Motion in radiotherapy: photon therapy

Korreman, S.

doi:10.1088/0031-9155/57/23/r161

Cited by 135 publications

(116 citation statements)

References 137 publications

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“…Management of respiratory motion is an important component in the workflow of thoracic and abdominal radiotherapy 1, 2, 3. 4D CT incorporates the patient's respiratory information into a stack of 3D images such that the sequential snapshot images at different respiratory phases can be reconstructed 4, 5.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the combined use of two different respiratory monitoring systems for 4D CT simulation and gated treatment

Liu

Lin

Fan

et al. 2018

J Applied Clin Med Phys

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PurposeTwo different respiratory monitoring systems (Varian's Real‐Time Position Management (RPM) System and Siemens’ ANZAI belt Respiratory Gating System) are compared in the context of respiratory signals and 4D CT images that are accordingly reconstructed. This study aims to evaluate the feasibility of combined use of RPM and ANZAI systems for 4DCT simulation and gated radiotherapy treatment, respectively.MethodsThe RPM infrared reflecting marker and the ANZAI belt pressure sensor were both placed on the patient's abdomen during 4DCT scans. The respiratory signal collected by the two systems was synchronized. Fifteen patients were enrolled for respiratory signal collection and analysis. The discrepancies between the RPM and ANZAI traces can be characterized by phase shift and shape distortion. To reveal the impact of the changes in respiratory signals on 4D images, two sets of 4D images based on the same patient's raw data were reconstructed using the RPM and ANZAI data for phase sorting, respectively. The volume of whole lung and the position of diaphragm apex were measured and compared for each respiratory phase.ResultsThe mean phase shift was measured as 0.2 ± 0.1 s averaged over 15 patients. The shape of the breathing trace was found to be in disagreement. For all the patients, the ANZAI trace had a steeper falloff in exhalation than RPM. The inhalation curve, however, was matched for nine patients, steeper in ANZAI for five patients and steeper in RPM for one patient. For 4D image comparison, the difference in whole‐lung volume was about −4% to +4% and the difference in diaphragm position was about −5 mm to +4 mm, compared in each individual phase and averaged over seven patients.ConclusionsCombined use of one system for 4D CT simulation and the other for gated treatment should be avoided as the resultant gating window would not fully match with each other due to the remarkable discrepancy in breathing traces acquired by the two different surrogate systems.

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Section: Introductionmentioning

confidence: 99%

Evaluation of the combined use of two different respiratory monitoring systems for 4D CT simulation and gated treatment

Liu

Lin

Fan

et al. 2018

J Applied Clin Med Phys

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“…Real-time imaging is necessary in such treatment, as organ motion [2] may occur quickly and at any instant aside from respiratory motion. With real-time 4d visualization, tracking and rapid compensation of motion can be automated through a robotic solution, thus increasing efficiency while decreasing inconvenience.…”

Section: Motivementioning

confidence: 99%

Robotic 4D ultrasound solution for real-time visualization and teleoperation

Al-Badri

Ipsen

Böttger

et al. 2017

Current Directions in Biomedical Engineering

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Automation of the image acquisition process via robotic solutions offer a large leap towards resolving ultrasound's user-dependency. This paper, as part of a larger project aimed to develop a multipurpose 4d-ultrasonic forcesensitive robot for medical applications, focuses on achieving real-time remote visualisation for 4d ultrasound image transfer. This was possible through implementing our software modification on a GE Vivid 7 Dimension workstation, which operates a matrix array probe controlled by a KUKA LBR iiwa 7 7-DOF robotic arm. With the help of robotic positioning and the matrix array probe, fast volumetric imaging of target regions was feasible. By testing ultrasound volumes, which were roughly 880 kB in size, while using gigabit Ethernet connection, a latency of ~57 ms was achievable for volume transfer between the ultrasound station and a remote client application, which as a result allows a frame count of 17.4 fps. Our modification thus offers for the first time real-time remote visualization, recording and control of 4d ultrasound data, which can be implemented in teleoperation.

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“…1,2 If respiratory-induced uncertainties persist, the field size will enlarge, causing damage to healthy tissues surrounding the tumor. 3,4 Several techniques, including forced shallow breathing, breath-holding, respiratory gating and dynamic tumor-tracking, have been proposed to reduce respiratoryinduced uncertainties.…”

Section: Introductionmentioning

confidence: 99%

“…3,4 Several techniques, including forced shallow breathing, breath-holding, respiratory gating and dynamic tumor-tracking, have been proposed to reduce respiratoryinduced uncertainties. 1,2 Recent interest has focused on the dynamic tumor-tracking technique, which can dynamically reposition the radiation field in line with the position of the moving target. [5][6][7][8][9] One advantage of dynamic tumor-tracking is minimization of internal uncertainties without burdening patient respiration or prolonging treatment time.…”

Section: Introductionmentioning

confidence: 99%

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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Motion in radiotherapy: photon therapy

Cited by 135 publications

References 137 publications

Evaluation of the combined use of two different respiratory monitoring systems for 4D CT simulation and gated treatment

Evaluation of the combined use of two different respiratory monitoring systems for 4D CT simulation and gated treatment

Robotic 4D ultrasound solution for real-time visualization and teleoperation

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

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