Dataset Information

A random phased-array for MR-guided transcranial ultrasound neuromodulation in non-human primates.

ABSTRACT: Transcranial focused ultrasound (FUS) is a non-invasive technique for therapy and study of brain neural activation. Here we report on the design and characterization of a new MR-guided FUS transducer for neuromodulation in non-human primates at 650?kHz. The array is randomized with 128 elements 6.6?mm in diameter, radius of curvature 7.2?cm, opening diameter 10.3?cm (focal ratio 0.7), and 46% coverage. Simulations were used to optimize transducer geometry with respect to focus size, grating lobes, and directivity. Focus size and grating lobes during electronic steering were quantified using hydrophone measurements in water and a three-axis stage. A novel combination of optical tracking and acoustic mapping enabled measurement of the 3D pressure distribution in the cortical region of an ex vivo skull to within ~3.5?mm of the surface, and allowed accurate modelling of the experiment via non-homogeneous 3D acoustic simulations. The data demonstrates acoustic focusing beyond the skull bone, with the focus slightly broadened and shifted proximal to the skull. The fabricated design is capable of targeting regions within the S1 sensorimotor cortex of macaques.

SUBMITTER: Chaplin V

PROVIDER: S-EPMC6941739 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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A random phased-array for MR-guided transcranial ultrasound neuromodulation in non-human primates.

Chaplin Vandiver V Phipps Marshal A MA Caskey Charles F CF

Physics in medicine and biology 20180517 10

Transcranial focused ultrasound (FUS) is a non-invasive technique for therapy and study of brain neural activation. Here we report on the design and characterization of a new MR-guided FUS transducer for neuromodulation in non-human primates at 650 kHz. The array is randomized with 128 elements 6.6 mm in diameter, radius of curvature 7.2 cm, opening diameter 10.3 cm (focal ratio 0.7), and 46% coverage. Simulations were used to optimize transducer geometry with respect to focus size, grating lobe ...[more]

PMID: 29667598

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Project description:Background and purposeTranscranial MR imaging-guided focused ultrasound is a promising novel technique to treat multiple disorders and diseases. Planning for transcranial MR imaging-guided focused ultrasound requires both a CT scan for skull density estimation and treatment-planning simulation and an MR imaging for target identification. It is desirable to simplify the clinical workflow of transcranial MR imaging-guided focused ultrasound treatment planning. The purpose of this study was to examine the feasibility of deep learning techniques to convert MR imaging ultrashort TE images directly to synthetic CT of the skull images for use in transcranial MR imaging-guided focused ultrasound treatment planning.Materials and methodsThe U-Net neural network was trained and tested on data obtained from 41 subjects (mean age, 66.4 ± 11.0 years; 15 women). The derived neural network model was evaluated using a k-fold cross-validation method. Derived acoustic properties were verified by comparing the whole skull-density ratio from deep learning synthesized CT of the skull with the reference CT of the skull. In addition, acoustic and temperature simulations were performed using the deep learning CT to predict the target temperature rise during transcranial MR imaging-guided focused ultrasound.ResultsThe derived deep learning model generates synthetic CT of the skull images that are highly comparable with the true CT of the skull images. Their intensities in Hounsfield units have a spatial correlation coefficient of 0.80 ± 0.08, a mean absolute error of 104.57 ± 21.33 HU, and a subject-wise correlation coefficient of 0.91. Furthermore, deep learning CT of the skull is reliable in the skull-density ratio estimation (r = 0.96). A simulation study showed that both the peak target temperatures and temperature distribution from deep learning CT are comparable with those of the reference CT.ConclusionsThe deep learning method can be used to simplify workflow associated with transcranial MR imaging-guided focused ultrasound.

Dataset Information

A random phased-array for MR-guided transcranial ultrasound neuromodulation in non-human primates.

Publications

A random phased-array for MR-guided transcranial ultrasound neuromodulation in non-human primates.

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