Influence of sampling accuracy on augmented reality for laparoscopic image-guided surgery

Abstract

Purpose: This study aims to evaluate the accuracy of point-based registration (PBR) when used for augmented reality (AR) in laparoscopic liver resection surgery. Material and methods: The study was conducted in three different scenarios in which the accuracy of sampling targets for PBR decreases: using an assessment phantom with machined divot holes, a patient-specific liver phantom with markers visible in computed tomography (CT) scans and in vivo, relying on the surgeon's anatomical understanding to perform annotations. Target registration error (TRE) and fiducial registration error (FRE) were computed using five randomly selected positions for image-to-patient registration. Results: AR with intra-operative CT scanning showed a mean TRE of 6.9 mm for the machined phantom, 7.9 mm for the patient-specific phantom and 13.4 mm in the in vivo study. Conclusions: AR showed an increase in both TRE and FRE throughout the experimental studies, proving that AR is not robust to the sampling accuracy of the targets used to compute imageto-patient registration. Moreover, an influence of the size of the volume to be register was observed. Hence, it is advisable to reduce both errors due to annotations and the size of registration volumes, which can cause large errors in AR systems.

Mixed reality as a novel tool for diagnostic and surgical navigation in orthopaedics

“…Registration was performed through singular value decomposition using seven target positions (similarly to [ 10 ]). The targets used for the patient phantom are metallic washers (six millimetres of diameter, also shown in [ 10 ]), glued to the patient phantom, which were visible in the CT scan and were segmented and clustered using fuzzy means classification. The metallic washers are replaceable by any hypo- or hyperintense markers, visible in CT (e.g.…”

“…To dynamically update the position of the MR while the HoloLens or the patient is moved during diagnostics, the following equation (according to [ 10 ]) was used: In which, O is the coordinate system for the OTS, M for the HoloLens optical markers, I is the image coordinate space, C is the HoloLens camera, P is the origin of the patient space coordinates (in our study, the OTS origin was used as global reference) and C is the camera pose. Through image-to-patient registration and the reference frame on the patient, the holographic model can be aligned to the patient’s body and dynamically updated while moving the limb by changing , as shown in Fig.…”

“…For this reason, the ground truth positions for the 23 metallic washers in the coordinate system of the OTS are not as accurate as the divot holes of the Validation phantom (as described in [ 10 ]). Furthermore, since transformation is not a measured transformation (as per the Validation phantom ), to compute the ground truth positions of the metallic washers in optical tracking system coordinates (O), the patient optical marker-plate spheres and pins were clustered from the CT scan, and were used to compute .…”

“…To dynamically update the position of the MR while the HoloLens or the patient is moved during diagnostics, the following equation (according to [10]) was used:…”

Augmented Reality and Image-Guided Robotic Liver Surgery

“…Furthermore, AR in hepatic surgery has showed a delayed distribution compared to other surgical fields as neurosurgery, otolaryngology, orthopedics, and maxillofacial surgery [ 69 , 70 , 71 ]. This difference comes from anatomical obstacles, such as working with a deformable soft organ that is constantly moving during operation because of respiratory cycles as well as pneumoperitoneum creation [ 72 ]. Although some strategies have been described in this context [ 23 , 73 , 74 ], these features make the development of AR more complex, and new software are needed for shortening modeling creation and improving the accuracy of manual, semiautomatic, and automatic images overlapping.…”