Teatini Andrea, Pérez de Frutos Javier, Eigl Benjamin, Pelanis Egidijus, Aghayan Davit L, Lai Marco, Kumar Rahul Prasanna, Palomar Rafael, Edwin Bjørn, Elle Ole Jakob
The Intervention Centre, Oslo University Hospital Rikshospitalet, Oslo, Norway.
Department of Informatics, University of Oslo, Oslo, Norway.
Minim Invasive Ther Allied Technol. 2021 Aug;30(4):229-238. doi: 10.1080/13645706.2020.1727524. Epub 2020 Mar 5.
This study aims to evaluate the accuracy of point-based registration (PBR) when used for augmented reality (AR) in laparoscopic liver resection surgery.
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 , 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.
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 study.
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 image-to-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.
本研究旨在评估基于点的配准（PBR）用于腹腔镜肝切除手术中的增强现实（AR）时的准确性。
本研究在三种不同场景下进行，在这些场景中PBR的采样目标准确性会降低：使用带有加工凹坑孔的评估模型、在计算机断层扫描（CT）中可见标记的患者特异性肝脏模型以及依靠外科医生的解剖学理解进行标注。使用五个随机选择的图像到患者配准位置计算目标配准误差（TRE）和基准配准误差（FRE）。
术中CT扫描的AR显示，加工模型的平均TRE为6.9毫米，患者特异性模型为7.9毫米，本研究中为13.4毫米。
在整个实验研究中，AR的TRE和FRE均有所增加，证明AR对用于计算图像到患者配准的目标采样准确性不稳健。此外，观察到待配准体积大小的影响。因此，建议减少由于标注导致的误差以及配准体积的大小，这可能会在AR系统中导致较大误差。
