skull models for acoustic simulations in TUS studies using existing T1-weighted MR images for neuronavigation. The toolbox uses a 3D residual U-Net, pre-trained on 110 subjects and refined on a separate dataset of 37, to synthesize a 100 keV pseudo-CT image from a T1-weighted MR image. This toolbox fills the gap in existing methods and helps promote best practices and reproducibility in TUS. The toolbox is implemented in MONAI, an open-source deep-learning framework for medical imaging.

Key Takeaways

• The toolbox generates computed tomography (CT) images from T1-weighted magnetic resonance (MR) images for transcranial ultrasound stimulation (TUS) studies.
• It is aimed at researchers to generate individualized skull models for acoustic simulations in TUS studies.
• CT images are considered the gold standard for skull imaging, but obtaining CT images can be difficult due to radiation exposure and limited access to CT scanners.
• The toolbox uses a pre-trained 3D residual U-Net to synthesize a pseudo-CT image from T1-weighted MR image.
• The network was trained and validated on multiple datasets to increase its generalizability.
• The toolbox and accompanying code for transcranial simulations are open-source and have already been used by several TUS researchers worldwide.
• The toolbox is designed to be useful to both novice and expert users and can be used for transfer learning applications on their own datasets.

Find out more information on the open-source tool here