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Commissioning of a fluoroscopic‐based real‐time markerless tumor tracking system in a superconducting rotating gantry for carbon‐ion pencil beam scanning treatment
Author(s) -
Mori Shinichiro,
Sakata Yukinobu,
Hirai Ryusuke,
Furuichi Wataru,
Shimabukuro Kazuki,
Kohno Ryosuke,
Koom Woong Sub,
Kasai Shigeru,
Okaya Keiko,
Iseki Yasushi
Publication year - 2019
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13403
Subject(s) - imaging phantom , optics , dosimetry , pencil beam scanning , quality assurance , physics , computer science , artificial intelligence , materials science , nuclear medicine , computer vision , beam (structure) , proton therapy , engineering , medicine , operations management , external quality assessment
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 (±10 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 ±10 mm in a sinusoidal path with a moving cycle of 4 s and respiration was simulated with ±5 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 ± 2.67 mm to 0.25 ± 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 ± 7.6 ms. The gating control system latency was <3.1 ± 1.0 ms. The beam irradiation latency was <8.7 ± 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.