Clinical evaluation of a respiratory gated guidance system for liver punctures

Augmented reality navigation with ultrasound-assisted point cloud registration for percutaneous ablation of liver tumors

PURPOSE

We present a novel augmented reality (AR) surgical navigation method with ultrasound-assisted point cloud registration for percutaneous ablation of liver tumors. A preliminary study is carried out to verify its feasibility.

METHODS

Two three-dimensional (3D) point clouds of the liver surface are derived from the preoperative images and intraoperative tracked US images, respectively. To compensate for the soft tissue deformation, the point cloud registration between the preoperative images and the liver is performed using the non-rigid iterative closest point (ICP) algorithm. A 3D AR device based on integral videography technology is designed to accurately display naked-eye 3D images for surgical navigation. Based on the above registration, naked-eye 3D images of the liver surface, planning path, entry points, and tumor can be overlaid in situ through our 3D AR device. Finally, the AR-guided targeting accuracy is evaluated through entry point positioning.

RESULTS

Experiments on both the liver phantom and in vitro pork liver were conducted. Several entry points on the liver surface were used to evaluate the targeting accuracy. The preliminary validation on the liver phantom showed average entry-point errors (EPEs) of 2.34 ± 0.45 mm, 2.25 ± 0.72 mm, 2.71 ± 0.82 mm, and 2.50 ± 1.11 mm at distinct US point cloud coverage rates of 100%, 75%, 50%, and 25%, respectively. The average EPEs of the deformed pork liver were 4.49 ± 1.88 mm and 5.02 ± 2.03 mm at the coverage rates of 100% and 75%, and the average covered-entry-point errors (CEPEs) were 4.96 ± 2.05 mm and 2.97 ± 1.37 mm at 50% and 25%, respectively.

CONCLUSION

Experimental outcomes demonstrate that the proposed AR navigation method based on US-assisted point cloud registration has achieved an acceptable targeting accuracy on the liver surface even in the case of liver deformation.

Augmented Reality Guidance for the Resection of Missing Colorectal Liver Metastases: An Initial Experience

BACKGROUND

Modern chemotherapy achieves the shrinking of colorectal cancer liver metastases (CRLM) to such extent that they may disappear from radiological imaging. Disappearing CRLM rarely represents a complete pathological remission and have an important risk of recurrence. Augmented reality (AR) consists in the fusion of real-time patient images with a computer-generated 3D virtual patient model created from pre-operative medical imaging. The aim of this prospective pilot study is to investigate the potential of AR navigation as a tool to help locate and surgically resect missing CRLM.

METHODS

A 3D virtual anatomical model was created from thoracoabdominal CT-scans using customary software (VR RENDER(®), IRCAD). The virtual model was superimposed to the operative field using an Exoscope (VITOM(®), Karl Storz, Tüttlingen, Germany). Virtual and real images were manually registered in real-time using a video mixer, based on external anatomical landmarks with an estimated accuracy of 5 mm. This modality was tested in three patients, with four missing CRLM that had sizes from 12 to 24 mm, undergoing laparotomy after receiving pre-operative oxaliplatin-based chemotherapy.

RESULTS

AR display and fine registration was performed within 6 min. AR helped detect all four missing CRLM, and guided their resection. In all cases the planned security margin of 1 cm was clear and resections were confirmed to be R0 by pathology. There was no postoperative major morbidity or mortality. No local recurrence occurred in the follow-up period of 6-22 months.

CONCLUSIONS

This initial experience suggests that AR may be a helpful navigation tool for the resection of missing CRLM.

Augmented Reality Guidance for the Resection of Missing Colorectal Liver Metastases: An Initial Experience

BACKGROUND

Modern chemotherapy achieves the shrinking of colorectal cancer liver metastases (CRLM) to such extent that they may disappear from radiological imaging. Disappearing CRLM rarely represents a complete pathological remission and have an important risk of recurrence. Augmented reality (AR) consists in the fusion of real-time patient images with a computer-generated 3D virtual patient model created from pre-operative medical imaging. The aim of this prospective pilot study is to investigate the potential of AR navigation as a tool to help locate and surgically resect missing CRLM.

METHODS

A 3D virtual anatomical model was created from thoracoabdominal CT-scans using customary software (VR RENDER(®), IRCAD). The virtual model was superimposed to the operative field using an Exoscope (VITOM(®), Karl Storz, Tüttlingen, Germany). Virtual and real images were manually registered in real-time using a video mixer, based on external anatomical landmarks with an estimated accuracy of 5 mm. This modality was tested in three patients, with four missing CRLM that had sizes from 12 to 24 mm, undergoing laparotomy after receiving pre-operative oxaliplatin-based chemotherapy.

RESULTS

AR display and fine registration was performed within 6 min. AR helped detect all four missing CRLM, and guided their resection. In all cases the planned security margin of 1 cm was clear and resections were confirmed to be R0 by pathology. There was no postoperative major morbidity or mortality. No local recurrence occurred in the follow-up period of 6-22 months.

CONCLUSIONS

This initial experience suggests that AR may be a helpful navigation tool for the resection of missing CRLM.

A navigation system for open liver surgery: design, workflow and first clinical applications

BACKGROUND

The surgical treatment of liver tumours relies on precise localization of the lesions and detailed knowledge of the patient-specific vascular and biliary anatomy. Detailed three-dimensional (3D) anatomical information facilitates complete tumour removal while preserving a sufficient amount of functional liver tissue.

METHODS

We present an easy to use, clinically applicable navigation system for efficient visualization and tool guidance during liver surgery. Accurate instrument guidance within 3D planning models was achieved with a fast registration procedure, assuming a locally rigid and temporarily static scenario. After deformations occurring during the procedure, efficient means for registration updates are provided. Special focus was given to workflow integration and the minimization of overhead time. The navigation system was validated with nine clinical cases.

RESULTS

Navigated surgical interventions were performed with a median time overhead of 16.5 min. The navigation technology had a median accuracy of 6.3 mm, improving anatomical orientation and the detection of structures at risk.

CONCLUSIONS

Successful application of the navigation technology to open liver surgery was achieved by minimizing the procedural complexity and optimizing integration within the existing surgical environment. The assumption of locally rigid patient registration was validated, and clinical evaluation shows clear benefits for the surgeon.

On combining internal and external fiducials for liver motion compensation

This paper presents an in-vivo accuracy study on combining skin markers (external fiducials) and fiducial needles (internal fiducials) for motion compensation during liver interventions. We compared the target registration error (TRE) for different numbers of skin markers n(s) and fiducial needles n(f), as well as for different transformation types, in two swine using the tip of an additional tracked needle as the target. During continuous breathing, n(f) had the greatest effect on the accuracy, yielding mean root mean square (RMS) errors of 4.8 +/- 1.1 mm (n(f) = 0), 2.0 +/- 0.9 mm (n(f) = 1) and 1.7 +/- 0.8 mm (n(f) = 2) when averaged over multiple tool arrangements (n = 18, 36, 18) with n(s) = 4. These values correspond to error reductions of 11%, 64% and 70%, respectively, compared to the case when no motion compensation is performed, i.e., when the target position is assumed to be constant. At expiration, the mean RMS error ranged from 1.1 mm (n(f) = 0) to 0.8 mm (n(f) = 2), which is of the order of magnitude of the target displacement. Our study further indicates that the fiducial registration error (FRE) of a rigid transformation reflecting tissue motion generally correlates strongly with the TRE. Our findings could be used in practice to (1) decide on a suitable combination of fiducials for a given intervention, considering the trade-off between high accuracy and low invasiveness, and (2) provide an intra-interventional measure of confidence for the accuracy of the system based on the FRE.