A Radiotherapy Positioning Method for Both Coarse Guidance and Precise Verification Based on Integration of AR and Optical Surface Imaging

Abstract

Traditional methods of radiotherapy positioning have shortcomings such as fragile skin-markers, additional doses and lack of information integration. Emerging technologies may provide alternatives for the relevant clinical practice. We proposed a noninvasive radiotherapy positioning method integrating augmented reality (AR) and optical surface, and evaluated its feasibility in clinical workflow. AR and structured light-based surface were integrated to implement the coarse-to-precise positioning through two coherent steps, i) the AR-based coarse guidance. To implement quality assurance, recognition of face and pattern was used for patient authentication, case association and accessory validation in AR scenes. The holographic images reconstructed from simulation computed tomography (CT) images, guided the initial posture correction by virtual-real alignment. ii) optical surface-based precise verification. The point clouds were fused, with the calibration and pose estimation of structured light cameras, and segmented according to the preset regions of interest (ROIs). The global-to-local registration for cross-source point clouds was achieved to calculate couch shifts in 6 degrees-of-freedom (DoF), which were ultimately transmitted to AR scenes. The evaluation based on phantom and human-body (4 volunteers) included, i) quality assurance workflow, ii) errors of both steps and correlation analysis, and iii) receiver operating characteristic (ROC). The maximum errors in phantom evaluation were 3.4±2.5 mm in Vrt and 1.4±1.0° in Pitch for the coarse guidance step, while 1.6±0.9 mm in Vrt and 0.6±0.4° in Pitch for the precise verification step. The Pearson correlation coefficients between precise verification and cone beam CT (CBCT) results were distributed in the interval [0.81, 0.85]. In ROC analysis, the areas under the curve (AUC) were 0.87 and 0.89 for translation and rotation respectively. In human body-based evaluation, the errors of thorax and abdomen (T&A) were significantly greater than those of head and neck (H&N) in Vrt (2.6±1.3 vs. 1.7±1.1, p<0.01), Lng (2.4±1.3 vs. 1.4±0.1, p<0.01) and Rtn (0.8±0.5 vs. 0.6±0.4, p = 0.03) while relatively similar in Lat (1.7±1.0 vs. 1.9±1.1, p = 0.13). The combination of AR and optical surface has utility and feasibility for patient positioning, in terms of both safety and accuracy.