Discussion of results
Our results indicate that the two-step registration solution can improve registration speed and may be able to improve accuracy for laparoscopic AR applications in time-critical surgical phases. One limitation of our results is the unusually low overall accuracy of the surface-based primary registration. We see two potential reasons for this: firstly, we used pivot-calibrated standard laparoscopic graspers as an optically tracked tactile pointer to record the required point clouds. We chose this instrument because it is readily available in the operating room. However, it is somewhat flexible and bends easily under mechanical load. This considerably affects the tooltip tracking as that is based on a rigid pivot calibration. An interesting follow-up objective of our work may lie in the measurement and quantification of this deformation and its contribution to the overall registration error. To our knowledge, this has not been previously reported in the literature. Secondly, our participants had very limited experience with handling laparoscopic tools. We anecdotally observed that several participants accidentally recorded some points after the tooltip had slipped off the phantom surface. Participants also applied high pressure when tracing the instrument across the phantom surface, which increased the issue of instrument deformation. Moreover, it was difficult for some participants to keep the tooltip rather than the side of the tool on the surface because of the typically constrained tooltip motion. It seems unlikely that experienced laparoscopic surgeons would experience these specific difficulties.
Inaccuracies in the surface-based primary registration are passed on to the secondary registration. This is because the fiducial location in the virtual model is conducted after and, thereby, based on the primary registration. Thus, there is a discrepancy between the physical fiducial’s position on the kidney and the fiducial’s recorded position on the virtual model. This “fiducial storage error” is added to the fiducial localisation error that occurs during the secondary point acquisition. We were able to quantify the impact of the resulting accuracy loss (fiducial storage error plus fiducial localisation error) with a TRE growth of approximately 0.47–4.23 mm. We, therefore, believe that absolute accuracy could be considerably improved by modifying the means of surface acquisition.
General discussion
While this article represents a successful proof-of-concept evaluation for our two-step registration method, it does not yet demonstrate clinical applicability or benefit. The obvious follow-up question for our results is whether the improvements that our method brings are sufficient to make AR support feasible during the time-critical phase of resection site repair. Specifically, three questions arise: firstly, is the added registration task with an estimated duration of 40 s during a time-critical phase justified by the clinical benefit? That is, can the resection site repair either be accelerated enough to compensate for the additional 40 s or does the additional information make the process more safe and effective? Future work should also examine whether this can be further accelerated by supporting the user in the fiducial acquisition. For example, the fiducials could be detected and highlighted in the video stream. Secondly, is the registration (with improved surface acquisition accuracy) sufficiently accurate to provide meaningful information about the position of risk structures? Finally, our participants’ experience does not reflect the skill level of the experienced surgeons that would use the system in a real application. The different levels of experience may influence users’ abilities to recognise landmarks/fiducials due to a better understanding of the surgical site and to record those landmarks/fiducials due to a higher skill level by using the laparoscopic tools. The third question is, therefore: Which accuracy levels would experienced surgeons achieve with this approach? These questions remain to be answered in future follow-up work.
Another important scope limitation is that we did not consider the effects of organ deformation during the tumour resection. Organ deformation in abdominal AR registration is a major limiting challenge and an active field of research [
1,
21]. Promising concepts exist in the literature to mitigate this by applying biomechanical models to the virtual content and, thereby, simulating the physical organ’s deformation. One approach [
20] informs a biomechanical model via fiducial marker locations and is, therefore, promising for our application and marker-based concept. However, to our knowledge, current biomechanical models [
20,
30] assume that the kidney is deformed but structurally intact. In our application, however, the kidney is additionally deformed from its preoperative state by removing an unknown tissue volume. While some data have been published on the surface deformation caused by a single straight-line incision [
1], a biomechanical model for our application would also have to consider the intrarenal structure deformation that is caused by the removal of a tissue sample. While this requires further research, a deformation study for the liver [
16] has shown that intraoperative deformation is very limited on a local scale. Thus, within the area of the four fiducials and resection site, rigid registration may even prove to be sufficient.
The two conditions that we compared in our study were measured in a fixed order. This may have led to training effects between the two stages of the registration process. Specifically, participants were more familiar with the surgical object (in our case, the phantom) during the secondary registration than during the primary registration. A part of the fiducial acquisition acceleration may be attributed to this fact. However, this prior familiarisation with the surgical site is realistic and, therefore, does not affect the validity of our results.
Overall, registration accuracy in a clinical setting may be higher due to better surface acquisition methods, or it may be lower due to organ deformation. Thus, the absolute TRE values from our study are of limited external validity. However, the effects we found indicate that our concept may be a viable approach for AR support during the resection site repair phase of LPN/RPN.
It should be noted that this article presents a registration concept for the resection bed but does not address the issue of visualising relevant anatomical information. This poses a separate challenge because the exact resected volume is unknown at this point during the surgery. Further work is required to address this, but one potential approach may be visualisations that are based on instrument locations (e.g. [
25]) rather than the permanent bulk display of anatomical structures (e.g. [
7]).
This article discusses the proposed registration method in the context of resection wound repair in LPN/RPN. Further research is required to assess its suitability in other laparoscopic oncological resections. Moreover, the general two-step concept may be suitable for even more image-guided surgery applications in which the registration process is conducted under time pressure and in which the opportunities for intraoperative imaging or preoperative fiducial placing are limited.