Real-time direct diaphragm tracking using kV imaging on a standard linear accelerator.

Purpose As the predominant driver of respiratory motion, the diaphragm represents a key surrogate for motion management during the irradiation of thoracic cancers. Existing approaches to diaphragm tracking often produce phase-based estimates, suffer from lateral side failures or are not executable in real-time. In this paper, we present an algorithm that continuously produces real-time estimates of three-dimensional (3D) diaphragm position using kV images acquired on a standard linear accelerator. Methods Patient-specific 3D diaphragm models were generated via automatic segmentation on end-exhale four-dimensional-computed tomography (4D-CT) images. The estimated trajectory of diaphragmatic motion, referred to as the principal motion vector, was obtained by registering end-exhale to end-inhale 4D-CT images. Two-dimensional (2D) diaphragm masks were generated by forward-projecting 3D models over the complement of angles spanned during kV image acquisition. For each kV image, diaphragm position was determined by shifting angle-matched 2D masks along the principal motion vector and selecting the position of highest contrast on a vertical difference image. Retrospective analysis was performed using 22 cone beam CT (CBCT) image sequences for six lung cancer patients across two datasets. Given the current lack of objective ground truth for diaphragm position, our algorithm was evaluated by examining its ability to track implanted markers. Simple linear regression was used to construct 3D marker motion models and estimation errors were computed as the difference between estimated and ground truth marker positions. Additionally, Pearson correlation coefficients were used to characterize diaphragm-marker correlation. Results The mean +/- standard deviation of the estimation errors across all image sequences was -0.1 +/- 0.7 mm, -0.1 +/- 1.8 mm and 0.2 +/- 1.4 mm in the LR, SI, and AP directions respectively. The 95th percentile of the absolute errors ranged over 0.5-3.1 mm, 1.6-6.7 mm, and 1.2-4.0 mm in the LR, SI, and AP directions, respectively. The mean +/- standard deviation of diaphragm-marker correlations over all image sequences was -0.07 +/- 0.57, 0.67 +/- 0.49, and 0.29 +/- 0.52 in the LR, SI, and AP directions, respectively. Diaphragm-marker correlation was observed to be highly dependent on marker position. Mean correlation along the SI axis ranged over 0.91-0.93 for markers situated in the lower lobes of the lung, while correlations ranging over -0.51-0.79 were observed for markers situated in the upper and middle lobes. Conclusion This work advances a new approach to real-time direct diaphragm tracking in realistic treatment scenarios. By achieving continuous estimates of diaphragmatic motion, the proposed method has applications for both markerless tumor tracking and respiratory binning in 4D-CBCT.