Intrafractional tracking accuracy in infrared marker-based hybrid dynamic tumour-tracking irradiation with a gimballed linac

Mukumoto, Nobutaka; Nakamura, Mitsuhiro; Yamada, Masahiro; Takahashi, Kunio; Tanabe, Hiroaki; Yano, Shinsuke; Miyabe, Yuki; Ueki, N.; Kaneko, Shuji; Matsuo, Yukinori; Sawada, Akira; Kitajima, Masaki; Hiraoka, Masahiro

doi:10.1016/j.radonc.2014.02.018

Cited by 30 publications

(52 citation statements)

References 16 publications

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“…We previously showed that the intrafractional prediction errors in overall treatment were nearly identical to the training error for various respiratory motions. 13 However, in the present study, we found large differences between the Abbreviations: LR, left-right; CC, cranio-caudal; AP, anterior-posterior; 3D, three dimension. training error and the intrafractional prediction error when a 2,000 ms sampling interval was employed.…”

Section: Discussioncontrasting

confidence: 62%

“…The second motion was acquired during beam delivery to reconstruct the 4D model, improving predictive accuracy. 7,13 The interval between the first and second respiratory motion acquisitions was 12.6 AE 3.1 min. Table I shows characteristics of the first and second respiratory motions.…”

Section: A Respiratory Motionsmentioning

confidence: 99%

“…These are the positions and velocities of the infrared-reflective markers used as the surrogate signal, expressed as follows 13 :…”

Section: B 4d Modeling Of Dynamic Tumor-tracking Using the Vero4drtmentioning

confidence: 99%

“…The 4D model is expressed by a quadratic equation involving two variables: the position and the velocity of the infrared-reflective markers. 13 To compensate for the system delay of dynamic tumor-tracking, the future positions of the infrared-reflective markers are predicted linearly from the past motion of the infrared-reflective markers. Subsequently, the gimbal-mounted X-ray head tracks the moving target by predicting its future position from the displacements of infrared-reflective markers located on the abdominal wall.…”

Section: Introductionmentioning

confidence: 99%

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Impact of sampling interval in training data acquisition on intrafractional predictive accuracy of indirect dynamic tumor‐tracking radiotherapy

et al. 2017

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Purpose: To explore the effect of sampling interval of training data acquisition on the intrafractional prediction error of surrogate signal-based dynamic tumor-tracking using a gimbal-mounted linac. Materials and methods: Twenty pairs of respiratory motions were acquired from 20 patients (ten lung, five liver, and five pancreatic cancer patients) who underwent dynamic tumor-tracking with the Vero4DRT. First, respiratory motions were acquired as training data for an initial construction of the prediction model before the irradiation. Next, additional respiratory motions were acquired for an update of the prediction model due to the change of the respiratory pattern during the irradiation. The time elapsed prior to the second acquisition of the respiratory motion was 12.6 AE 3.1 min. A four-axis moving phantom reproduced patients' three dimensional (3D) target motions and one dimensional surrogate motions. To predict the future internal target motion from the external surrogate motion, prediction models were constructed by minimizing residual prediction errors for training data acquired at 80 and 320 ms sampling intervals for 20 s, and at 500, 1,000, and 2,000 ms sampling intervals for 60 s using orthogonal kV x-ray imaging systems. The accuracies of prediction models trained with various sampling intervals were estimated based on training data with each sampling interval during the training process. The intrafractional prediction errors for various prediction models were then calculated on intrafractional monitoring images taken for 30 s at the constant sampling interval of a 500 ms fairly to evaluate the prediction accuracy for the same motion pattern. In addition, the first respiratory motion was used for the training and the second respiratory motion was used for the evaluation of the intrafractional prediction errors for the changed respiratory motion to evaluate the robustness of the prediction models. Results: The training error of the prediction model was 1.7 AE 0.7 mm in 3D for all sampling intervals. The intrafractional prediction error for the same motion pattern was 1.9 AE 0.7 mm in 3D for an 80 ms sampling interval, which increased larger than 1 mm in 10.0% of prediction models trained at a 2,000 ms sampling interval with a significant difference (P < 0.01) and up to 2.5% for the other sampling intervals without a significant difference (P > 0.05). The intrafractional prediction error for the changed respiratory motion pattern increased to 5.1 AE 2.4 mm in 3D for an 80 ms sampling interval; however, there was not a significant difference in the robustness of the prediction model between the 80 ms sampling interval and other sampling intervals (P > 0.05). Conclusions: Although the training error of the prediction model was consistent for the all sampling intervals, the prediction model using the larger sampling interval of the 2,000 ms increased the intrafractional prediction error for the same motion pattern. The realistic accuracy of the prediction model was difficult to estimate using the larger sa...

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Section: Discussioncontrasting

confidence: 62%

Section: A Respiratory Motionsmentioning

confidence: 99%

“…These are the positions and velocities of the infrared-reflective markers used as the surrogate signal, expressed as follows 13 :…”

Section: B 4d Modeling Of Dynamic Tumor-tracking Using the Vero4drtmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact of sampling interval in training data acquisition on intrafractional predictive accuracy of indirect dynamic tumor‐tracking radiotherapy

et al. 2017

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“…Our previous study, which evaluated the initial 10 patients from the present study cohort, confirmed high accuracies in the tracking performance. The 95th percentiles of overall tracking errors were 1.3 mm, 2.4 mm and 1.4 mm in left-right, cranio-caudal and anterior-posterior directions, respectively [22].…”

Section: Discussionmentioning

confidence: 91%

Evaluation of dynamic tumour tracking radiotherapy with real-time monitoring for lung tumours using a gimbal mounted linac

Matsuo

Ueki

Takayama

et al. 2014

Radiotherapy and Oncology

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Direct measurement and correction of both megavoltage and kilovoltage scattered x‐rays for orthogonal kilovoltage imaging subsystems with dual flat panel detectors

Iramina

Nakamura

Mizowaki

2020

J Applied Clin Med Phys

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Purpose To measure the scattered x‐rays of megavoltage (MV) and kilovoltage (kV) beams (MV scatter and kV scatter, respectively) on the orthogonal kV imaging subsystems of Vero4DRT. Methods Images containing MV‐ and kV‐scatter from another source only (i.e., MV‐ and kV‐scatter maps) were acquired for each investigated flat panel detector. The reference scatterer was a water‐equivalent cuboid phantom. The maps were acquired by changing one of the following parameters from the reference conditions while keeping the others fixed: field size: 10.0 × 10.0 cm2; dose rate: 400 MU/min; gantry and ring angles: 0°; kV collimator aperture size at isocenter: 10.0 × 10.0 cm2: tube voltage: 110 kV; and exposure: 0.8 mAs. The average pixel values of MV‐ and kV‐scatter (i.e., the MV‐ and kV‐scatter values) at the center of each map were calculated and normalized to the MV‐scatter value under the reference conditions (MV‐ and kV‐scatter value factor, respectively). In addition, an MV‐ and kV‐scatter correction experiment with intensity‐modulated beams was performed using a phantom with four gold markers (GMs). The ratios between the intensities of the GMs and those of their surroundings were calculated. Results The measurements showed a strong dependency of the MV‐scatter on the field size and dose rate. The maximum MV‐scatter value factors were 2.0 at a field size of 15.0 × 15.0 cm2 and 2.5 at a dose rate of 500 MU/min. The maximum kV‐scatter value was 0.48 with a fully open kV collimator aperture. In the phantom experiment, the intensity ratios of kV images with MV‐ and kV‐scatter were decreased from the reference ones. After correction of kV‐scatter only, MV‐scatter only, and both MV‐ and kV‐scatter, the intensity ratios gradually improved. Conclusions MV‐ and kV‐scatter could be corrected by subtracting the scatter maps from the projections, and the correction improved the intensity ratios of the GMs.

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Intrafractional tracking accuracy in infrared marker-based hybrid dynamic tumour-tracking irradiation with a gimballed linac

Abstract: Prediction errors were the primary cause of overall targeting errors, whereas mechanical errors were negligible. Furthermore, improvement of the prediction accuracy could be achieved by correcting systematic prediction errors in the previous field.

Cited by 30 publications

References 16 publications

Impact of sampling interval in training data acquisition on intrafractional predictive accuracy of indirect dynamic tumor‐tracking radiotherapy

Impact of sampling interval in training data acquisition on intrafractional predictive accuracy of indirect dynamic tumor‐tracking radiotherapy

Evaluation of dynamic tumour tracking radiotherapy with real-time monitoring for lung tumours using a gimbal mounted linac

Direct measurement and correction of both megavoltage and kilovoltage scattered x‐rays for orthogonal kilovoltage imaging subsystems with dual flat panel detectors

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