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The registration process for fusing pre‐operative information with intra‐operative data collected during image‐guided liver surgery struggles due to partial visibility. Learning‐based partial point cloud‐to‐complete surface generation has shown a promising direction for improving registration outcomes. Yet, the intra‐operative liver surface can undergo significant deformation, leading to geometric discrepancies from its pre‐operative shape and introducing error in the completed intra‐operative surface. It is essential to understand the error introduced during surface generation and its impact on both rigid and non‐rigid registration to ensure robust performance in clinical settings. In this study, we leveraged a VN‐OccNet framework trained in a patient‐specific manner on simulated deformed data to generate complete surfaces from partial observations extracted from five viewpoints across four in vitro liver phantoms. We first analysed the error associated with the generated complete surface mesh from the partial point cloud, then integrated the complete surface generated into Go‐ICP and GMM‐FEM registration. Furthermore, we estimated the registration error separately for visible and invisible regions. Our results indicate that the error in the generated surface is more significant further away from the partially visible liver surface, and it could affect the registration, not only in the invisible region, but to some extent also in the visible region within the camera's field of view. This study investigates the impact of surface generation error on registration accuracy during image‐guided liver surgery. Using a patient‐specific VN‐OccNet trained on simulated deformations, complete liver surfaces were reconstructed from partial intra‐operative point clouds and then registered using Go‐ICP and GMM‐FEM. Results show that surface generation errors increase with distance from the visible area and can impact both visible and invisible regions during registration.

Image-guided liver surgery aims to enhance the precision of resection and ablation by providing fast localisation of tumours and adjacent complex vasculature to improve oncologic outcome. This Letter presents a novel end-to-end solution for fast stereo reconstruction and motion estimation that demonstrates high accuracy with phantom and clinical data. The authors’ computationally efficient coarse-to-fine (CTF) stereo approach facilitates liver imaging by accounting for low texture regions, enabling precise three-dimensional (3D) boundary recovery through the use of adaptive windows and utilising a robust 3D motion estimator to reject spurious data. To the best of their knowledge, theirs is the only adaptive CTF matching approach to reconstruction and motion estimation that registers time series of reconstructions to a single key frame for registration to a volumetric computed tomography scan. The system is evaluated empirically in controlled laboratory experiments with a liver phantom and motorised stages for precise quantitative evaluation. Additional evaluation is provided through testing with patient data during liver resection.

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.

Surgical planning and navigation systems now play a significant role in the way treatment decisions are made in many surgical disciplines, including neurosurgery and orthopedic surgery. Advanced visualization techniques such as volume and surface rendering enable three-dimensional (3D) visualization and volumetric analysis of traditional two-dimensional (2D) diagnostic images (e.g., CT, MRI, PET), allowing the surgeon to plan their procedure with greater confidence and based on more detailed (and often, more quantitative) information. Currently available navigation systems augment surgical procedures by allowing surgeons to track their instruments in real-time relative to these preoperative images and patient-specific surgical plans. Real-time instrument tracking enables surgeons to quickly localize areas of interest such as cancerous tumors or major vascular structures. Additionally, there are many approaches to mapping multiple imaging modalities to the surgical field, which can greatly enhance the amount of information available to the operating surgeon. Future efforts in this field will be focused on increasing the accuracy and automation of image processing and intraoperative registration methods and more streamlined integration with surgical therapeutic instruments (e.g., resection and ablation devices), intraoperative imaging, robotics, and augmented reality.

BackgroundThe laparoscopic approach to liver resection may reduce morbidity and hospital stay. However, uptake has been slow due to concerns about patient safety and oncological radicality. Image guidance systems may improve patient safety by enabling 3D visualisation of critical intra- and extrahepatic structures. Current systems suffer from non-intuitive visualisation and a complicated setup process. A novel image guidance system (SmartLiver), offering augmented reality visualisation and semi-automatic registration has been developed to address these issues. A clinical feasibility study evaluated the performance and usability of SmartLiver with either manual or semi-automatic registration.MethodsIntraoperative image guidance data were recorded and analysed in patients undergoing laparoscopic liver resection or cancer staging. Stereoscopic surface reconstruction and iterative closest point matching facilitated semi-automatic registration. The primary endpoint was defined as successful registration as determined by the operating surgeon. Secondary endpoints were system usability as assessed by a surgeon questionnaire and comparison of manual vs. semi-automatic registration accuracy. Since SmartLiver is still in development no attempt was made to evaluate its impact on perioperative outcomes.ResultsThe primary endpoint was achieved in 16 out of 18 patients. Initially semi-automatic registration failed because the IGS could not distinguish the liver surface from surrounding structures. Implementation of a deep learning algorithm enabled the IGS to overcome this issue and facilitate semi-automatic registration. Mean registration accuracy was 10.9 ± 4.2 mm (manual) vs. 13.9 ± 4.4 mm (semi-automatic) (Mean difference − 3 mm; p = 0.158). Surgeon feedback was positive about IGS handling and improved intraoperative orientation but also highlighted the need for a simpler setup process and better integration with laparoscopic ultrasound.ConclusionThe technical feasibility of using SmartLiver intraoperatively has been demonstrated. With further improvements semi-automatic registration may enhance user friendliness and workflow of SmartLiver. Manual and semi-automatic registration accuracy were comparable but evaluation on a larger patient cohort is required to confirm these findings.

Evaluate the feasibility, safety and accuracy of a CT-guided robotic assistance for percutaneous needle placement in the liver. Sixty-six fiducials were surgically inserted into the liver of ten swine and used as targets for needle insertions. All CT-scan acquisitions and robotically-assisted needle insertions were coordinated with breath motion using respiratory monitoring. Skin entry and target points were defined on planning CT-scan. Then, robotically-assisted insertions of 17G needles were performed either by experienced interventional radiologists or by a novice. Post-needle insertion CT-scans were acquired to assess accuracy (3D deviation, ie. distance from needle tip to predefined target) and safety. All needle insertions (43/43; median trajectory length = 83 mm (interquartile range [IQR] 72–105 mm) could be performed in one (n = 36) or two (n = 7) attempts (100% feasibility). Blinded evaluation showed an accuracy of 3.5 ± 1.3 mm. Accuracy did not differ between novice and experienced operators (3.7 ± 1.3 versus 3.4 ± 1.2 mm, P  = 0.44). Neither trajectory angulation nor trajectory length significantly impacted accuracy. No complications were encountered. Needle insertion using the robotic device was shown feasible, safe and accurate in a swine liver model. Accuracy was influenced neither by the trajectory length nor by trajectory angulations nor by operator’s experience. A prospective human clinical trial is recruiting.

ObjectivesTo reappraise the rate of and risk factors for complications of targeted and non-targeted US-guided liver biopsy in a large series.MethodsWe analyzed 2405 liver biopsies performed in 2137 patients (58% males, mean age 54 ± 15 years old) between January 2010 and December 2015. Biopsies were performed for focal liver lesions characterization (targeted) or chronic liver disease assessment (non-targeted). Clinical, laboratory, and technical data were recorded. For targeted biopsies, we also recorded the largest diameter, location, enhancement pattern, and pathology. Advert events were divided into marked symptoms and complications. Those requiring specific treatment (embolization or surgery) were considered as severe.ResultsA total of 1283 (53%) targeted and 1122 (47%) non-targeted biopsies were performed. Marked symptoms occurred after 134 biopsies (5.6%) (95 (7.4%) targeted and 39 (3.5%) non-targeted, p < 0.001), the most common being pain (109/134). Complications occurred after 38 biopsies (1.6%) (24 (1.9%) targeted and 14 (1.2%) non-targeted, p = 0.253) and were severe in 13 patients. In univariate analysis, prothrombin time (p = 0.006), serum creatinine level (p < 0.001), largest lesion diameter (p < 0.001), and tumor pathology (p = 0.040) were associated with the occurrence of complications but not platelet count or lesion enhancement pattern. In multivariate analysis, only the largest lesion diameter was retained (OR 1.014 [1.002–1.026], p = 0.018).ConclusionThe rate of advert events after US-guided liver biopsy was low, with no difference between targeted and non-targeted biopsies. When focusing on targeted biopsies, the largest lesion diameter but not enhancement pattern appeared as the main risk factor.Key Points• Targeted and non-targeted liver biopsies are associated with the same observed risk of complication.• Arterial phase hyperenhanced tumors on contrast-enhanced CT or MRI are not associated with a higher risk of complication when compared with non-hyperenhanced ones.• A high serum creatinine level is associated with a higher risk of complication and should motivate strict post-biopsy surveillance.

Background For stereotactic body radiation therapy (SBRT) of liver tumors, tumor motion induced by respiration must be taken into account in planning and treatment. We evaluated whether liver tumor motion at the planning simulation represents liver tumor motion during SBRT, and estimated inter- and intrafractional tumor motion changes in patients undergoing liver SBRT. Methods Ten patients underwent four-dimensional cone-beam computed tomography (4D-CBCT) image-guided liver SBRT with abdominal compression (AC) and fiducial markers. 4D-CBCT was performed to evaluate liver tumor motion at the planning simulation, pre-, and post-SBRT. The translational distances at the center position of the fiducial markers from all 10 phases on the 4D-CBCT images were measured as the extent of the liver tumor motion in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions. Pearson correlation coefficients were calculated to evaluate the correlation between liver tumor motion of the planning simulation and the mean liver tumor motion of the pre-SBRT. Inter- and intrafractional liver tumor motion changes were measured based on the 4D-CBCT of planning simulation, pre-, and post-SBRT. Significant inter- and intrafractional changes in liver tumor motion were defined as a change of >3 mm. Results The mean (± SD) liver tumor motion of the planning simulation 4D-CBCT was 1.7 ± 0.8 mm, 2.4 ± 2.2 mm, and 5.3 ± 3.3 mm, in the LR, AP, and SI directions, respectively. Those of the pre-SBRT 4D-CBCT were 1.2 ± 0.7 mm, 2.3 ± 2.3 mm, and 4.5 ± 3.8 mm, in the LR, AP, and SI directions, respectively. There was a strong significant correlation between liver tumor motion of the planning simulation and pre-SBRT in the LR ( R  = 0.7, P  < 0.01), AP ( R  = 0.9, P  < 0.01), and SI ( R  = 0.9, P  < 0.01) directions. Significant inter- and intrafractional liver tumor motion changes occurred in 10 and 2% of treatment fractions, respectively. Conclusions Liver tumor motion at the planning simulation represents liver tumor motion during SBRT. Inter- and intrafractional liver tumor motion changes were small in patients with AC.

Background To investigate efficiency, accuracy and clinical benefit of a new augmented reality system for 3D laparoscopic liver surgery. Methods All patients who received laparoscopic liver resection by a new image-guided surgery system with augmented 3D-imaging in a university hospital were included for analysis. Digitally processed preoperative cross-sectional imaging was merged with the laparoscopic image. Intraoperative efficiency of the procedure was measured as time needed to achieve sufficient registration accuracy. Technical accuracy was reported as fiducial registration error (FRE). Clinical benefit was assessed trough a questionnaire, reporting measures in a 5-point Likert scale format ranging from 1 (high) to 5 (low). Results From January to March 2018, ten laparoscopic liver resections of a total of 18 lesions were performed using the novel augmented reality system. Median time for registration was 8:50 min (range 1:31–23:56). The mean FRE was reduced from 14.0 mm (SD 5.0) in the first registration attempt to 9.2 mm (SD 2.8) in the last attempt. The questionnaire revealed the ease of use of the system (1.2, SD 0.4) and the benefit for resection of vanishing lesions (1.0, SD 0.0) as convincing positive aspects, whereas image registration accuracy for resection guidance was consistently judged as too inaccurate. Conclusions Augmented reality in 3D laparoscopic liver surgery with landmark-based registration technique is feasible with only little impact on the intraoperative workflow. The benefit for detecting particularly vanishing lesions is high. For an additional benefit during the resection process, registration accuracy has to be improved and non-rigid registration algorithms will be required to address intraoperative anatomical deformation.

Artificial intelligence makes surgical resection easier and safer, and, at the same time, can improve oncological results. The robotic system fits perfectly with these more or less diffused technologies, and it seems that this benefit is mutual. In liver surgery, robotic systems help surgeons to localize tumors and improve surgical results with well-defined preoperative planning or increased intraoperative detection. Furthermore, they can balance the absence of tactile feedback and help recognize intrahepatic biliary or vascular structures during parenchymal transection. Some of these systems are well known and are already widely diffused in open and laparoscopic hepatectomies, such as indocyanine green fluorescence or ultrasound-guided resections, whereas other tools, such as Augmented Reality, are far from being standardized because of the high complexity and elevated costs. In this paper, we review all the experiences in the literature on the use of artificial intelligence systems in robotic liver resections, describing all their practical applications and their weaknesses.