Despite the increasing adoption of other imaging modalities, ultrasound (US) guidance is widely used for surgical procedures and clinical imaging due to its low cost, non-invasiveness, real-time visual feedback. Many US guided procedures require extensive training and where possible training on simulations should be preferred over patients. We are researching techniques to accurately simulate ultrasound images at interactive rates from sliced Computed Tomography (CT) volumes (and other data sources) using the computational power of the modern Graphics Processing Unit (GPU).
Computational resources for existing approaches to US simulation are usually limited by real-time requirements. Unlike previous approaches we simulate freehand US images from 3D CT data on the Graphics Processing Unit (GPU). We build upon the method proposed by Wein et al for estimating US reflection properties of tissue and modify it to a computationally more efficient form. In addition to previous approaches, we also estimate US absorption properties from CT data. Using NVIDIA’s Compute Unified Device Architecture (CUDA), we provide a physically plausible simulation of US reflection, shadowing artefacts, speckle noise and radial blurring. All parameters of the simulated probe are interactively configurable at run-time, including US frequency and intensity as well as field geometry.
Applications for this technology lie primarily in the training of US for clinical use. As the input data can be varied, a multitude of training scenarios are feasible including training transrectal guided ultrasound biopsy and the use of ultrasound in emergency medicine.