Abstract
The simulation of medical ultrasound from patient-specific data may improve the planning and execution of interventions e.g. in the field of neurosurgery. However, both the long computation times and the limited realism due to lack of acoustic information from tomographic scans prevent a wide adoption of such a simulation. In this work, we address these problems by proposing a novel efficient ultrasound simulation method based on convolutional ray-tracing which directly takes volumetric image data as input. We show how the required acoustic simulation parameters can be derived from a segmented MRI scan of the patient. We also propose an automatic optimization of ultrasonic simulation parameters and tissue-specific acoustic properties from matching ultrasound and MRI scan data. Both qualitative and quantitative evaluation on a database of 14 neurosurgical patients demonstrate the potential of our approach for clinical use.
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Keywords
- Acoustic Parameter
- Medical Ultrasound
- Bhattacharyya Distance
- Ultrasound Simulation
- Time Gain Compensation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Salehi, M., Ahmadi, SA., Prevost, R., Navab, N., Wein, W. (2015). Patient-specific 3D Ultrasound Simulation Based on Convolutional Ray-tracing and Appearance Optimization. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9350. Springer, Cham. https://doi.org/10.1007/978-3-319-24571-3_61
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DOI: https://doi.org/10.1007/978-3-319-24571-3_61
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