• augmented reality
• general surgery
• human–robot interaction
• imaging technologies
• robotic surgery

• HCC
• hepatectomy
• surgical outcomes

• Humans
• Surgery, Computer-Assisted/methods
• Surgery, Computer-Assisted/trends
• Robotic Surgical Procedures/trends
• Robotic Surgical Procedures/methods
• Imaging, Three-Dimensional
• Laparoscopy/methods
• Endoscopy/methods
• Endoscopy/trends
• Forecasting
• Augmented Reality

• biomedical engineering
• imaging techniques

• Cone-Beam Computed Tomography/methods
• Animals
• Artifacts
• Sheep
• Metals
• Swine
• Surgery, Computer-Assisted/methods
• Arthroplasty, Replacement, Hip/methods
• Cadaver
• Pedicle Screws
• Cervical Vertebrae/diagnostic imaging
• Cervical Vertebrae/surgery

• R01 EB027127 NIBIB NIH HHS
• 2021/ECF1293 Cancer Institute NSW
• R01EB027127 NIH HHS
• National Institutes of Health

• artificial intelligence
• augmented reality
• digestive surgery
• general surgery
• hybrid operating room
• operating room
• robotic surgery
• surgery
• telemedicine
• telesurgery

• interventional radiology
• liver
• minimally invasive surgical procedures
• multimodal imaging
• operating rooms

• Humans
• Operating Rooms
• Surgery, Computer-Assisted/methods
• Liver Neoplasms/surgery
• Biliary Tract Surgical Procedures/methods

• Head mounted display
• Hepatic surgery training
• Three dimensional visualization
• Virtual reality

• Humans
• Virtual Reality
• Imaging, Three-Dimensional
• Tomography, X-Ray Computed/methods
• Female
• Male
• Hepatectomy/methods
• Hepatectomy/education
• Adult
• Young Adult
• Clinical Decision-Making
• User-Computer Interface
• Liver Neoplasms/surgery
• Liver Neoplasms/diagnostic imaging

• SFB 125 Deutsche Forschungsgemeinschaft
• Universitätsklinikum Heidelberg (8914)

• Humans
• Carcinoma, Hepatocellular/diagnostic imaging
• Carcinoma, Hepatocellular/surgery
• Carcinoma, Hepatocellular/complications
• Indocyanine Green
• Liver Neoplasms/diagnostic imaging
• Liver Neoplasms/surgery
• Augmented Reality
• Retrospective Studies
• Blood Loss, Surgical
• Hepatectomy/methods
• Laparoscopy/methods
• Optical Imaging/methods
• Ischemia/complications
• Ischemia/surgery

• Image guidance
• Laparoscopic surgery
• Liver motion
• Mri
• Soft tissue surgery
• Ventilation

• Swine
• Animals
• Organ Motion
• Pneumoperitoneum/diagnostic imaging
• Pneumoperitoneum/etiology
• Laparotomy
• Liver/diagnostic imaging
• Liver/surgery
• Respiration

• Universitätsklinikum Heidelberg (8914)

• Artificial intelligence
• Computer-assisted surgery
• Deep learning
• Intraoperative recognition
• Laparoscopic liver resection
• Semantic segmentation

• Humans
• Artificial Intelligence
• Retrospective Studies
• Liver/diagnostic imaging
• Liver/surgery
• Liver/blood supply
• Hepatectomy/methods
• Laparoscopy/methods

• Augmented reality
• Hepatectomy
• Hepatic resection
• Hepatic surgery

• Humans
• Augmented Reality
• Hepatectomy/methods
• Liver
• Surgeons
• Surgery, Computer-Assisted/methods

Background: Endobronchial ultrasound-guided transbronchial biopsy (EBUS-TBB) is used for the diagnosis of peripheral pulmonary lesions (PPLs), but the diagnostic yield is not adequate. Cone-beam computed tomography-derived augmented fluoroscopy (CBCT-AF) can be utilized to assess the location of PPLs and biopsy devices, and has the potential to improve the diagnostic accuracy of bronchoscopic techniques. The purpose of this study was to verify the contribution of CBCT-AF to EBUS-TBB. Methods: Patients who underwent EBUS-TBB for diagnosis of PPLs were enrolled. The navigation success rate and diagnostic yield were used to evaluate the effectiveness of CBCT-AF in EBUS-TBB. Results: In this study, 236 patients who underwent EBUS-TBB for PPL diagnosis were enrolled. One hundred fifteen patients were in CBCT-AF group and 121 were in non-AF group. The navigation success rate was significantly higher in the CBCT-AF group (96.5% vs. 86.8%, p = 0.006). The diagnostic yield was even better in the CBCT-AF group when the target lesion was small in size (68.8% vs. 0%, p = 0.026 for lesions ≤10 mm and 77.5% vs. 46.4%, p = 0.016 for lesions 10–20 mm, respectively). The diagnostic yield of the two study groups became similar when the procedures with a failure of navigation were excluded. The procedure-related complication rate was similar between the two study groups. Conclusion: CBCT-AF is safe, and effectively enhances the navigation success rate, thereby increasing the diagnostic yield of EBUS-TBB for PPLs.

• artificial intelligence
• augmented reality
• image-guided surgery
• liver surgery
• robotic

• biomedical engineering
• mechanical engineering
• polymers

• Animals
• Bioprinting
• Catheters
• Cell Line
• Elasticity
• Electric Conductivity
• Humans
• Hydrogels/chemistry
• Liver/diagnostic imaging
• Magnets/chemistry
• Rats, Sprague-Dawley
• Robotics
• Swine
• Tomography, X-Ray Computed
• Viscosity
• Rats

• Augmented reality
• Computer assisted navigation
• Computer assisted surgery
• Image guided surgery
• Laparoscopic liver resection
• Laparoscopic liver surgery
• Machine vision
• Robotic liver surgery

• Collapsed lung
• Deformable mesh registration
• Pneumothorax deformation
• Thoracoscopic surgery
• Tissue heterogeneity

• CBCT
• artifact radiography
• dual energy
• misalignment

• Algorithms
• Cholecystectomy, Laparoscopic/methods
• Humans
• Machine Learning
• Surgery, Computer-Assisted/methods
• Workflow

• anatomical hepatectomy
• augmented reality
• laparoscopic surgical navigation
• primary liver cancer
• three-dimensional laparoscopy

Pancreaticoduodenectomy (PD) with superior mesenteric vein (SMV) reconstruction are often required to achieve complete (R0) resection for pancreatic head cancer (PHC) with tumor invasion of the SMV. Augmented reality (AR) technology can be used to assist in determining the extent of SMV involvement by superimposing virtual 3-dimensional (3D) images of the pancreas and regional vasculature on the surgical field.

Three patients with PHC and tumor invasion of the SMV underwent AR-assisted PD with SMV resection and reconstruction following preoperative computed tomography scanning. Preoperative imaging data were used to reconstruct 3D images of anatomical structures, including the tumor, portal vein (PV), SMV, and splenic vein (SV). Using AR software installed on a smart phone, the reconstructed 3D images were superimposed on the surgical field as viewed in a smart phone display to provide intermittent navigational assistance to the surgeon in identifying the boundaries of PHC tumor invasion for resection of the vessels involved.

All patients successfully completed the operation. Intraoperative AR applications displayed virtual images of the pancreas, SMV, bile duct, common hepatic artery (CHA), and superior mesenteric artery (SMA). Two patients required end-to-end anastomosis for reconstruction of the SMV. One patient required allogenic vascular bypass to reconstruct the SMV-PV juncture with concomitant reconstruction of the SV-SMV confluence by end-to-side anastomosis of the SV and bypass vessel. Postoperative pathology confirmed R0 resections for all patients.

AR navigation technology based on preoperative CT image data can assist surgeons performing PD with SMV resection and reconstruction.

• Augmented reality
• Liver resection
• Non-rigid registration
• RGB-D camera

• Head-mounted display
• Liver surgery
• Operation planning
• Remote communication
• Surgical training
• Virtual reality
• Virtual tumor board

Hepatectomy, living donor liver transplantations and other major hepatic interventions rely on precise calculation of the total, remnant and graft liver volume. However, liver volume might differ between the pre- and intraoperative situation. To model liver volume changes and develop and validate such pre- and intraoperative assistance systems, exact information about the influence of lung ventilation and intraoperative surgical state on liver volume is essential.

This study assessed the effects of respiratory phase, pneumoperitoneum for laparoscopy, and laparotomy on liver volume in a live porcine model. Nine CT scans were conducted per pig (N = 10), each for all possible combinations of the three operative (native, pneumoperitoneum and laparotomy) and respiratory states (expiration, middle inspiration and deep inspiration). Manual segmentations of the liver were generated and converted to a mesh model, and the corresponding liver volumes were calculated.

With pneumoperitoneum the liver volume decreased on average by 13.2% (112.7 ml ± 63.8 ml, p < 0.0001) and after laparotomy by 7.3% (62.0 ml ± 65.7 ml, p = 0.0001) compared to native state. From expiration to middle inspiration the liver volume increased on average by 4.1% (31.1 ml ± 55.8 ml, p = 0.166) and from expiration to deep inspiration by 7.2% (54.7 ml ± 51.8 ml, p = 0.007).

Considerable changes in liver volume change were caused by pneumoperitoneum, laparotomy and respiration. These findings provide knowledge for the refinement of available preoperative simulation and operation planning and help to adjust preoperative imaging parameters to best suit the intraoperative situation.

• Computer assistance
• Liver volume
• Navigation
• Operation planning
• Respiratory phase
• Soft tissue surgery

• 3rd order registration circuits
• Breathing motion compensation
• Deformation field
• Elastic body spline
• Reduction of target registration error

• 3D
• Augmented reality
• Image-guided surgery
• Laparoscopy
• Liver

• Algorithms
• Hepatectomy
• Humans
• Imaging, Three-Dimensional
• Laparoscopy
• Liver/diagnostic imaging
• Liver/surgery
• Surgery, Computer-Assisted

• education
• organ transplantation
• surgery, computer-assisted

• Augmented Reality
• Humans
• Organ Transplantation/methods
• Surgery, Computer-Assisted

• Image-guided surgery
• Intraoperative imaging
• Operating room
• Review
• Surgical data science

• deformation
• image guided surgery
• liver
• registration
• ultrasound

• Algorithms
• Image Processing, Computer-Assisted
• Liver/diagnostic imaging
• Liver/surgery
• Surgery, Computer-Assisted
• Tomography, X-Ray Computed
• Ultrasonography

• P30 CA008748 NCI NIH HHS
• R01 CA162477 NCI NIH HHS
• R01 EB027498 NIBIB NIH HHS
• T32 EB021937 NIBIB NIH HHS

• Laparoscopic distal pancreatectomy
• Laparoscopic spleen-preserving distal pancreatectomy
• Preoperative simulation
• Three-dimensional virtual endoscopy

• Aged
• Aged, 80 and over
• Cohort Studies
• Female
• Humans
• Imaging, Three-Dimensional
• Laparoscopy/methods
• Male
• Middle Aged
• Pancreatectomy/methods
• Pancreatic Neoplasms/diagnostic imaging
• Pancreatic Neoplasms/pathology
• Pancreatic Neoplasms/surgery
• Surgery, Computer-Assisted/methods
• Tomography, X-Ray Computed
• Virtual Reality

• Augmented reality
• Laparoscopic surgery
• Liver resection
• Surgical navigation

• Algorithms
• Animals
• Augmented Reality
• Deep Learning
• Depth Perception
• Disease Models, Animal
• Image Processing, Computer-Assisted
• Imaging, Three-Dimensional
• Laparoscopes
• Laparoscopy/methods
• Liver/diagnostic imaging
• Liver/surgery
• Neoplasms/diagnostic imaging
• Reproducibility of Results
• Software
• Surgery, Computer-Assisted
• Swine
• Tomography, X-Ray Computed
• Video Recording

Soft tissue deformation severely impacts the registration of pre- and intra-operative image data during computer-assisted navigation in laparoscopic liver surgery. However, quantifying the impact of target surface size, surface orientation, and mesh quality on non-rigid registration performance remains an open research question. This paper aims to uncover how these affect volume-to-surface registration performance.

To find such evidence, we design three experiments that are evaluated using a three-step pipeline: (1) volume-to-surface registration using the physics-based shape matching method or PBSM, (2) voxelization of the deformed surface to a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1024^3$$\end{document}10243 voxel grid, and (3) computation of similarity (e.g., mutual information), distance (i.e., Hausdorff distance), and classical metrics (i.e., mean squared error or MSE).

Using the Hausdorff distance, we report a statistical significance for the different partial surfaces. We found that removing non-manifold geometry and noise improved registration performance, and a target surface size of only 16.5% was necessary.

By investigating three different factors and improving registration results, we defined a generalizable evaluation pipeline and automatic post-processing strategies that were deemed helpful. All source code, reference data, models, and evaluation results are openly available for download: https://github.com/ghattab/EvalPBSM/.

• Deformation
• Evaluation
• Registration
• Soft tissue
• Surface

• Arthroscopy
• Self-positioning
• Virtual-vision
• Visual–inertial stereo slam

To facilitate intraoperative localization of lung nodules, this study used model-based shape matching techniques to analyze the inter-subject three-dimensional surface deformation induced by pneumothorax. Methods: Contrast- enhanced computed tomography (CT) images of the left lungs of 11 live beagle dogs were acquired at two bronchial pressures (14 and 2 cm\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\,\hbox {H}_2\hbox {O}$$\end{document}H2O). To address shape matching problems for largely deformed lung images with pixel intensity shift, a complete Laplacian-based shape matching solution that optimizes the differential displacement field was introduced.

Experiments were performed to confirm the methods’ registration accuracy using CT images of lungs. Shape similarity and target displacement errors in the registered models were improved compared with those from existing shape matching methods. Spatial displacement of the whole lung’s surface was visualized with an average error of within 5 mm.

The proposed methods address problems with the matching of surfaces with large curvatures and deformations and achieved smaller registration errors than existing shape matching methods, even at the tip and ridge regions. The findings and inter-subject statistical representation are directly available for further research on pneumothorax deformation modeling.

The online version of this article (10.1007/s11548-019-02013-0) contains supplementary material, which is available to authorized users.

• Lung
• Model-based shape matching
• Pneumothorax deformation analysis
• Thoracoscopic surgery

• Colorectal liver metastases
• Laparoscopic liver resection
• Open approach
• Postoperative outcomes
• Survival

• Breathing motion
• Deformation field
• Elastic body spline
• Reduction of target registration error
• Registration circuits

• 3-dimensional patient-specific modeling
• Augmented reality
• Computer-assisted surgery
• Image-guided surgery
• Liver surgery
• Precision surgery
• Virtual reality

• Humans
• Imaging, Three-Dimensional/methods
• Liver Neoplasms/diagnostic imaging
• Liver Neoplasms/surgery
• Minimally Invasive Surgical Procedures/methods
• Surgery, Computer-Assisted/methods
• Tomography, X-Ray Computed/methods

In scenarios where the use of a Computed Tomography (CT) is difficult, such as during surgery or in the ICU, the use of a C-arm system to generate tomographic information could contribute with interesting additional clinical information. Recent days are seeing the development of the so-called cone-beam CT (CBCT) based on advanced motorized isocentric C-arm systems. To be able to make use of more basic C-arm systems, apart from the geometric non-idealities common to any CBCT, we need to address other difficulties. First, the trajectory of the source-detector pair may differ from a circular path and the system may suffer mechanical strains that modify the relative positions of the source and detector for different projection angles. Second, and more importantly, the exact position of the source and detector elements may not be repeatable for consecutive rotations due to low mechanical precision, thus preventing an accurate geometrical calibration of the system. Finally, the limitation of the angular span and the difficulty of obtaining a high number of projections pose a great challenge to the image reconstruction. In this work, we present a novel method to adapt a standard C-arm, originally designed for planar imaging, to be used as a tomograph. The key parts of the new acquisition protocol are (1) a geometrical calibration method to compensate mechanical inaccuracies that prevent an accurate repetition of source-detector position between acquisitions, and (2) an advanced image reconstruction method able to deal with limited angle data, sparse projections and non-circular trajectories. Both methods exploit surface information from the patient, which can be obtained using a 3D surface scanner. The proposed method was evaluated with two real C-arm systems, based on an image intensifier and a flat panel detector respectively, showing the feasibility of the proposal.

• Algorithms
• Calibration
• Cone-Beam Computed Tomography/instrumentation
• Cone-Beam Computed Tomography/methods
• Foot/diagnostic imaging
• Hand/diagnostic imaging
• Humans
• Imaging, Three-Dimensional/instrumentation
• Imaging, Three-Dimensional/methods
• Models, Anatomic
• Phantoms, Imaging

• Ministerio de Economía y Competitividad
• Instituto de Salud Carlos III

• Augmented reality
• Image visualization
• Mobile device
• Visual assistance

• 3D technology
• Augmented reality
• Image-guided surgery
• Liver surgery

• Apparatus and instruments
• Automatic data processing
• Laparoscopy
• Medical device
• Robotic surgical procedures
• Surgery 4.0

• Electronic Data Processing/methods
• Electronic Data Processing/organization & administration
• Humans
• Laparoscopy/instrumentation
• Laparoscopy/methods
• Monitoring, Intraoperative/instrumentation
• Monitoring, Intraoperative/methods
• Operating Room Information Systems/organization & administration
• Operating Rooms/methods
• Operating Rooms/organization & administration
• Software
• Surgery, Computer-Assisted/instrumentation
• Surgery, Computer-Assisted/methods
• Surgical Equipment/standards
• Workflow

• Computer-aided interventions
• Deformable image registration
• Finite element method
• Human liver