BACKGROUND: In computer- and robot-assisted orthopedic surgery (CAOS), patient-specific surgical plans are generated from preoperative medical imaging data to define target locations and implant trajectories. During surgery, these plans must be precisely transferred to the intraoperative setting to guide accurate execution. The accuracy and success of this transfer rely on cross-registration between preoperative and intraoperative data. However, the substantial heterogeneity across imaging modalities and devices renders this registration process challenging and error-prone, leading to inaccuracies. Consequently, more robust and accurate methods for automatic, modality-agnostic multimodal registration of bone surfaces would have a substantial clinical impact. METHODS: We propose NeuralBoneReg, an instance-specific self-supervised, surface-based framework for bone surface registration using 3D point clouds as an intermediate representation. NeuralBoneReg comprises two key components: an implicit neural unsigned distance field (UDF) module and a multilayer perceptron (MLP)-based registration module. The UDF module learns a neural representation of the preoperative bone model. The registration module solves both global initialization and local refinement by generating a set of transformation hypotheses to register the intraoperative point cloud with the preoperative neural UDF based on a coarse-to-fine strategy. Compared to state-of-the-art (SOTA) supervised registration, NeuralBoneReg operates in an instance-specific self-supervised manner, without requiring inter-subject training data with ground truth transformations. We evaluated NeuralBoneReg against baseline methods on two publicly available multi-modal datasets: a CT-ultrasound dataset of the fibula and tibia (UltraBones100k) and a CT-RGB-D dataset of spinal vertebrae (SpineDepth). The evaluation also includes a newly introduced CT-ultrasound dataset of cadaveric subjects containing femur and pelvis (UltraBonesHip), which will be made publicly available. RESULTS: Quantitative and qualitative results show that NeuralBoneReg achieves competitive performance across anatomies and modalities. On UltraBones100k, it obtains an RRE of 1.83±1.30°, an RTE of 2.02±1.30mm, an RR of 0.89, a CD of 0.82±0.12mm, and an HD95 of 2.06±0.36mm. On UltraBonesHip, it maintains stable performance with an RRE of 1.90±1.56°, an RTE of 2.21±0.86mm, an RR of 0.88, a CD of 2.50±1.08mm, and an HD95 of 8.97±4.08mm, while other methods degrade significantly. On SpineDepth, it achieves an RRE of 3.78±19.34°, an RTE of 2.80±3.75mm, an RR of 0.84, a CD of 1.78±1.61mm, and an HD95 of 4.26±4.58mm. Overall, the method consistently achieves accuracy close to pseudo ground truth across datasets. CONCLUSION: NeuralBoneReg achieves robust, accurate, and modality-agnostic registration of bone surfaces, offering a promising solution for reliable cross-modal alignment in computer- and robot-assisted orthopedic surgery.

Ultrasound (US) is a widely used medical imaging modality due to its real-time capabilities, non-invasive nature, and cost-effectiveness. By reducing operator dependency and enhancing access to complex anatomical regions, robotic ultrasound can help improve workflow efficiency. Recent studies have demonstrated the potential of deep reinforcement learning (DRL) and imitation learning (IL) to enable more autonomous and intelligent robotic ultrasound navigation. However, the application of learning-based robotic ultrasound to computer-assisted surgical tasks, such as anatomy reconstruction and surgical guidance, remains largely unexplored. A key bottleneck for this is the lack of realistic and efficient simulation environments tailored to these tasks. In this work, we present SonoGym, a scalable simulation platform for robotic ultrasound, enabling parallel simulation across tens to hundreds of environments. Our framework supports realistic and real-time simulation of US data from CT-derived 3D models of the anatomy through both a physics-based and a Generative Adversarial Network (GAN) approach. Our framework enables the training of DRL and recent IL agents (vision transformers and diffusion policies) for relevant tasks in robotic orthopedic surgery by integrating common robotic platforms and orthopedic end effectors. We further incorporate submodular DRL---a recent method that handles history-dependent rewards---for anatomy reconstruction and safe reinforcement learning for surgery. Our results demonstrate successful policy learning across a range of scenarios, while also highlighting the limitations of current methods in clinically relevant environments. We believe our simulation can facilitate research in robot learning approaches for such challenging robotic surgery applications. Dataset, codes and videos are publicly available at https://sonogym.github.io/.