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

Abstract

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.

Real-Time 2D MR Cine From Beam Eye’s View With Tumor-Volume Projection to Ensure Beam-to-Tumor Conformality for MR-Guided Radiotherapy of Lung Cancer

“…Therefore, the tumor volume can be found with an acceptable tolerance when the diaphragm position has a precise match. As the diaphragm is a large object, uncertainties in its position determination are present ( 28 ). The sub-mm COM accuracy results have validated the assumption and method of segmenting the diaphragm used in this study.…”

Pre-treatment and real-time image guidance for a fixed-beam radiotherapy system

“…Recent advances in deep learning have improved the speed and geometric accuracy of directly tracking the tumor, while the marker-less tracking of surrogates such as the diaphragm has also been shown to be an accurate alternative. 34 Marker-less tracking would be particularly beneficial in a low-cost system such as the fixed-beam protype. The increase in computational complexity would be offset by the reduction in linac complexity, bunker size and shielding requirements for a fixed beam system.…”

Technical Note: 3D localization of lung tumors on cone beam CT projections via a convolutional recurrent neural network

“…However, the invasive implantation remains a daunting barrier for its wide acceptance in the clinic. A markerless approach is highly desirable and has been investigated intensively . With highly customized image pre‐processing and enhancement maneuvers, localization through registration based on cross‐correlation is successful when image contrast between tumor and its background is reasonable, but often fails at angles where tumor is obscured by mediastinum or ribs .…”

“…A markerless approach is highly desirable and has been investigated intensively. [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] With highly customized image pre-processing and enhancement maneuvers, localization through registration based on cross-correlation is successful when image contrast between tumor and its background is reasonable, but often fails at angles where tumor is obscured by mediastinum or ribs. 18 A potential solution is a markerless tracking scenario, which explores the large body of data collected in the marker-based setting, builds a framework to extract underlining non-marker features inside the tumor among a population of lung patients, and uses this information as a prior to help the detection and tracking of tumor in subsequent patients.…”