Report on a novel bone registration method: A rapid, accurate, and radiation-free technique for computer- and robotic-assisted orthopedic surgeries

A review of advances in image-guided orthopedic surgery

Orthopedic surgery remains technically demanding due to the complex anatomical structures and cumbersome surgical procedures. The introduction of image-guided orthopedic surgery (IGOS) has significantly decreased the surgical risk and improved the operation results. This review focuses on the application of recent advances in artificial intelligence (AI), deep learning (DL), augmented reality (AR) and robotics in image-guided spine surgery, joint arthroplasty, fracture reduction and bone tumor resection. For the pre-operative stage, key technologies of AI and DL based medical image segmentation, 3D visualization and surgical planning procedures are systematically reviewed. For the intra-operative stage, the development of novel image registration, surgical tool calibration and real-time navigation are reviewed. Furthermore, the combination of the surgical navigation system with AR and robotic technology is also discussed. Finally, the current issues and prospects of the IGOS system are discussed, with the goal of establishing a reference and providing guidance for surgeons, engineers, and researchers involved in the research and development of this area.

A novel bone registration method using impression molding and structured-light 3D scanning technology

Accurate bone registration is critical for computer navigation and robotic surgery. Existing registration systems are expensive, cumbersome, limited in accuracy and/or require intraoperative radiation. We recently reported a novel method of registration utilizing an inexpensive, compact, and X-ray-free structured-light 3D scanner. However, this technique is not always practical in a real surgical setting where soft tissue and blood can obstruct the continuous line-of-sight required for structured-light technology. We sought to remedy these limitations using a novel technique using rapid-setting impression molding to capture bone surface features and scan the undersurface of the mold with a structured-light scanner. The photonegative of this mold is compared to the preoperative computed tomography (CT)-scan to register the bone. A registration accuracy study was conducted on 36 CT-scanned femur sawbones, simulating typical exposure in hip/knee arthroplasty and bone tumor surgery. A cadaver experiment was also conducted to evaluate the feasibility of using the impression molding in a more realistic operating room setting. The registration accuracy of the proposed technique was 0.50 ± 0.19 mm. This was close to the reported accuracy of 0.43 ± 0.18 mm using a structured-light scanner without impression molding (p = 0.085). In comparison, historical values for "paired-point" and intraoperative CT image-based registration methods currently used in modern robotic/computer-navigation systems were 0.68 ± 0.14 mm (p = 0.004) and 0.86 ± 0.38 mm, respectively. The registration accuracy of the cadaver experiment was consistent with that of sawbone experiments. Although future studies are needed to extend to human subjects, this study shows that the impression molding method can produce comparable or better registration accuracy than the existing techniques.

A Novel 3D Light Assisted Drawing (3D-LAD) Method to Aid Intraoperative Reproduction of Osteotomy Lines Surrounding a Bone Tumor During Wide Resection: An Experimental Study

Introduction

Computer navigation and customized 3D-printed jigs improve accuracy during bone tumor resection, but such technologies can be bulky, costly, and require intraoperative radiation, or long lead time to be ready in OR.

Methods

We developed a method utilizing a compact, inexpensive, non-X-ray based 3D surface light scanner to provide a visual aid that helps surgeons accurately draw osteotomy lines on the surface of exposed bone to reproduce a well-defined preoperative bone resection plan. We tested the accuracy of the method on 18 sawbones using a distal femur hemimetaphyseal resection model and compared it with a traditional, freehand method.

Results

The method significantly reduces the positional error from 2.53 (±1.13) mm to 1.04 (±0.43) mm (p<0.001), and angular error of the front angle from 2.10° (±0.83°) to 0.80° (±0.66°) (p=0.001). The method also reduces the mean maximum deviation of the bone resection, with respect to the preoperative path, from 3.75mm to 2.69mm (p=0.003). However, no increased accuracy was observed at the back side of the bone surface where this method would not be expected to provide information.

Discussion

In summary, we developed a novel 3D-LAD navigation technology. From the experimental study, we demonstrated that the method can improve the ability of surgeons to accurately draw the preoperative osteotomy lines and perform resection of a primary bone sarcoma, with comparison to traditional methods, using 18 sawbones.