Liver deformation in an animal model due to pneumoperitoneum assessed by a vessel-based deformable registration

Intraoperative liver deformation and organ motion caused by ventilation, laparotomy, and pneumoperitoneum in a porcine model for image-guided liver surgery

BACKGROUND

Image-guidance promises to make complex situations in liver interventions safer. Clinical success is limited by intraoperative organ motion due to ventilation and surgical manipulation. The aim was to assess influence of different ventilatory and operative states on liver motion in an experimental model.

METHODS

Liver motion due to ventilation (expiration, middle, and full inspiration) and operative state (native, laparotomy, and pneumoperitoneum) was assessed in a live porcine model (n = 10). Computed tomography (CT)-scans were taken for each pig for each possible combination of factors. Liver motion was measured by the vectors between predefined landmarks along the hepatic vein tree between CT scans after image segmentation.

RESULTS

Liver position changed significantly with ventilation. Peripheral regions of the liver showed significantly higher motion (maximal Euclidean motion 17.9 ± 2.7 mm) than central regions (maximal Euclidean motion 12.6 ± 2.1 mm, p < 0.001) across all operative states. The total average motion measured 11.6 ± 0.7 mm (p < 0.001). Between the operative states, the position of the liver changed the most from native state to pneumoperitoneum (14.6 ± 0.9 mm, p < 0.001). From native state to laparotomy comparatively, the displacement averaged 9.8 ± 1.2 mm (p < 0.001). With pneumoperitoneum, the breath-dependent liver motion was significantly reduced when compared to other modalities. Liver motion due to ventilation was 7.7 ± 0.6 mm during pneumoperitoneum, 13.9 ± 1.1 mm with laparotomy, and 13.5 ± 1.4 mm in the native state (p < 0.001 in all cases).

CONCLUSIONS

Ventilation and application of pneumoperitoneum caused significant changes in liver position. Liver motion was reduced but clearly measurable during pneumoperitoneum. Intraoperative guidance/navigation systems should therefore account for ventilation and intraoperative changes of liver position and peripheral deformation.

Ultrasound guidance in navigated liver surgery: toward deep-learning enhanced compensation of deformation and organ motion

PURPOSE

Accuracy of image-guided liver surgery is challenged by deformation of the liver during the procedure. This study aims at improving navigation accuracy by using intraoperative deep learning segmentation and nonrigid registration of hepatic vasculature from ultrasound (US) images to compensate for changes in liver position and deformation.

METHODS

This was a single-center prospective study of patients with liver metastases from any origin. Electromagnetic tracking was used to follow US and liver movement. A preoperative 3D model of the liver, including liver lesions, and hepatic and portal vasculature, was registered with the intraoperative organ position. Hepatic vasculature was segmented using a reduced 3D U-Net and registered to preoperative imaging after initial alignment followed by nonrigid registration. Accuracy was assessed as Euclidean distance between the tumor center imaged in the intraoperative US and the registered preoperative image.

RESULTS

Median target registration error (TRE) after initial alignment was 11.6 mm in 25 procedures and improved to 6.9 mm after nonrigid registration (p = 0.0076). The number of TREs above 10 mm halved from 16 to 8 after nonrigid registration. In 9 cases, registration was performed twice after failure of the first attempt. The first registration cycle was completed in median 11 min (8:00-18:45 min) and a second in 5 min (2:30-10:20 min).

CONCLUSION

This novel registration workflow using automatic vascular detection and nonrigid registration allows to accurately localize liver lesions. Further automation in the workflow is required in initial alignment and classification accuracy.

Database-driven patient-specific registration error compensation method for image-guided laparoscopic surgery

PURPOSE

A surgical navigation system helps surgeons understand anatomical structures in the operative field during surgery. Patient-to-image registration, which aligns coordinate systems between the CT volume and a positional tracker, is vital for accurate surgical navigation. Although a point-based rigid registration method using fiducials on the body surface is often utilized for laparoscopic surgery navigation, precise registration is difficult due to such factors as soft tissue deformation. We propose a method that compensates a transformation matrix computed using fiducials on the body surface based on the analysis of positional information in the database.

METHODS

We built our database by measuring the positional information of the fiducials and the guidance targets in both the CT volume and positional tracker coordinate systems through previous surgeries. We computed two transformation matrices: using only the fiducials and using only the guidance targets in all the data in the database. We calculated the differences between the two transformation matrices in each piece of data. The compensation transformation matrix was computed by averaging these difference matrices. In this step, we selected the data from the database based on the similarity of the fiducials and the configuration of the guidance targets.

RESULTS

We evaluated our proposed method using 20 pieces of data acquired during laparoscopic gastrectomy for gastric cancer. The locations of blood vessels were used as guidance targets for computing target registration error. The mean target registration errors significantly decreased from 33.0 to 17.1 mm before and after the compensation.

CONCLUSION

This paper described a registration error compensation method using a database for image-guided laparoscopic surgery. Since our proposed method reduced registration error without additional intraoperative measurements during surgery, it increases the accuracy of surgical navigation for laparoscopic surgery.

Techniques and Algorithms for Hepatic Vessel Skeletonization in Medical Images: A Survey

Hepatic vessel skeletonization serves as an important means of hepatic vascular analysis and vessel segmentation. This paper presents a survey of techniques and algorithms for hepatic vessel skeletonization in medical images. We summarized the latest developments and classical approaches in this field. These methods are classified into five categories according to their methodological characteristics. The overview and brief assessment of each category are provided in the corresponding chapters, respectively. We provide a comprehensive summary among the cited publications, image modalities and datasets from various aspects, which hope to reveal the pros and cons of every method, summarize its achievements and discuss the challenges and future trends.

A study of generalization and compatibility performance of 3D U-Net segmentation on multiple heterogeneous liver CT datasets

BACKGROUND

Most existing algorithms have been focused on the segmentation from several public Liver CT datasets scanned regularly (no pneumoperitoneum and horizontal supine position). This study primarily segmented datasets with unconventional liver shapes and intensities deduced by contrast phases, irregular scanning conditions, different scanning objects of pigs and patients with large pathological tumors, which formed the multiple heterogeneity of datasets used in this study.

METHODS

The multiple heterogeneous datasets used in this paper includes: (1) One public contrast-enhanced CT dataset and one public non-contrast CT dataset; (2) A contrast-enhanced dataset that has abnormal liver shape with very long left liver lobes and large-sized liver tumors with abnormal presets deduced by microvascular invasion; (3) One artificial pneumoperitoneum dataset under the pneumoperitoneum and three scanning profiles (horizontal/left/right recumbent position); (4) Two porcine datasets of Bama type and domestic type that contains pneumoperitoneum cases but with large anatomy discrepancy with humans. The study aimed to investigate the segmentation performances of 3D U-Net in: (1) generalization ability between multiple heterogeneous datasets by cross-testing experiments; (2) the compatibility when hybrid training all datasets in different sampling and encoder layer sharing schema. We further investigated the compatibility of encoder level by setting separate level for each dataset (i.e., dataset-wise convolutions) while sharing the decoder.

RESULTS

Model trained on different datasets has different segmentation performance. The prediction accuracy between LiTS dataset and Zhujiang dataset was about 0.955 and 0.958 which shows their good generalization ability due to that they were all contrast-enhanced clinical patient datasets scanned regularly. For the datasets scanned under pneumoperitoneum, their corresponding datasets scanned without pneumoperitoneum showed good generalization ability. Dataset-wise convolution module in high-level can improve the dataset unbalance problem. The experimental results will facilitate researchers making solutions when segmenting those special datasets.

CONCLUSIONS

(1) Regularly scanned datasets is well generalized to irregularly ones. (2) The hybrid training is beneficial but the dataset imbalance problem always exits due to the multi-domain homogeneity. The higher levels encoded more domain specific information than lower levels and thus were less compatible in terms of our datasets.

Morphologic Change of In Vivo Porcine Liver Under 13 mm Hg Pneumoperitoneum Pressure

BACKGROUND

Clinically, the total and residual liver volume must be accurately calculated before major hepatectomy. However, liver volume might be influenced by pneumoperitoneum during surgery. Changes in liver volume change also affect the accuracy of simulation and augmented reality navigation systems, which are commonly first validated in animal models. In this study, the morphologic changes in porcine livers in vivo under 13 mm Hg pneumoperitoneum pressure were investigated.

MATERIALS AND METHODS

Twenty male pigs were scanned with contrast-enhanced computed tomography without pneumoperitoneum and with 13 mm Hg pneumoperitoneum pressure.

RESULTS

The surface area and volume of the liver and the vascular diameter of the aortic lumen, inferior vena cava lumen, and portal vein lumen were measured. There were statistically significant differences in the surface area and volume of the liver (P=0.000), transverse diameter of the portal vein (P=0.038), longitudinal diameter of the inferior vena cava (P=0.033), longitudinal diameter of the portal vein (P=0.036), vascular cross-sectional area of the inferior vena cava (P=0.028), and portal vein (P=0.038) before and after 13 mm Hg pneumoperitoneum pressure.

CONCLUSIONS

This study indicated that the creation of pneumoperitoneum at 13 mm Hg pressure in a porcine causes liver morphologic alterations affecting the area and volume, as well as the diameter of a blood vessel.

Deformation of the Pelvic Arteries Caused by Pneumoperitoneum and Postural Changes in an Animal Model

BACKGROUND/AIM

We investigated pelvic arterial deformation and shift due to intraoperative pneumoperitoneum and postural changes in an animal model.

MATERIALS AND METHODS

Computed tomography images of pigs were acquired in different body positions (supine, head down at 5° and 10°, right lateral recumbent at 5° and 15°) before and after insufflation. We used a free software (3D Slicer) for image analysis. After landmark registration using 10 markers inserted into the pelvis, pelvic arterial deformation and shift of seven arterial bifurcation points were evaluated. The distance moved was the target registration error (TRE) from the points registered in the supine position. Fiducial registration error (FRE) was measured using the 10 pelvic markers.

RESULTS

TRE average from postural changes ranged from 0.7 to 1.2 mm and was 1.4 mm due to pneumoperitoneum. TRE and FRE averages were 2.1 mm and 0.2 mm, respectively.

CONCLUSION

The pelvis was useful for registering anatomical landmarks.

Initial Report: A Novel Intraoperative Navigation System for Laparoscopic Liver Resection Using Real-Time Virtual Sonography

Recent progress in navigation has revealed problems involving non-rigid registration for hepatic surgery. With the increasing popularity of laparoscopic liver surgery, a new laparoscopic navigation system is necessary. This study involved an in-vitro demonstration of a 3-dimensional printer model and in vivo demonstration in four patients. For the in vitro examination, a position detecting unit attached at 33 cm and 13 cm distance conditions from the tip of the electrocautery was examined eight times at the marked points on the liver surface eight times respectively. The differences between the simulation and the authentic dissecting plane were conventionally investigated in vivo. In vitro, the errors of the 33 cm and 13 cm distance model were7.8 ± 3.5 mm (mean ± SD), and 3.3 ± 1.0 mm, respectively. The mean differences of the dissection plane were within 10 mm. The potentiality and safety of the novel navigation system was confirmed, although further investigation is recommended.

Image-guided minimally invasive endopancreatic surgery using a computer-assisted navigation system

BACKGROUND

Minimally invasive endopancreatic surgery (EPS), performing a pancreatic resection from inside the pancreatic duct, has been proposed as an experimental alternative to duodenum-preserving pancreatic head resection in benign diseases such as chronic pancreatitis, but is complicated by difficult spatial orientation when trying to reach structures of interest. This study assessed the feasibility and potential benefits of image-guided EPS using a computer-assisted navigation system in artificial pancreas silicon model.

METHODS

A surgical navigation system displayed a 3D reconstruction of the original computed tomography (CT) scan and the endoscope in relation to a selected target structure. In a first step, different surface landmark (LM)-based and intraparenchymal LM-based approaches for image-to-physical space registration were evaluated. The accuracy of registration was measured as fiducial registration error (FRE). Subsequently, intrapancreatic lesions (n = 8) that were visible on preoperative imaging, but not on the endoscopic view, were targeted with a computer-assisted, image-guided endopancreatic resection technique in pancreas silicon models. After each experiment, a CT scan was obtained for measurement of the shortest distance from the resection cavity to the centre of the lesion.

RESULTS

Intraparenchymal LM registration [FRE 2.24 mm (1.40-2.85)] was more accurate than surface LM registration [FRE 3.46 mm (2.25-4.85); p = 0.035], but not more accurate than combined registration of intraparenchymal and surface LM [FRE 2.46 mm (1.60-3.35); p = 0.052]. Using image-guided EPS, six of seven lesions were successfully targeted. The median distance from the resection cavity to the centre of the lesion on CT was 1.52 mm (0-2.4). In one pancreas, a lesion could not be resected due to the fragility of the pancreas model.

CONCLUSION

Image-guided minimally invasive EPS using a computer-assisted navigation system enabled successful targeting of pancreatic lesions that were invisible on the endoscopic image, but detectable on preoperative imaging. In the clinical setting, this tool could facilitate complex minimally invasive and robotic pancreatic procedures.

Application of image-guided navigation system for laparoscopic hepatobiliary surgery

BACKGROUND

To achieve safety of the operation, preoperative simulation became a routine practice for hepatobiliary and pancreatic (HBP) surgery. The use of intraoperative ultrasonography (IOUS) is essential in HBP surgery. There is a limitation in the use of IOUS in laparoscopic surgery (LS), for which a new intraoperative system is expected. We have developed an image-guided navigation system (IG-NS) for open HBP surgery since 2006, and we have applied our system to LS. The aim of this study is to evaluate the results of clinical application of IG-NS in LS.

MATERIALS AND METHODS

Eight patients underwent LS using IG-NS; LS consisted of cholecystectomy and hepatectomy in four patients each. After registration, the 3D models were superimposed on the surgical field. We performed LS while observing the navigation image. Moreover, we developed a support system for operations.

RESULTS

The average registration error was 8.8 mm for LS. Repeated registration was effective for organ deformation and improved the precision of IG-NS. By using various countermeasures, identification of the tumor's position and the setting of the resection line became easy.

CONCLUSION

As IG-NS provided real-time detailed and intuitive information, this intraoperative assist system may be an effective tool in LS.

The effect of intraoperative imaging on surgical navigation for laparoscopic liver resection surgery

Conventional surgical navigation systems rely on preoperative imaging to provide guidance. In laparoscopic liver surgery, insufflation of the abdomen (pneumoperitoneum) can cause deformations on the liver, introducing inaccuracies in the correspondence between the preoperative images and the intraoperative reality. This study evaluates the improvements provided by intraoperative imaging for laparoscopic liver surgical navigation, when displayed as augmented reality (AR). Significant differences were found in terms of accuracy of the AR, in favor of intraoperative imaging. In addition, results showed an effect of user-induced error: image-to-patient registration based on annotations performed by clinicians caused 33% more inaccuracy as compared to image-to-patient registration algorithms that do not depend on user annotations. Hence, to achieve accurate surgical navigation for laparoscopic liver surgery, intraoperative imaging is recommendable to compensate for deformation. Moreover, user annotation errors may lead to inaccuracies in registration processes.

Subject-specific modelling of pneumoperitoneum: model implementation, validation and human feasibility assessment

PURPOSE

The aim of this study is to propose a model that simulates patient-specific anatomical changes resulting from pneumoperitoneum, using preoperative data as input. The framework can assist the surgeon through a real-time visualisation and interaction with the model. Such could further facilitate surgical planning preoperatively, by defining a surgical strategy, and intraoperatively to estimate port positions.

METHODS

The biomechanical model that simulates pneumoperitoneum was implemented within the GPU-accelerated NVIDIA FleX position-based dynamics framework. Datasets of multiple porcine subjects before and after abdominal insufflation were used to generate, calibrate and validate the model. The feasibility of modelling pneumoperitoneum in human subjects was assessed by comparing distances between specific landmarks from a patient abdominal wall, to the same landmark measurements on the simulated model.

RESULTS

The calibration of simulation parameters resulted in a successful estimation of an optimal set parameters. A correspondence between the simulation pressure parameter and the experimental insufflation pressure was determined. The simulation of pneumoperitoneum in a porcine subject resulted in a mean Hausdorff distance error of 5-6 mm. Feasibility of modelling pneumoperitoneum in humans was successfully demonstrated.

CONCLUSION

Simulation of pneumoperitoneum provides an accurate subject-specific 3D model of the inflated abdomen, which is a more realistic representation of the intraoperative scenario when compared to preoperative imaging alone. The simulation results in a stable and interactive framework that performs in real time, and supports patient-specific data, which can assist in surgical planning.

Up-to-date intraoperative computer assisted solutions for liver surgery

Computer assisted surgical planning allowed for a better selection of patients, evaluation of operative strategy, appropriate volumetric measurements, identification of anatomical risks, definition of tumour resection margins and choice of surgical approach in liver oncologic resections and living donor liver transplantations. Although preoperative computer surgical analysis has been widely used in daily clinical practice, intraoperative computer assisted solutions for risk analysis and navigation in liver surgery are not widely available or still under clinical evaluation. Computer science technology can efficiently assist modern surgeons during complex liver operations, mainly by providing image guidance with individualized 2D images and 3D models of the various anatomical and pathological structures of interest. Intraoperative computer assisted liver surgery is particularly useful in complex parenchyma-sparing hepatectomies, for intraoperative risk analysis and for the effective treatment of colorectal metastases after neoadjuvant therapy or when they are multiple. In laparoscopic liver surgery, intraoperative computer aid is definitively more important as, apart from a restricted field of view, there is also loss of the fine haptic feedback. Intraoperative computer assisted developments face challenges that prevent their application in daily clinical practice. There is a vast variety of studies regarding intraoperative computer assisted liver surgery but there are no clear objective measurements in order to compare them and select the most effective solutions. An overview of up-to-date intraoperative computer assisted solutions for liver surgery will be discussed.

Laboratory test of Single Landmark registration method for ultrasound-based navigation in laparoscopy using an open-source platform

Purpose

Test the feasibility of the novel Single Landmark image-to-patient registration method for use in the operating room for future clinical trials. The algorithm is implemented in the open-source platform CustusX, a computer-aided intervention research platform dedicated to intraoperative navigation and ultrasound, with an interface for laparoscopic ultrasound probes.

Methods

The Single Landmark method is compared to fiducial landmark on an IOUSFAN (Kyoto Kagaku Co., Ltd., Japan) soft tissue abdominal phantom and T2 magnetic resonance scans of it.

Results

The experiments show that the accuracy of the Single Landmark registration is good close to the registered point, increasing with the distance from this point (12.4 mm error at 60 mm away from the registered point). In this point, the registration accuracy is mainly dominated by the accuracy of the user when clicking on the ultrasound image. In the presented set-up, the time required to perform the Single Landmark registration is 40% less than for the FLRM.

Conclusion

The Single Landmark registration is suitable for being integrated in a laparoscopic workflow. The statistical analysis shows robustness against translational displacements of the patient and improvements in terms of time. The proposed method allows the clinician to accurately register lesions intraoperatively by clicking on these in the ultrasound image provided by the ultrasound transducer. The Single Landmark registration method can be further combined with other more accurate registration approaches improving the registration at relevant points defined by the clinicians.

Characterization and correction of intraoperative soft tissue deformation in image-guided laparoscopic liver surgery

Laparoscopic liver surgery is challenging to perform due to a compromised ability of the surgeon to localize subsurface anatomy in the constrained environment. While image guidance has the potential to address this barrier, intraoperative factors, such as insufflation and variable degrees of organ mobilization from supporting ligaments, may generate substantial deformation. The severity of laparoscopic deformation in humans has not been characterized, and current laparoscopic correction methods do not account for the mechanics of how intraoperative deformation is applied to the liver. We first measure the degree of laparoscopic deformation at two insufflation pressures over the course of laparoscopic-to-open conversion in 25 patients. With this clinical data alongside a mock laparoscopic phantom setup, we report a biomechanical correction approach that leverages anatomically load-bearing support surfaces from ligament attachments to iteratively reconstruct and account for intraoperative deformations. Laparoscopic deformations were significantly larger than deformations associated with open surgery, and our correction approach yielded subsurface target error of [Formula: see text] and surface error of [Formula: see text] using only sparse surface data with realistic surgical extent. Laparoscopic surface data extents were examined and found to impact registration accuracy. Finally, we demonstrate viability of the correction method with clinical data.