Fully automatic tracking of native knee kinematics from stereo-radiography with digitally reconstructed radiographs

计算机视觉 人工智能 射线照相术 运动学 计算机科学 成像体模 医学 核医学 放射科 物理 经典力学
作者
William S. Burton,Casey A. Myers,Margareta Stefanovic,Kevin B. Shelburne,Paul J. Rullkoetter
出处
期刊:Journal of Biomechanics [Elsevier BV]
卷期号:166: 112066-112066 被引量:2
标识
DOI:10.1016/j.jbiomech.2024.112066
摘要

Precise measurement of joint-level motion from stereo-radiography facilitates understanding of human movement. Conventional procedures for kinematic tracking require significant manual effort and are time intensive. The current work introduces a method for fully automatic tracking of native knee kinematics from stereo-radiography sequences. The framework consists of three computational steps. First, biplanar radiograph frames are annotated with segmentation maps and key points using a convolutional neural network. Next, initial bone pose estimates are acquired by solving a polynomial optimization problem constructed from annotated key points and anatomic landmarks from digitized models. A semidefinite relaxation is formulated to realize the global minimum of the non-convex problem. Pose estimates are then refined by registering computed tomography-based digitally reconstructed radiographs to masked radiographs. A novel rendering method is also introduced which enables generating digitally reconstructed radiographs from computed tomography scans with inconsistent slice widths. The automatic tracking framework was evaluated with stereo-radiography trials manually tracked with model-image registration, and with frames which capture a synthetic leg phantom. The tracking method produced pose estimates which were consistently similar to manually tracked values; and demonstrated pose errors below 1.0 degree or millimeter for all femur and tibia degrees of freedom in phantom trials. Results indicate the described framework may benefit orthopaedics and biomechanics applications through acceleration of kinematic tracking.
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