Real2Sim is becoming increasingly important with the rapid development of surgical artificial intelligence (AI) and autonomy. In this work, we propose a novel Real2Sim methodology, Instrument-Splatting, that leverages 3D Gaussian Splatting to provide fully controllable 3D reconstruction of surgical instruments from monocular surgical videos. To maintain both high visual fidelity and manipulability, we introduce a geometry pre-training to bind Gaussian point clouds on part mesh with accurate geometric priors and define a forward kinematics to control the Gaussians as flexible as real instruments. Afterward, to handle unposed videos, we design a novel instrument pose tracking method leveraging semantics-embedded Gaussians to robustly refine per-frame instrument poses and joint states in a render-and-compare manner, which allows our instrument Gaussian to accurately learn textures and reach photorealistic rendering. We validated our method on 2 publicly released surgical videos and 4 videos collected on ex vivo tissues and green screens. Quantitative and qualitative evaluations demonstrate the effectiveness and superiority of the proposed method.

According to [2], 11.6% of men will develop prostate cancer in their lifetime, with approximately a 20% death rate in the United States. Radical prostatectomy is a popular surgical approach to treat PCa by removing the entire prostate gland since 1905 [3,4]. In clinical practice, the traditional open radical prostatectomy (ORP) has almost been replaced by laparoscopic radical prostatectomy (RLP) [5]. As a minimally invasive surgical procedure for PCa, RLP significantly reduces blood loss, hospitalization duration, and postoperative complications [6]. However, the long learning curve associated with laparoscopic procedures limits the application of RLP [7]. Robot-assisted laparoscopic prostatectomy (RALP) has been demonstrated [5] to shorten this learning curve by leveraging the wristed instruments and the 3-D endoscopic camera of the telerobotic surgical system, usually the da Vinci surgical system, to achieve intuitive operation [8]. However, the endoscopic camera cannot localize the prostate lesions nor visualize the sub-surface anatomy of the prostate gland. Therefore, a complementary medical imaging modality is necessary to facilitate RALP.

Purpose: To achieve effective robot-assisted laparoscopic prostatectomy, the integration of transrectal ultrasound (TRUS) imaging system which is the most widely used imaging modelity in prostate imaging is essential. However, manual manipulation of the ultrasound transducer during the procedure will significantly interfere with the surgery. Therefore, we propose an image co-registration algorithm based on a photoacoustic marker method, where the ultrasound / photoacoustic (US/PA) images can be registered to the endoscopic camera images to ultimately enable the TRUS transducer to automatically track the surgical instrument Methods: An optimization-based algorithm is proposed to co-register the images from the two different imaging modalities. The principles of light propagation and an uncertainty in PM detection were assumed in this algorithm to improve the stability and accuracy of the algorithm. The algorithm is validated using the previously developed US/PA image-guided system with a da Vinci surgical robot. Results: The target-registration-error (TRE) is measured to evaluate the proposed algorithm. In both simulation and experimental demonstration, the proposed algorithm achieved a sub-centimeter accuracy which is acceptable in practical clinics. The result is also comparable with our previous approach, and the proposed method can be implemented with a normal white light stereo camera and doesn't require highly accurate localization of the PM. Conclusion: The proposed frame registration algorithm enabled a simple yet efficient integration of commercial US/PA imaging system into laparoscopic surgical setting by leveraging the characteristic properties of acoustic wave propagation and laser excitation, contributing to automated US/PA image-guided surgical intervention applications.