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Predictive uncertainty in infrared marker‐based dynamic tumor tracking with Vero4DRT a)
Author(s) -
Akimoto Mami,
Nakamura Mitsuhiro,
Mukumoto Nobutaka,
Tanabe Hiroaki,
Yamada Masahiro,
Matsuo Yukinori,
Monzen Hajime,
Mizowaki Takashi,
Kokubo Masaki,
Hiraoka Masahiro
Publication year - 2013
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 - 10.1118/1.4817236
Subject(s) - tracking (education) , nuclear medicine , infrared , medical imaging , gold standard (test) , radiation therapy , computer science , artificial intelligence , medicine , optics , physics , surgery , radiology , psychology , pedagogy
Purpose: To quantify the predictive uncertainty in infrared (IR)‐marker‐based dynamic tumor tracking irradiation (IR Tracking) with Vero4DRT (MHI‐TM2000) for lung cancer using logfiles.Methods: A total of 110 logfiles for 10 patients with lung cancer who underwent IR Tracking were analyzed. Before beam delivery, external IR markers and implanted gold markers were monitored for 40 s with the IR camera every 16.7 ms and with an orthogonal kV x‐ray imaging subsystem every 80 or 160 ms. A predictive model [four‐dimensional (4D) model] was then created to correlate the positions of the IR markers ( P IR ) with the three‐dimensional (3D) positions of the tumor indicated by the implanted gold markers ( P detect ). The sequence of these processes was defined as 4D modeling. During beam delivery, the 4D model predicted the future 3D target positions ( P predict ) from the P IR in real‐time, and the gimbaled x‐ray head then tracked the target continuously. In clinical practice, the authors updated the 4D model at least once during each treatment session to improve its predictive accuracy. This study evaluated the predictive errors in 4D modeling ( E 4DM ) and those resulting from the baseline drift of P IR and P detect during a treatment session ( E BD ). E 4DM was defined as the difference between P predict and P detect in 4D modeling, and E BD was defined as the mean difference between P predict calculated from P IR in updated 4D modeling using (a) a 4D model created from training data before the model update and (b) an updated 4D model created from new training data.Results: The mean E 4DM was 0.0 mm with the exception of one logfile. Standard deviations of E 4DM ranged from 0.1 to 1.0, 0.1 to 1.6, and 0.2 to 1.3 mm in the left‐right (LR), anterior–posterior (AP), and superior–inferior (SI) directions, respectively. The median elapsed time before updating the 4D model was 13 (range, 2–33) min, and the median frequency of 4D modeling was twice (range, 2–3 times) per treatment session. E BD ranged from −1.0 to 1.0, −2.1 to 3.3, and −2.0 to 3.5 mm in the LR, AP, and SI directions, respectively. E BD was highly correlated with BD detect in the LR ( R = −0.83) and AP directions ( R = −0.88), but not in the SI direction ( R = −0.40). Meanwhile, E BD was highly correlated with BD IR in the SI direction ( R = −0.67), but not in the LR ( R = 0.15) or AP ( R = −0.11) direction. If the 4D model was not updated in the presence of intrafractional baseline drift, the predicted target position deviated from the detected target position systematically.Conclusions: Application of IR Tracking substantially reduced the geometric error caused by respiratory motion; however, an intrafractional error due to baseline drift of >3 mm was occasionally observed. To compensate for E BD , the authors recommend checking the target and IR marker positions constantly and updating the 4D model several times during a treatment session.