Telerobotic system concept for real‐time soft‐tissue imaging during radiotherapy beam delivery

Schlosser, Jeffrey; Salisbury, Kenneth; Hristov, Dimitre

doi:10.1118/1.3515457

Cited by 66 publications

(62 citation statements)

References 32 publications

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“…This is of particular importance not only for treatment systems with longer system latencies but also with the realization of more complex image-guided radiotherapy systems under development that are subject to larger system latencies. [26][27][28][29] RMSE for diaphragm respiratory prediction was higher than abdominal wall respiratory prediction RMSE. This is potentially due to the lower sampling rate of diaphragm respiratory data (5 Hz) compared to abdominal wall respiratory data (30 Hz); a previous study by Ren et al found that a lower sampling rate resulted in a reduction in prediction accuracy.…”

Section: Discussionmentioning

confidence: 98%

Audiovisual biofeedback improves motion prediction accuracy

et al. 2013

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Purpose:The accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities present in the patients' respiratory pattern. Audiovisual (AV) biofeedback has been shown to reduce respiratory irregularities. The aim of this study was to test the hypothesis that AV biofeedback improves the accuracy of motion prediction. Methods: An AV biofeedback system combined with real-time respiratory data acquisition and MR images were implemented in this project. One-dimensional respiratory data from (1) the abdominal wall (30 Hz) and (2) the thoracic diaphragm (5 Hz) were obtained from 15 healthy human subjects across 30 studies. The subjects were required to breathe with and without the guidance of AV biofeedback during each study. The obtained respiratory signals were then implemented in a kernel density estimation prediction algorithm. For each of the 30 studies, five different prediction times ranging from 50 to 1400 ms were tested (150 predictions performed). Prediction error was quantified as the root mean square error (RMSE); the RMSE was calculated from the difference between the real and predicted respiratory data. The statistical significance of the prediction results was determined by the Student's t-test. Results: Prediction accuracy was considerably improved by the implementation of AV biofeedback. Of the 150 respiratory predictions performed, prediction accuracy was improved 69% (103/150) of the time for abdominal wall data, and 78% (117/150) of the time for diaphragm data. The average reduction in RMSE due to AV biofeedback over unguided respiration was 26% (p < 0.001) and 29% (p < 0.001) for abdominal wall and diaphragm respiratory motion, respectively. Conclusions: This study was the first to demonstrate that the reduction of respiratory irregularities due to the implementation of AV biofeedback improves prediction accuracy. This would result in increased efficiency of motion management techniques affected by system latencies used in radiotherapy.

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Section: Discussionmentioning

confidence: 98%

Audiovisual biofeedback improves motion prediction accuracy

et al. 2013

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“…Two telerobotic systems for US monitoring of radiotherapy were developed by Schlosser et al (2010Schlosser et al ( , 2011 at Stanford University, with the latter being commercialized by SoniTrack Systems (Palo Alto, CA, USA). Another telerobotic research system for US monitoring of radiotherapy was developed at Lubeck University by Kuhlemann (2013) and has been tested on the hearts of healthy human subjects.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Control with Ultrasound Guidance for Radiation Therapy

et al. 2016

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Radiation therapy typically begins with the acquisition of a CT scan of the patient for planning, followed by multiple days where radiation is delivered according to the plan. This requires that the patient be reproducibly positioned (set up) on the radiation therapy device (linear accelerator) such that the radiation beams pass through the target. Modern linear accelerators provide cone-beam computed tomography (CBCT) imaging, but this does not provide sufficient contrast to discriminate many abdominal soft-tissue targets, and therefore patient setup is often done by aligning bony anatomy or implanted fiducials. Ultrasound (US) can be used to both assist with patient setup and to provide real-time monitoring of soft-tissue targets. However, one challenge is that the ultrasound probe contact pressure can deform the target area and cause discrepancies with the treatment plan. Another challenge is that radiation therapists typically do not have ultrasound experience and therefore cannot easily find the target in the US image. We propose cooperative control strategies to address both the challenges. First, we use cooperative control with virtual fixtures (VFs) to enable acquisition of a planning CT that includes the soft-tissue deformation. Then, for the patient setup during the treatment sessions, we propose to use real-time US image feedback to dynamically update the VFs; this co-manipulation strategy provides haptic cues that guide the therapist to correctly place the US probe. A phantom study is performed to demonstrate that the co-manipulation strategy enables inexperienced operators to quickly and accurately place the probe on a phantom to reproduce a desired reference image. This is a necessary step for patient setup and, by reproducing the reference image, creates soft-tissue deformations that are consistent with the treatment plan, thereby enabling real-time monitoring during treatment delivery.

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“…[9][10][11][12] In addition, the radiation therapy LINAC does not affect speckle tracking algorithms, 13 while commercially-available optical tracking systems may be used to relate speckle tracking coordinates to the treatment room coordinate system. 14,15 For subtle probe adjustments that may be required during treatment, a haptic teleoperated robotic system was introduced by Schlosser et al 16 with minimal treatment plan modifications required to accommodate the robot and US probe. Wu et al 17 additionally demonstrated that the presence of the US probe for transabdominal imaging minimally affects radiotherapy treatment plans when the anterior-posterior treatment beam is omitted.…”

Section: Introductionmentioning

confidence: 99%

In vivo reproducibility of robotic probe placement for a novel ultrasound-guided radiation therapy system

et al. 2014

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Abstract. Ultrasound can provide real-time image guidance of radiation therapy, but the probe-induced tissue deformations cause local deviations from the treatment plan. If placed during treatment planning, the probe causes streak artifacts in required computed tomography (CT) images. To overcome these challenges, we propose robot-assisted placement of an ultrasound probe, followed by replacement with a geometrically identical, CT-compatible model probe. In vivo reproducibility was investigated by implanting a canine prostate, liver, and pancreas with three 2.38-mm spherical markers in each organ. The real probe was placed to visualize the markers and subsequently replaced with the model probe. Each probe was automatically removed and returned to the same position or force. Under position control, the median three-dimensional reproducibility of marker positions was 0.6 to 0.7 mm, 0.3 to 0.6 mm, and 1.1 to 1.6 mm in the prostate, liver, and pancreas, respectively. Reproducibility was worse under force control. Probe substitution errors were smallest for the prostate (0.2 to 0.6 mm) and larger for the liver and pancreas (4.1 to 6.3 mm), where force control generally produced larger errors than position control. Results indicate that position control is better than force control for this application, and the robotic approach has potential, particularly for relatively constrained organs and reproducibility errors that are smaller than established treatment margins.

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Telerobotic system concept for real‐time soft‐tissue imaging during radiotherapy beam delivery

Abstract: Remotely controlled robotic U.S. imaging is feasible in the radiotherapy environment and for the first time may offer real-time volumetric soft-tissue guidance concurrent with radiotherapy delivery.

Cited by 66 publications

References 32 publications

Audiovisual biofeedback improves motion prediction accuracy

Audiovisual biofeedback improves motion prediction accuracy

Cooperative Control with Ultrasound Guidance for Radiation Therapy

In vivo reproducibility of robotic probe placement for a novel ultrasound-guided radiation therapy system

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