Utilizing confocal laser endomicroscopy for evaluating the adequacy of laparoscopic liver ablation.

BACKGROUND: Laparoscopic liver ablation therapy can be used for the treatment of primary and secondary liver malignancy. The increased incidence of cancer recurrence associated with this approach, has been attributed to the inability of monitoring the extent of ablated liver tissue. METHODS: The feasibility of assessing liver ablation with probe-based confocal laser endomicroscopy (CLE) was studied in a porcine model of laparoscopic microwave liver ablation. Following the intravenous injection of the fluorophores fluorescein and indocyanine green, CLE images were recorded at 488 nm and 660 nm wavelength and compared to liver histology. Statistical analysis was performed to assess if fluorescence intensity change can predict the presence of ablated liver tissue. RESULTS: CLE imaging of fluorescein at 488 nm provided good visualization of the hepatic microvasculature; whereas, CLE imaging of indocyanine green at 660 nm enabled detailed visualization of hepatic sinusoid architecture and interlobular septations. Fluorescence intensity as measured in relative fluorescence units was found to be 75-100% lower in ablated compared to healthy liver regions. General linear mixed modeling and ROC analysis found the decrease in fluorescence to be statistically significant. CONCLUSION: Laparoscopic, dual wavelength CLE imaging using two different fluorophores enables clinically useful visualization of multiple liver tissue compartments, in greater detail than is possible at a single wavelength. CLE imaging may provide valuable intraoperative information on the extent of laparoscopic liver ablation.

The NifTK software platform for image-guided interventions: platform overview and NiftyLink messaging.

PURPOSE: To perform research in image-guided interventions, researchers need a wide variety of software components, and assembling these components into a flexible and reliable system can be a challenging task. In this paper, the NifTK software platform is presented. A key focus has been high-performance streaming of stereo laparoscopic video data, ultrasound data and tracking data simultaneously. METHODS: A new messaging library called NiftyLink is introduced that uses the OpenIGTLink protocol and provides the user with easy-to-use asynchronous two-way messaging, high reliability and comprehensive error reporting. A small suite of applications called NiftyGuide has been developed, containing lightweight applications for grabbing data, currently from position trackers and ultrasound scanners. These applications use NiftyLink to stream data into NiftyIGI, which is a workstation-based application, built on top of MITK, for visualisation and user interaction. Design decisions, performance characteristics and initial applications are described in detail. NiftyLink was tested for latency when transmitting images, tracking data, and interleaved imaging and tracking data. RESULTS: NiftyLink can transmit tracking data at 1,024 frames per second (fps) with latency of 0.31 milliseconds, and 512 KB images with latency of 6.06 milliseconds at 32 fps. NiftyIGI was tested, receiving stereo high-definition laparoscopic video at 30 fps, tracking data from 4 rigid bodies at 20-30 fps and ultrasound data at 20 fps with rendering refresh rates between 2 and 20 Hz with no loss of user interaction. CONCLUSION: These packages form part of the NifTK platform and have proven to be successful in a variety of image-guided surgery projects. Code and documentation for the NifTK platform are available from http://www.niftk.org . NiftyLink is provided open-source under a BSD license and available from http://github.com/NifTK/NiftyLink . The code for this paper is tagged IJCARS-2014.

Locally rigid, vessel-based registration for laparoscopic liver surgery.

PURPOSE: Laparoscopic liver resection has significant advantages over open surgery due to less patient trauma and faster recovery times, yet is difficult for most lesions due to the restricted field of view and lack of haptic feedback. Image guidance provides a potential solution but is challenging in a soft deforming organ such as the liver. In this paper, we therefore propose a laparoscopic ultrasound (LUS) image guidance system and study the feasibility of a locally rigid registration for laparoscopic liver surgery. METHODS: We developed a real-time segmentation method to extract vessel centre points from calibrated, freehand, electromagnetically tracked, 2D LUS images. Using landmark-based initial registration and an optional iterative closest point (ICP) point-to-line registration, a vessel centre-line model extracted from preoperative computed tomography (CT) is registered to the ultrasound data during surgery. RESULTS: Using the locally rigid ICP method, the RMS residual error when registering to a phantom was 0.7 mm, and the mean target registration error (TRE) for two in vivo porcine studies was 3.58 and 2.99 mm, respectively. Using the locally rigid landmark-based registration method gave a mean TRE of 4.23 mm using vessel centre lines derived from CT scans taken with pneumoperitoneum and 6.57 mm without pneumoperitoneum. CONCLUSION: In this paper we propose a practical image-guided surgery system based on locally rigid registration of a CT-derived model to vascular structures located with LUS. In a physical phantom and during porcine laparoscopic liver resection, we demonstrate accuracy of target location commensurate with surgical requirements. We conclude that locally rigid registration could be sufficient for practically useful image guidance in the near future.

Enhanced visualisation for minimally invasive surgery.

PURPOSE: Endoscopes used in minimally invasive surgery provide a limited field of view, thus requiring a high degree of spatial awareness and orientation. Attempts at expanding this small, restricted view with previously observed imagery have been made by researchers and is generally known as image mosaicing or dynamic view expansion. For minimally invasive endoscopy, SLAM-based methods have been shown to have potential values but have yet to address effective visualisation techniques. METHODS: The live endoscopic video feed is expanded with previously observed footage. To this end, a method that highlights the difference between actual camera image and historic data observed earlier is proposed. Old video data is faded out to grey scale to mimic human peripheral vision. Specular highlights are removed with the help of texture synthesis to avoid distracting visual cues. The method is further evaluated on in vivo and phantom sequences by a detailed user study to examine the ability of the user in discerning temporal motion trajectories while visualising the expanded field of view, a feature that is of practical value for enhancing spatial awareness and orientation. RESULTS: The difference between historic data and live video is integrated effectively. The use of a single texture domain generated by planar parameterisation is demonstrated for view expansion. Specular highlights can be removed through texture synthesis without introducing noticeable artefacts. The implicit encoding of motion trajectory of the endoscopic camera visualised by the proposed method facilitates both global awareness and temporal evolution of the scene. CONCLUSIONS: Dynamic view expansion provides more context for navigation and orientation by establishing reference points beyond the camera's field of view. Effective integration of visual cues is paramount for concise visualisation.

Dense surface reconstruction for enhanced navigation in MIS.

Recent introduction of dynamic view expansion has led to the development of computer vision methods for minimally invasive surgery to artificially expand the intra-operative field-of-view of the laparoscope. This provides improved awareness of the surrounding anatomical structures and minimises the effect of disorientation during surgical navigation. It permits the augmentation of live laparoscope images with information from previously captured views. Current approaches, however, can only represent the tissue geometry as planar surfaces or sparse 3D models, thus introducing noticeable visual artefacts in the final rendering results. This paper proposes a high-fidelity tissue geometry mapping by combining a sparse SLAM map with semi-dense surface reconstruction. The method is validated on phantom data with known ground truth, as well as in-vivo data captured during a robotic assisted MIS procedure. The derived results have shown that the method is able to effectively increase the coverage of the expanded surgical view without compromising mapping accuracy.