Towards 3D-Denser Ultrasound Image Simulation from 2D CT-Scan for Ultrasound-Guided Percutaneous Nephrolithotomy Training

Virtual reality (VR) simulation can improve the outcomes of percutaneous nephrolithotomy (PNCL) - a surgery to extract kidney stones using ultrasound (US) or fluoroscopy image guidance. These simulators almost exclusively employ fluoroscopy, and no commercial VR simulator is available for US-guided PNCL (usPCNL). In this paper, we proposed the first step towards developing an usPCNL simulator that integrates a volumetric US model of the patient's anatomy derived from parallel 2D computed tomography (CT) scans. A critical challenge in US image generation from CT scans is that the limited spatial resolution of CT slices may lead to inaccuracies in the simulated US images. The proposed algorithm interpolates successive CT images to create an augmented dataset with increased spatial resolution. Each CT slice is then converted into a US image based on principles of linear acoustics and spatial impulse response. These images are then combined to form two different volumetric US images, one derived from the original sparse CT scans, and one created with the augmented data. From these volumetric US images, new images can be formed along arbitrary imaging planes not captured in the original CT data. The obtained simulated images are compared with their corresponding real US images acquired experimentally, and further evaluated quantitatively using normalized root mean square error (NRMSE) and dice similarity coefficient (DSC). The results reveal an NRMSE of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{0.235}\pm \mathbf{0.051}$</tex> and a DSC of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{0.9139}\pm \mathbf{0.062}$</tex>, showcasing a close resemblance between simulated and actual ultrasound images. Additionally, we show that denser CT scan data leads to a 25% improvement in image quality based on peak signal-to-noise ratio compared to the original dataset. This initial work is laying the foundation for the development of the usPCNL simulator, which could potentially have significant benefits for training and enhancing skills in this medical procedure.