2023
DOI: 10.1016/j.brs.2023.01.838
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Pseudo-CTs from T1-weighted MRI for planning of low-intensity transcranial focused ultrasound neuromodulation: An open-source tool

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Cited by 21 publications
(21 citation statements)
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“…Next, we performed transcranial simulations for the dACC and PCC for each participant in the study. We estimated the skull for each participant from a pseudo-CT derived from the participant's T1-weighted MRI using a deep learning method 36,37 . The skull was obtained from pseudo-CT images by thresholding at 300 HU and clamping values above 2000 HU.…”
Section: Acoustic Simulationsmentioning
confidence: 99%
See 1 more Smart Citation
“…Next, we performed transcranial simulations for the dACC and PCC for each participant in the study. We estimated the skull for each participant from a pseudo-CT derived from the participant's T1-weighted MRI using a deep learning method 36,37 . The skull was obtained from pseudo-CT images by thresholding at 300 HU and clamping values above 2000 HU.…”
Section: Acoustic Simulationsmentioning
confidence: 99%
“…We set our simulation grid size to the size of the T1-weighted MRI with a grid spacing of 1 mm. Our acoustic simulation methods are described in further detail elsewhere 40 and the code is available online 41 .…”
Section: Acoustic Simulationsmentioning
confidence: 99%
“…However, the usage of CT scans is often constrained due to concerns about ionizing radiation exposure. To address this challenge, we used a toolbox developed by Yaakub, White, Kerfoot et al (2023). This toolbox uses deep learning convolutional neural networks (CNNs) to generate pseudo-CT images from anatomical T1-weighted MRI images, thereby aiding the planning process for TUS.…”
Section: Structural Mri and Pseudo-ctmentioning
confidence: 99%
“…We visually inspected the pseudo-CTs by comparing them with their corresponding T1-weighted images and identified no irregularities. It is important to mention that the T1 MR protocol employed in our study differed slightly from the one used in the initial development of the pseudo-CT algorithm by Yaakub, White., Kerfoot et al (2023). However, according to Yaakub, White, Roberts et al (2023), such variations in the T1 MR protocol do not significantly affect the fidelity of converting MRI to pseudo-CT images.…”
Section: Tus Simulationmentioning
confidence: 99%
“…A method based on normalizing the ZTE signal has the advantage of requiring training. However, as ML methods continue to improve [11,26,40,41], there is a potential of reducing the need for training data from CT scans for studies with limited access to them.…”
Section: Current Limitations Of Zte-based Processingmentioning
confidence: 99%