Abstract <p>Ultrasound-guided renal access for percutaneous nephrolithotomy (usPCNL) is a common technique used to remove large kidney stones through an incision in the patient’s back. The procedure requires a high level of dexterity and is associated with a steep learning curve. Advanced simulation tools can enhance clinical training and provide interventionists with a platform to rehearse the operation with pre-operative patient data. This paper presents the first step towards the development of such a simulator. We propose a new framework and algorithms to generate volumetric ultrasound images from preoperative 2D computed tomography (CT). First, successive CT scans are interpolated to augment the dataset and increase spatial resolution. Each scan is then converted into an ultrasound image based on principles of linear acoustics and spatial impulse response. These ultrasound images are then combined to form two volumetric images, one derived from the original sparse CT scans, and one with the denser data. New images can then be formed along arbitrary imaging planes not captured in the original CT data. The obtained images are compared with real images acquired experimentally, and further evaluated quantitatively. We demonstrate that the peak signal-to-noise ratio (PSNR) in the simulated images shows a significant enhancement for the denser CT scan datasets, with an improvement of 34.8% for Dataset&#xa0;1 and 46.5% for Dataset&#xa0;2. The second part of the paper proposes a multi-level approach where kidney stones and kidney contours are segmented from CT scans and fused on top of ultrasound images. Four levels of assistance are proposed, ranging from full anatomical information, to no assistance with increasing levels of attenuation, increasing complexity and realism to build the trainee’s expertise progressively. This work lays the foundation for developing the first usPCNL simulator, which could enhance training and procedural outcomes in this complex medical procedure.</p> Graphical abstract <p></p>