Kalia Megha, Mathur Prateek, Navab Nassir, Salcudean Septimiu E
Robotics and Control Lab, Electrical and Computer Engineering, University of British Columbia, 2329 West Mall, Vancouver, BC V6T 1Z4, Canada.
Computer Aided Medical Procedures, Technical University of Munich, Boltzmannstraße 15, 85748 Garching bei Múnchen, Germany.
Healthc Technol Lett. 2019 Nov 12;6(6):255-260. doi: 10.1049/htl.2019.0094. eCollection 2019 Dec.
Accurate medical Augmented Reality (AR) rendering requires two calibrations, a camera intrinsic matrix estimation and a hand-eye transformation. We present a unified, practical, marker-less, real-time system to estimate both these transformations during surgery. For camera calibration we perform calibrations at multiple distances from the endoscope, pre-operatively, to parametrize the camera intrinsic matrix as a function of distance from the endoscope. Then, we retrieve the camera parameters intra-operatively by estimating the distance of the surgical site from the endoscope in less than 1 s. Unlike in prior work, our method does not require the endoscope to be taken out of the patient; for the hand-eye calibration, as opposed to conventional methods that require the identification of a marker, we make use of a rendered tool-tip in 3D. As the surgeon moves the instrument and observes the offset between the actual and the rendered tool-tip, they can select points of high visual error and manually bring the instrument tip to match the virtual rendered tool tip. To evaluate the hand-eye calibration, 5 subjects carried out the hand-eye calibration procedure on a da Vinci robot. Average Target Registration Error of approximately 7mm was achieved with just three data points.
精确的医学增强现实（AR）渲染需要两次校准，即相机内参矩阵估计和手眼变换。我们提出了一个统一、实用、无标记的实时系统，用于在手术过程中估计这两种变换。对于相机校准，我们在术前从内窥镜的多个距离进行校准，将相机内参矩阵参数化为距内窥镜距离的函数。然后，我们在不到1秒的时间内通过估计手术部位与内窥镜的距离来术中检索相机参数。与先前的工作不同，我们的方法不需要将内窥镜从患者体内取出；对于手眼校准，与需要识别标记的传统方法不同，我们利用3D渲染的工具尖端。当外科医生移动器械并观察实际工具尖端与渲染工具尖端之间的偏移时，他们可以选择视觉误差较大的点，并手动将器械尖端调整到与虚拟渲染工具尖端匹配。为了评估手眼校准，5名受试者在达芬奇机器人上进行了手眼校准程序。仅用三个数据点就实现了约7毫米的平均目标配准误差。
