In this thesis, we propose a cooperative robot control methodology that provides real-time ultrasound-based guidance in the direct manipulation paradigm for image-guided radiation therapy (IGRT) in which a clinician and robot share control of a 3D ultrasound (US) probe. IGRT involves two main steps: (1) planning/simulation and (2) treatment delivery. The proposed US probe co-manipulation methodology has two goals. The first goal is to provide guidance to the therapists for patient setup on the treatment delivery days based on the robot position, contact force, and reference US image recorded during simulation. The second goal is the real-time target monitoring during fractionated radiotherapy of soft tissue targets, especially in the upper abdomen. We provide the guidance in the form of virtual fixtures, which are software-generated force and position signals applied to human operators that permit the operators to perform physical interactions, yet retain direct control of the task. The co-manipulation technique is used to locate soft-tissue targets with US imaging for radiotherapy, enabling therapists with minimal US experience to find an US image which has previously been identified by an expert sonographer on the planning day. Moreover, to compensate for soft tissue deformations created by the probe, we propose a novel clinical workflow where a robot holds the US probe on the patient during acquisition of the planning computerized tomography (CT) image, thereby ensuring that planning is performed on the deformed tissue. Our results show that the proposed cooperative control technique with virtual fixtures and US image feedback can significantly reduce the time it takes to find the reference US images, can provide more accurate US probe placement compared to finding the images free hand, and finally, can increase the accuracy of the patient setup, and thus, the radiation therapy.
H. Tutkun Şen received his B.S. degree in Mechanical Engineering with a double major in Electrical and Electronics Engineering from Middle East Technical University, Turkey in 2009 and 2010, respectively. In addition, he obtained a Master of Science in Computer Science from Johns Hopkins University in 2015. He has been a Michael J. Zinner Fellow (Brown Challenge Fellow in the Whiting School of Engineering) since 2010. He has been pursuing a Ph.D. in the department of Computer Science at Johns Hopkins University, advised by Dr. Peter Kazanzides and Dr. Russ Taylor since 2009. After completion of his PhD, Tutkun will begin work as a Control Systems Engineer at Verb Surgical Inc. in Mountain View, CA, where he will be responsible for performing system analysis and designing controllers for a new medical robotic system.