**7. Tissue deformation**

*3D-CT stereoscopic image registration* uses 2 cameras that focus on the same object in space and a combination of the perspectives from 2 separate viewpoints allows a spatial reference for the object. This system has been used for kidneys by isolating a point on the surface of the organ, which can then be aligned to the corresponding point on a 3D reconstruction image. Using this as the centre of rotation, the surgeon can manipulate the operative view for a better understanding of field of operation. This system has shown average target registration error of less than 1 mm and mean registration duration of 48.1 s i.e. more accurate resection

There is however, a lack of organ tracking in this system, meaning that each movement of the camera needs a new image registration. Furthermore, stereoscopic cameras are usually very expensive and require large handling computer systems, which is another limitation to

An ideal AR system would allow the organ to be tracked in real time as it is affected by respiration, tissue deformation and other complications like bleeding. The renal system is particularly vulnerable to these dynamic changes compared to other organs systems like the brain or bones where rigid image registration systems are the norm and do not require as much tracking. The registered image projected into the operative view should be locked and

There are many types of tracking studied in AR surgery. These have mainly included optical tracking where optical markers on the organ allow a measured position of the laparoscopic camera relative to the organ and move with the organ [20]. Infra-red tracking is another method which involves the use of infrared-emitting diode markers. The main issue with optical tracking is instruments obscuring the direct view of field (required for tracking) and a limited depth perception. Infra-red tracking has the issue of selecting the correct anatomical landmarks as markers—mismatches can occur due to deformations, compression and intraoperative haemorrhaging. Many studies have failed to achieve accurate registration of

Electromagnetic tracking is another way of doing this—this has been explored by use of the wireless trackers an ex-vivo bovine partial nephrectomy model. This involved surrounding the tumour within the kidney with magnetic transponders which relayed back to the surgeon and in conjunction with optical camera tracking, a partial nephrectomy was performed [22]. There are limitations to this tracking method as magnetic fields can have interference from laparoscopic instruments and operating tables. The method also requires placement of the magnetic transponders into the target organ and it is currently hard to achieve an alignment error of less

An alternative has been explored by Yip et al. where 3D stereoscopic image registration has been combined this with tracking algorithms—producing only 1.3–3.3 mm degree of error

than 5 mm—a tumour margin error too great for accurate partial nephrectomies [23].

margins, faster registration and reduced theatre time.

dynamically move with the organ and the laparoscopic camera.

dynamic intra-abdominal organs with infra-red [21].

adoption of this method.

98 Evolving Trends in Kidney Cancer

**6. Organ tracking**

The kidney is a dynamic organ and renal surgery involves deformation of the anatomy. Every step in the operation changes the initially projected image registration [26] and for an ideal AR system, there need to be real time feedback for tracing this. An answer to this would be the development of computer algorithms to predict changes in anatomy at crucial steps such as clamping of the renal arteries and surgical dissection at tumour margins. Algorithms can also be developed to predict the effect of ongoing influences on the organ position such as respiratory patterns and peritoneal insufflation.

There have been some tissue deformation models considering renal clamping, incision and external pressure loads to the kidney like intra-operative insufflation. Some of these have shown an improvement of 29% in the registration error when compared to a non-deformation model. However, in these models, the kidney is assumed to have a linear elastic behaviour and the models have been based on ex-vivo kidneys. Another method has been developed taking the diaphragm motion and its influence on kidney motion into consideration—this has used preoperative CT scans during inspiration and expiration and computer errors have been shown to be less than 2 mm in predicting kidney positions [27].

Although there is a scarcity of studies in this area, one study showed that mathematical models were able to predict up to 52% of the operative deformation in porcine kidneys when compared to pre and post-op CT imaging [28].

Baumhauer et al. [29] have proposed a system to answer tissue deformation by insertion of custom-designed navigation aids into the kidney and using "inside-out tracking." This is where the CT-scan can provide real-time spatial awareness by identifying the navigation aids and projecting imaging onto the laparoscopic view. Tested in a virtual environment, this system showed a visualisation error of 1.36 mm (adequately accurate). This system was replicated in the clinical setting by Teber et al. [30] where 10 patients had retroperitoneal laparoscopic partial nephrectomies. The results showed zero cases with a positive surgical margin, zero complication rate and zero conversion to open surgery. This system does however, require placement of aids (like 1.5 cm long needles) into the kidney and is dependent on at least 4 aids being present. This brings risks of damage to healthy parenchyma and aids being lost intra-op.

**9. Specific aspects of renal cancer surgery focused upon in AR** 

There are a few aspects of renal cancer surgery (highlighted by Detmer et al. [27]), that AR development has specifically focused on. These include precise tumour resection and safe

Augmented Reality in Kidney Cancer http://dx.doi.org/10.5772/intechopen.81890 101

We have covered the issue of a precise tumour resection to preserve maximum healthy tissue throughout the chapter. However, Detmer et al. specifically mention some studies to tackle this very area. Ukimara and Gill [38] describe using different colours to signify increasing distance from the tumour and this is overlaid on the AR field of view. Another method uses contouring of the organ around the tumour margin to highlight the tumour itself. Uncertainty of the tumour margin has also been encoded by using different colours to signify certain and

Renal artery clamping is a crucial procedural step as ischemia needs to be limited to tumourspecific parenchyma. This is done by identifying and clamping only the tumour specific arterial branches (usually tertiary or higher-order). This concept has been described as "zeroischaemia" [39]. There have been some studies detecting renal vessels underneath the organ surface and several studies aiming to identify arterial branches for selective clamping with variable success. These have been used for pre-operative planning and some intraoperative guidance. Development has been mostly based in manual registration techniques with dis-

This chapter has described the different aspects of AR in renal cancer and areas that have seen progress. However, there are current limitations holding back the use of AR technology in the clinical set-up. Vavar et al. and Detmer et al. have highlighted some of these (as below):

**1.** Pre-operative medical imaging: currently all reconstructed images need preparing in advance by powerful processing systems. Not only is this expense, it is time consuming. With technological advancement—real-time high-resolution medical imaging and 3D reconstructions could be the norm. This would be displayed in real time intraoperatively

**2.** Inattentional blindness: not seeing an unexpected object in the field of view. This has especially been an issue with 3D image registrations, where the surgeon does not register an object as they were not expecting it to be part of that procedural step. With the development of AR headsets, there will be more information displayed to the operating surgeon. This can be distracting and there needs to be a conscious effort towards only displaying vital information or switching between different sets of information. Further work needs to be done on reducing human factor issues and making the human-computer interface more ergonomic.

**development**

selective arterial clamping.

uncertain areas of the margin [27].

**10. Current limitations**

plays over the laparoscopic view or on a separate screen [27].

and would markedly reduce/eliminate pre-op times.

An answer to tissue deformation could lie closer to technology used within the commercial sector. Advanced facial recognition software used in simulating real time 'Apple animojis' [31] could be adapted to intra-operative kidneys. The software could delineate an optical real time map of the kidney which would change with the active deformation.
