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 both of 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.     «

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 both of these problems, by proposing a novel efficient ultrasound simulation method based on convolutional ray-tracing which directly takes volumetri...     »