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. DRR subimages around the tumor regions were cropped so that the subimage size was defined by tumor size. Training data were then classified into two groups: positive (including tumor) and negative (not including tumor) samples. Machine learning parameters were optimized by the extremely randomized tree method. For the tracking stage, a machine learning algorithm was generated to provide a tumor likelihood map using fluoroscopic images. Prior probability tumor positions were also calculated using the previous two frames. Tumor position was then estimated by calculating maximum probability on the tumor likelihood map and prior probability tumor positions.We acquired treatment planning 4DCT images in eight patients. Digital fluoroscopic imaging systems on either side of the vertical irradiation port allowed fluoroscopic image acquisition during treatment delivery. Each fluoroscopic dataset was acquired at 15 frames per second. We evaluated the tracking accuracy and computation times.Tracking positional accuracy averaged over all patients was 1.03 ± 0.34 mm (mean ± standard deviation, Euclidean distance) and 1.76 ± 0.71 mm (95 th percentile). Computation time was 28.66 ± 1.89 ms/frame averaged over all frames. Our markerless algorithm successfully estimated tumor position in real time.
Purpose: To perform the final quality assurance of our fluoroscopic-based markerless tumor tracking for gated carbon-ion pencil beam scanning (C-PBS) radiotherapy using a rotating gantry system, we evaluated the geometrical accuracy and tumor tracking accuracy using a moving chest phantom with simulated respiration. Methods: The positions of the dynamic flat panel detector (DFPD) and x-ray tube are subject to changes due to gantry sag. To compensate for this, we generated a geometrical calibration table (gantry flex map) in 15°gantry angle steps by the bundle adjustment method. We evaluated five metrics: (a) Geometrical calibration was evaluated by calculating chest phantom positional error using 2D/3D registration software for each 5°step of the gantry angle. (b) Moving phantom displacement accuracy was measured (AE10 mm in 1-mm steps) with a laser sensor. (c) Tracking accuracy was evaluated with machine learning (ML) and multi-template matching (MTM) algorithms, which used fluoroscopic images and digitally reconstructed radiographic (DRR) images as training data. The chest phantom was continuously moved AE10 mm in a sinusoidal path with a moving cycle of 4 s and respiration was simulated with AE5 mm expansion/contraction with a cycle of 2 s. This was performed with the gantry angle set at 0°, 45°, 120°, and 240°. (d) Four types of interlock function were evaluated: tumor velocity, DFPD image brightness variation, tracking anomaly detection, and tracking positional inconsistency in between the two corresponding rays. (e) Gate on/off latency, gating control system latency, and beam irradiation latency were measured using a laser sensor and an oscilloscope. Results: By applying the gantry flex map, phantom positional accuracy was improved from 1.03 mm/0.33°to <0.45 mm/0.27°for all gantry angles. The moving phantom displacement error was 0.1 mm. Due to long computation time, the tracking accuracy achieved with ML was <0.49 mm (=95% confidence interval [CI]) for imaging rates of 15 and 7.5 fps; those at 30 fps were decreased to 1.84 mm (95% CI: 1.79 mm-1.92 mm). The tracking positional accuracy with MTM was <0.52 mm (=95% CI) for all gantry angles and imaging frame rates. The tumor velocity interlock signal delay time was 44.7 ms (=1.3 frame). DFPD image brightness interlock latency was 34 ms (=1.0 frame). The tracking positional error was improved from 2.27 AE 2.67 mm to 0.25 AE 0.24 mm by the tracking anomaly detection interlock function. Tracking positional inconsistency interlock signal was output within 5.0 ms. The gate on/off latency was <82.7 AE 7.6 ms. The gating control system latency was <3.1 AE 1.0 ms. The beam irradiation latency was <8.7 AE 1.2 ms. Conclusions: Our markerless tracking system is now ready for clinical use. We hope to shorten the computation time needed by the ML algorithm at 30 fps in the future.
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