Cooperative control with ultrasound guidance for radiation therapy

Hasan Tutkun Şen, Alexis Cheng, Kai Ding, Emad Boctor, John Wong, Iulian Iordachita, Peter Kazanzides

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Article number49
JournalFrontiers Robotics AI
Volume3
Issue numberAUG
DOIs
StatePublished - Aug 1 2016

Fingerprint

Radiotherapy
Ultrasonics
Tissue
Linear accelerators
Radiation
Planning
Computerized tomography
Monitoring
Tomography
Cones
Feedback
Imaging techniques

Keywords

  • Cooperative control
  • Human-in-the-loop image servoing
  • Robot-assisted radiotherapy
  • Ultrasound guided radiotherapy
  • Virtual fixtures

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence

Cite this

Cooperative control with ultrasound guidance for radiation therapy. / Şen, Hasan Tutkun; Cheng, Alexis; Ding, Kai; Boctor, Emad; Wong, John; Iordachita, Iulian; Kazanzides, Peter.

In: Frontiers Robotics AI, Vol. 3, No. AUG, 49, 01.08.2016.

Research output: Contribution to journalArticle

Şen, Hasan Tutkun ; Cheng, Alexis ; Ding, Kai ; Boctor, Emad ; Wong, John ; Iordachita, Iulian ; Kazanzides, Peter. / Cooperative control with ultrasound guidance for radiation therapy. In: Frontiers Robotics AI. 2016 ; Vol. 3, No. AUG.
@article{e7939791319d4a4f84dc614460d549b4,
title = "Cooperative control with ultrasound guidance for radiation therapy",
abstract = "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.",
keywords = "Cooperative control, Human-in-the-loop image servoing, Robot-assisted radiotherapy, Ultrasound guided radiotherapy, Virtual fixtures",
author = "Şen, {Hasan Tutkun} and Alexis Cheng and Kai Ding and Emad Boctor and John Wong and Iulian Iordachita and Peter Kazanzides",
year = "2016",
month = "8",
day = "1",
doi = "10.3389/frobt.2016.00049",
language = "English (US)",
volume = "3",
journal = "Frontiers Robotics AI",
issn = "2296-9144",
publisher = "Frontiers Media S. A.",
number = "AUG",

}

TY - JOUR

T1 - Cooperative control with ultrasound guidance for radiation therapy

AU - Şen, Hasan Tutkun

AU - Cheng, Alexis

AU - Ding, Kai

AU - Boctor, Emad

AU - Wong, John

AU - Iordachita, Iulian

AU - Kazanzides, Peter

PY - 2016/8/1

Y1 - 2016/8/1

N2 - 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.

AB - 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.

KW - Cooperative control

KW - Human-in-the-loop image servoing

KW - Robot-assisted radiotherapy

KW - Ultrasound guided radiotherapy

KW - Virtual fixtures

UR - http://www.scopus.com/inward/record.url?scp=85061907404&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85061907404&partnerID=8YFLogxK

U2 - 10.3389/frobt.2016.00049

DO - 10.3389/frobt.2016.00049

M3 - Article

VL - 3

JO - Frontiers Robotics AI

JF - Frontiers Robotics AI

SN - 2296-9144

IS - AUG

M1 - 49

ER -