Commissioning of a fluoroscopic‐based real‐time markerless tumor tracking system in a superconducting rotating gantry for carbon‐ion pencil beam scanning treatment. Issue 4 (23rd February 2019)

• Record Type: Journal Article
• Authors: Mori, Shinichiro; Sakata, Yukinobu; Hirai, Ryusuke; Furuichi, Wataru; Shimabukuro, Kazuki; Kohno, Ryosuke; Koom, Woong Sub; Kasai, Shigeru; Okaya, Keiko; Iseki, Yasushi
• Is Part Of: Medical physics. Volume 46:Issue 4(2019)
• Page Start: 1561
• Page End: 1574
• Publication Date: 2019-02-23
• Subjects: image guidance -- machine learning -- markerless tracking -- real‐time image processing -- rotating gantry -- scanned carbon‐ion beam; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.13403
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : 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.