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Transcranial phase aberration correction using beam simulations and MR-ARFI.

ABSTRACT: Transcranial magnetic resonance-guided focused ultrasound surgery is a noninvasive technique for causing selective tissue necrosis. Variations in density, thickness, and shape of the skull cause aberrations in the location and shape of the focal zone. In this paper, the authors propose a hybrid simulation-MR-ARFI technique to achieve aberration correction for transcranial MR-guided focused ultrasound surgery. The technique uses ultrasound beam propagation simulations with MR Acoustic Radiation Force Imaging (MR-ARFI) to correct skull-caused phase aberrations.Skull-based numerical aberrations were obtained from a MR-guided focused ultrasound patient treatment and were added to all elements of the InSightec conformal bone focused ultrasound surgery transducer during transmission. In the first experiment, the 1024 aberrations derived from a human skull were condensed into 16 aberrations by averaging over the transducer area of 64 elements. In the second experiment, all 1024 aberrations were applied to the transducer. The aberrated MR-ARFI images were used in the hybrid simulation-MR-ARFI technique to find 16 estimated aberrations. These estimated aberrations were subtracted from the original aberrations to result in the corrected images. Each aberration experiment (16-aberration and 1024-aberration) was repeated three times.The corrected MR-ARFI image was compared to the aberrated image and the ideal image (image with zero aberrations) for each experiment. The hybrid simulation-MR-ARFI technique resulted in an average increase in focal MR-ARFI phase of 44% for the 16-aberration case and 52% for the 1024-aberration case, and recovered 83% and 39% of the ideal MR-ARFI phase for the 16-aberrations and 1024-aberration case, respectively.Using one MR-ARFI image and noa priori information about the applied phase aberrations, the hybrid simulation-MR-ARFI technique improved the maximum MR-ARFI phase of the beam's focus.

SUBMITTER: Vyas U

PROVIDER: S-EPMC3978249 | biostudies-other | 2014 Mar

REPOSITORIES: biostudies-other

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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.

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Transcranial phase aberration correction using beam simulations and MR-ARFI.

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