System Integration and In Vivo Testing of a Robot for Ultrasound Guidance and Monitoring During Radiotherapy

Abstract

We are developing a cooperatively controlled robot system for image-guided radiation therapy (IGRT) in which a clinician and robot share control of a 3-D ultrasound (US) probe. IGRT involves two main steps: 1) planning/simulation and 2) treatment delivery. The goals of the system are to provide guidance for patient setup and real-time target monitoring during fractionated radiotherapy of soft tissue targets, especially in the upper abdomen. To compensate for soft tissue deformations created by the probe, we present a novel workflow where the robot holds the US probe on the patient during acquisition of the planning computerized tomography image, thereby ensuring that planning is performed on the deformed tissue. The robot system introduces constraints (virtual fixtures) to help to produce consistent soft tissue deformation between simulation and treatment days, based on the robot position, contact force, and reference US image recorded during simulation. This paper presents the system integration and the proposed clinical workflow, validated by an in vivo canine study. The results show that the virtual fixtures enable the clinician to deviate from the recorded position to better reproduce the reference US image, which correlates with more consistent soft tissue deformation and the possibility for more accurate patient setup and radiation delivery.

Roboterbasierte Ultraschallsteuerung

“…Diverse Systeme zur robotergestützten Ultraschallbildgebung wurden in den letzten Jahren entwickelt, sowohl für diagnostische Zwecke (z. B. Karotis- [19], Beinvenen- [11] oder Leber-Scans [18]) als auch für interventionelle Anwendungsfälle (Nadelpunktionen [29] und Tumorverfolgung [24]). Die Vorteile der automatisierten Schallkopfführung liegen dabei vor allem in der Reproduzierbarkeit der Bilderfas- Ob sich ein solches System in den klinischen Alltag integrieren lässt, hängt außerdem von der Akzeptanz der Anwender und nicht zuletzt von der des Patien-ten ab.…”

Attention‐aware fully convolutional neural network with convolutional long short‐term memory network for ultrasound‐based motion tracking

“…Both the training and testing data involved 64 2D+t sequences; herein, the training size is 25, while the testing size is 39. The data had varying spatial (0.27-0.77 mm) and temporal resolution (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). The training data were annotated by CLUST as ground truth (center of blood vessels) of fiducial features throughout the acquisition sequence.…”

“…9,10 Assisted with the LINAC-compatible robotic-arm ultrasound imaging opens up the opportunity to acquire high temporal resolution images prior and during RT treatment. 11 Up to this point, majority approaches of object tracking involve a search over multiple window candidates based on a similarity measure, such as template matching. O'Shea et al 12 applied 3D cross-correlation block matching to track speckle in the ultrasound image and achieved high tracking accuracy at a cost of computational speed.…”

Evaluation of 2D and 3D ultrasound tracking algorithms and impact on ultrasound‐guided liver radiotherapy margins

“…A robotic arm offers the additional advantage of moving with the target organ or helping inexperienced users to find it through cooperative control. 41,42 Linear accelerator-based systems (LINAC) or adaptive targeting of the radiation beam using a multileaf collimator (MLC) or robotic treatment head, to follow the tumor motion during fractions, should take into consideration the treatment system latencies, including delays from the image acquisition, motion estimation algorithm, communication and control system, and beam delivery. 10,32,43 Therefore, the motion of the tumor should be accurately predicted for a sufficient time in the future to ensure the delivery of the radiation dose to the tumor and reduce the treatment margins.…”