A machine learning-based real-time tumor tracking system for fluoroscopic gating of lung radiotherapy.

To improve respiratory-gated radiotherapy accuracy, we developed a machine learning approach for markerless tumor tracking and evaluated it using lung cancer patient data. Digitally reconstructed radiography (DRR) datasets were generated using planning 4DCT data. Tumor positions were selected on respective DRR images to place the GTV center of gravity in the center of each DRR.

Regression model-based real-time markerless tumor tracking with fluoroscopic images for hepatocellular carcinoma.

Purpose: We have developed a new method to track tumor position using fluoroscopic images, and evaluated it using hepatocellular carcinoma case data.

Methods: Our method consists of a training stage and a tracking stage. In the training stage, the model data for the positional relationship between the diaphragm and the tumor are calculated using four-dimensional computed tomography (4DCT) data.

Real-time tumor tracking using fluoroscopic imaging with deep neural network analysis.

Purpose: To improve respiratory gating accuracy and treatment throughput, we developed a fluoroscopic markerless tumor tracking algorithm based on a deep neural network (DNN).

Methods: In the learning stage, target positions were projected onto digitally reconstructed radiography (DRR) images from four-dimensional computed tomography (4DCT). DRR images were cropped into subimages of the target or surrounding regions to build a network that takes input of the image pattern of subimages and produces a target probability map (TPM) for estimating the target position.