Impact of model-based risk analysis for liver surgery planning

Patient-individualized resection planning in liver surgery using 3D print and virtual reality (i-LiVR)-a study protocol for a prospective randomized controlled trial

Background

A multitude of different diseases—benign and malign—can require surgery of the liver. The liver is an especially challenging organ for resection planning due to its unique and interindividually variable anatomy. This demands a high amount of mental imagination from the surgeon in order to plan accordingly - a skill, which takes years of training to acquire and which is difficult to teach. Since the volume of the functional remnant liver is of great importance, parenchyma sparing resections are favoured. 3D reconstructions of computed tomography imaging enable a more precise understanding of anatomy and facilitate resection planning. The modality of presentation of these 3D models ranges from 2D monitors to 3D prints and virtual reality applications.

Methods

The presented trial compares three different modes of demonstration of a 3D reconstruction of CT scans of the liver, which are 3D print, a demonstration on a regular computer screen or using a head-mounted virtual reality headset, with the current gold standard of viewing the CT scan on a computer screen. The group size was calculated with n=25 each. Patients with major liver resections in a laparoscopic or open fashion are eligible for inclusion. Main endpoint is the comparison of the quotient between planned resection volume and actual resection volume between these groups. Secondary endpoints include usability for the surgical team as well as patient specifics and perioperative outcome measures and teaching issues.

Discussion

The described study will give insight in systematic planning of liver resections and the comparison of different demonstration modalities of 3D reconstruction of preoperative CT scans and the preference of technology. Especially teaching of these demanding operations is underrepresented in prior investigations.

Trial registration

Prospective trials registration at the German Clinical Trials register with the registration number DRKS00027865. Registration Date: January 24, 2022.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-022-06347-0.

Assisting Vascular Surgery with Smartphone Augmented Reality

Background Augmented reality is a technology that expands on image-guided surgery to allow intraoperative guidance and navigation. Augmented reality-assisted surgery (ARAS) has not been implemented in the vascular field yet. The wealth of sensors found on modern smartphones make them a promising platform for implementing vascular ARAS. However, current smartphone augmented reality platforms suffer from tracking instability, making them unsuitable for precise surgery. Novel algorithms need to be developed to tackle the stability and performance limitations of mobile phone augmented reality. Aim The primary aim was to develop an ARAS system utilizing low-cost smartphone hardware for vascular surgery. The second aim was to assess its performance by evaluating the stability of its tracking algorithms. Methods We designed an ARAS system utilizing standard optical tracking (SOT) and developed a novel tracking algorithm: hybrid gyroscopic and optical tracking (HGOT) for improved tracking stability. We evaluated the stability of both tracking algorithms using a phantom model and calculated tracking errors using root mean square error (RMSE).  Results The novel augmented reality system displayed a three-dimensional (3D) guidance model fused with the patient's anatomy on a smartphone in real-time. The rotational tracking RMSE was 3.12 degrees for SOT and 0.091 degrees for HGOT. Positional tracking RMSE was 3.3 mm for SOT compared to 0.03 mm for HGOT. Comparing the stability of both tracking techniques showed HGOT to be significantly superior to SOT (p = 0.004). Conclusion We have developed a novel augmented reality system for vascular procedures. The development of HGOT has significantly increased the stability of a low-cost handheld augmented reality solution.

Computational Modeling in Liver Surgery

The need for extended liver resection is increasing due to the growing incidence of liver tumors in aging societies. Individualized surgical planning is the key for identifying the optimal resection strategy and to minimize the risk of postoperative liver failure and tumor recurrence. Current computational tools provide virtual planning of liver resection by taking into account the spatial relationship between the tumor and the hepatic vascular trees, as well as the size of the future liver remnant. However, size and function of the liver are not necessarily equivalent. Hence, determining the future liver volume might misestimate the future liver function, especially in cases of hepatic comorbidities such as hepatic steatosis. A systems medicine approach could be applied, including biological, medical, and surgical aspects, by integrating all available anatomical and functional information of the individual patient. Such an approach holds promise for better prediction of postoperative liver function and hence improved risk assessment. This review provides an overview of mathematical models related to the liver and its function and explores their potential relevance for computational liver surgery. We first summarize key facts of hepatic anatomy, physiology, and pathology relevant for hepatic surgery, followed by a description of the computational tools currently used in liver surgical planning. Then we present selected state-of-the-art computational liver models potentially useful to support liver surgery. Finally, we discuss the main challenges that will need to be addressed when developing advanced computational planning tools in the context of liver surgery.

A novel method for planning liver resections using deformable Bézier surfaces and distance maps

BACKGROUND AND OBJECTIVE

For more than a decade, computer-assisted surgical systems have been helping surgeons to plan liver resections. The most widespread strategies to plan liver resections are: drawing traces in individual 2D slices, and using a 3D deformable plane. In this work, we propose a novel method which requires low level of user interaction while keeping high flexibility to specify resections.

METHODS

Our method is based on the use of Bézier surfaces, which can be deformed using a grid of control points, and distance maps as a base to compute and visualize resection margins (indicators of safety) in real-time. Projection of resections in 2D slices, as well as computation of resection volume statistics are also detailed.

RESULTS

The method was evaluated and compared with state-of-the-art methods by a group of surgeons (n=5, 5-31 years of experience). Our results show that theproposed method presents planning times as low as state-of-the-art methods (174 s median time) with high reproducibility of results in terms of resected volume. In addition, our method not only leads to smooth virtual resections easier to perform surgically compared to other state-of-the-art methods, but also shows superior preservation of resection margins.

CONCLUSIONS

Our method provides clinicians with a robust and easy-to-use method for planning liver resections with high reproducibility, smoothness of resection and preservation of resection margin. Our results indicate the ability of the method to represent any type of resection and being integrated in real clinical work-flows.