Performance of image guided navigation in laparoscopic liver surgery – A systematic review

Background: Compared to open surgery, minimally invasive liver resection has improved short term outcomes. It is however technically more challenging. Navigated image guidance systems (IGS) are being developed to overcome these challenges. The aim of this systematic review is to provide an overview of their current capabilities and limitations. Methods: Medline, Embase and Cochrane databases were searched using free text terms and corresponding controlled vocabulary. Titles and abstracts of retrieved articles were screened for inclusion criteria. Due to the heterogeneity of the retrieved data it was not possible to conduct a meta-analysis. Therefore results are presented in tabulated and narrative format. Results: Out of 2015 articles, 17 pre-clinical and 33 clinical papers met inclusion criteria. Data from 24 articles that reported on accuracy indicates that in recent years navigation accuracy has been in the range of 8–15 mm. Due to discrepancies in evaluation methods it is difficult to compare accuracy metrics between different systems. Surgeon feedback suggests that current state of the art IGS may be useful as a supplementary navigation tool, especially in small liver lesions that are difficult to locate. They are however not able to reliably localise all relevant anatomical structures. Only one article investigated IGS impact on clinical outcomes. Conclusions: Further improvements in navigation accuracy are needed to enable reliable visualisation of tumour margins with the precision required for oncological resections. To enhance comparability between different IGS it is crucial to find a consensus on the assessment of navigation accuracy as a minimum reporting standard

Towards an Accurate Tracking of Liver Tumors for Augmented Reality in Robotic Assisted Surgery

International audienceThis article introduces a method for tracking the internal structures of the liver during robot-assisted procedures. Vascular network, tumors and cut planes, computed from pre-operative data, can be overlaid onto the laparoscopic view for image-guidance, even in the case of large motion or deformation of the organ. Compared to current methods, our method is able to precisely propagate surface motion to the internal structures. This is made possible by relying on a fast yet accurate biomechanical model of the liver combined with a robust visual tracking approach designed to properly constrain the model. Augmentation results are demonstrated on in-vivo sequences of a human liver during robotic surgery, while quantitative validation is performed on an ex-vivo porcine liver experimentation. Validation results show that our approach gives an accurate surface registration with an error of less than 6mm on the position of the tumor

In vivo measurement of human brain elasticity using a light aspiration device

The brain deformation that occurs during neurosurgery is a serious issue impacting the patient "safety" as well as the invasiveness of the brain surgery. Model-driven compensation is a realistic and efficient solution to solve this problem. However, a vital issue is the lack of reliable and easily obtainable patient-specific mechanical characteristics of the brain which, according to clinicians' experience, can vary considerably. We designed an aspiration device that is able to meet the very rigorous sterilization and handling process imposed during surgery, and especially neurosurgery. The device, which has no electronic component, is simple, light and can be considered as an ancillary instrument. The deformation of the aspirated tissue is imaged via a mirror using an external camera. This paper describes the experimental setup as well as its use during a specific neurosurgery. The experimental data was used to calibrate a continuous model. We show that we were able to extract an in vivo constitutive law of the brain elasticity: thus for the first time, measurements are carried out per-operatively on the patient, just before the resection of the brain parenchyma. This paper discloses the results of a difficult experiment and provide for the first time in-vivo data on human brain elasticity. The results point out the softness as well as the highly non-linear behavior of the brain tissue.Comment: Medical Image Analysis (2009) accept\'

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

Comparative validation of single-shot optical techniques for laparoscopic 3-D surface reconstruction

Intra-operative imaging techniques for obtaining the shape and morphology of soft-tissue surfaces in vivo are a key enabling technology for advanced surgical systems. Different optical techniques for 3-D surface reconstruction in laparoscopy have been proposed, however, so far no quantitative and comparative validation has been performed. Furthermore, robustness of the methods to clinically important factors like smoke or bleeding has not yet been assessed. To address these issues, we have formed a joint international initiative with the aim of validating different state-of-the-art passive and active reconstruction methods in a comparative manner. In this comprehensive in vitro study, we investigated reconstruction accuracy using different organs with various shape and texture and also tested reconstruction robustness with respect to a number of factors like the pose of the endoscope as well as the amount of blood or smoke present in the scene. The study suggests complementary advantages of the different techniques with respect to accuracy, robustness, point density, hardware complexity and computation time. While reconstruction accuracy under ideal conditions was generally high, robustness is a remaining issue to be addressed. Future work should include sensor fusion and in vivo validation studies in a specific clinical context. To trigger further research in surface reconstruction, stereoscopic data of the study will be made publically available at www.open-CAS.com upon publication of the paper

Computación paralela heterogénea en registro de imágenes y aplicaciones de álgebra lineal

This doctoral thesis focuses on GPU acceleration of medical image registration and sparse general matrix-matrix multiplication (SpGEMM). The comprehensive work presented here aims to enable new possibilities in Image Guided Surgery (IGS). IGS provides the surgeon with advanced navigation tools during surgery. Image registration, which is a part of IGS, is computationally demanding, therefore GPU acceleration is greatly desirable. spGEMM, which is an essential part in many scientific and data analytics applications, e.g., graph applications, is also a useful tool in biomechanical modeling and sparse vessel network registration. We present this work in two parts. The first part of this thesis describes the optimization of the most demanding part of non-rigid Free Form Deformation registration, i.e., B-spline interpolation. Our novel optimization technique minimizes the data movement between processing cores and memory and maximizes the utilization of the very fast register file. In addition, our approach re-formulates B-spline interpolation to fully utilize Fused Multiply Accumulation instructions for additional benefits in performance and accuracy. Our optimized B-spline interpolation provides significant speedup to image registration. The second part describes the optimization of spGEMM. Hardware manufacturers, with the aim of increasing the performance of deep-learning, created specialized dense matrix multiplication units, called Tensor Core Units (TCUs). However, until now, no work takes advantage of TCUs for sparse matrix multiplication. With this work we provide the first TCU implementation of spGEMM and prove its benefits over conventional GPU spGEMM.Esta tesis doctoral se centra en la aceleración por GPU del registro de imágenes médicas y la multiplicación de matrices dispersas (SpGEMM). El exhaustivo trabajo presentado aquí tiene como objetivo permitir nuevas posibilidades en la cirugía guiada por imagen (IGS). IGS proporciona al cirujano herramientas de navegación avanzadas durante la cirugía. El registro de imágenes, parte de IGS computacionalmente exigente, por lo tanto, la aceleración en GPU es muy deseable. spGEMM, la cual es una parte esencial en muchas aplicaciones científicas y de análisis de datos, por ejemplo, aplicaciones de gráficos, también es una herramienta útil en el modelado biomecánico y el registro de redes de vasos dispersos. Presentamos este trabajo en dos partes. La primera parte de esta tesis describe la optimización de la parte más exigente del registro de deformación de forma libre no rígida, es decir, la interpolación B-spline. Nuestra novedosa técnica de optimización minimiza el movimiento de datos entre los núcleos de procesamiento y la memoria y maximiza la utilización del archivo de registro rápido. Además, nuestro enfoque reformula la interpolación B-spline para utilizar completamente las instrucciones de multiplicación-acumulación fusionada (FMAC) para obtener beneficios adicionales en rendimiento y precisión. Nuestra interpolación B-spline optimizada proporciona una aceleración significativa en el registro de imágenes. La segunda parte describe la optimización de spGEMM. Los fabricantes de hardware, con el objetivo de aumentar el rendimiento del aprendizaje profundo, crearon unidades especializadas de multiplicación de matrices densas, llamadas Tensor Core Units (TCU). Sin embargo, hasta ahora, no se ha encontrado ningún trabajo aprovecha las TCU para la multiplicación de matrices dispersas. Con este trabajo, proporcionamos la primera implementación TCU de spGEMM y demostramos sus beneficios sobre la spGEMM convencional operada sobre dispositivos GPU

Respiratory Compensated Robot for Liver Cancer Treatment: Design, Fabrication, and Benchtop Characterization

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death in the world. Radiofrequency ablation (RFA) is an effective method for treating tumors less than 5 cm. However, manually placing the RFA needle at the site of the tumor is challenging due to the complicated respiratory induced motion of the liver. This paper presents the design, fabrication, and benchtop characterization of a patient mounted, respiratory compensated robotic needle insertion platform to perform percutaneous needle interventions. The robotic platform consists of a 4-DoF dual-stage cartesian platform used to control the pose of a 1-DoF needle insertion module. The active needle insertion module consists of a 3D printed flexible fluidic actuator capable of providing a step-like, grasp-insert-release actuation that mimics the manual insertion procedure. Force characterization of the needle insertion module indicates that the device is capable of producing 22.6 ± 0.40 N before the needle slips between the grippers. Static phantom targeting experiments indicate a positional error of 1.14 ± 0.30 mm and orientational error of 0.99° ± 0.36°. Static ex-vivo porcine liver targeting experiments indicate a positional error of 1.22 ± 0.31 mm and orientational error of 1.16° ± 0.44°. Dynamic targeting experiments with the proposed active motion compensation in dynamic phantom and ex-vivo porcine liver show 66.3% and 69.6% positional accuracy improvement, respectively. Future work will continue to develop this platform with the long-term goal of applying the system to RFA for HCC

Patient-specific simulation environment for surgical planning and preoperative rehearsal

Surgical simulation is common practice in the fields of surgical education and training. Numerous surgical simulators are available from commercial and academic organisations for the generic modelling of surgical tasks. However, a simulation platform is still yet to be found that fulfils the key requirements expected for patient-specific surgical simulation of soft tissue, with an effective translation into clinical practice. Patient-specific modelling is possible, but to date has been time-consuming, and consequently costly, because data preparation can be technically demanding. This motivated the research developed herein, which addresses the main challenges of biomechanical modelling for patient-specific surgical simulation. A novel implementation of soft tissue deformation and estimation of the patient-specific intraoperative environment is achieved using a position-based dynamics approach. This modelling approach overcomes the limitations derived from traditional physically-based approaches, by providing a simulation for patient-specific models with visual and physical accuracy, stability and real-time interaction. As a geometrically- based method, a calibration of the simulation parameters is performed and the simulation framework is successfully validated through experimental studies. The capabilities of the simulation platform are demonstrated by the integration of different surgical planning applications that are found relevant in the context of kidney cancer surgery. The simulation of pneumoperitoneum facilitates trocar placement planning and intraoperative surgical navigation. The implementation of deformable ultrasound simulation can assist surgeons in improving their scanning technique and definition of an optimal procedural strategy. Furthermore, the simulation framework has the potential to support the development and assessment of hypotheses that cannot be tested in vivo. Specifically, the evaluation of feedback modalities, as a response to user-model interaction, demonstrates improved performance and justifies the need to integrate a feedback framework in the robot-assisted surgical setting.Open Acces

Determining the Biomechanical Behavior of the Liver Using Medical Image Analysis and Evolutionary Computation

Modeling the liver deformation forms the basis for the development of new clinical applications that improve the diagnosis, planning and guidance in liver surgery. However, the patient-specific modeling of this organ and its validation are still a challenge in Biomechanics. The reason is the difficulty to measure the mechanical response of the in vivo liver tissue. The current approach consist of performing minimally invasive or open surgery aimed at estimating the elastic constant of the proposed biomechanical models. This dissertation presents how the use of medical image analysis and evolutionary computation allows the characterization of the biomechanical behavior of the liver, avoiding the use of these minimally invasive techniques. In particular, the use of similarity coefficients commonly used in medical image analysis has permitted, on one hand, to estimate the patient-specific biomechanical model of the liver avoiding the invasive measurement of its mechanical response. On the other hand, these coefficients have also permitted to validate the proposed biomechanical models. Jaccard coefficient and Hausdorff distance have been used to validate the models proposed to simulate the behavior of ex vivo lamb livers, calculating the error between the volume of the experimentally deformed samples of the livers and the volume from biomechanical simulations of these deformations. These coefficients has provided information, such as the shape of the samples and the error distribution along their volume. For this reason, both coefficients have also been used to formulate a novel function, the Geometric Similarity Function (GSF). This function has permitted to establish a methodology to estimate the elastic constants of the models proposed for the human liver using evolutionary computation. Several optimization strategies, using GSF as cost function, have been developed aimed at estimating the patient-specific elastic constants of the biomechanical models proposed for the human liver. Finally, this methodology has been used to define and validate a biomechanical model proposed for an in vitro human liver.Martínez Martínez, F. (2014). Determining the Biomechanical Behavior of the Liver Using Medical Image Analysis and Evolutionary Computation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/39337TESI