In breast cancer screening, suspicious lesions may be found which need to be biopsied to check for malignancy. In the current gold standard this is done manually under MR-image guidance, which requires moving the patient table out of the MRI scanner due to accessibility constraints. When doing so, deformations of soft tissue due to e.g. breathing, muscle contractions and needle-tissue interactions significantly limit the targeting precision. Multiple biopsies may need to be extracted resulting in significant tissue damage, while sometimes the lesion is missed altogether requiring an additional biopsy or potentially resulting in a false negative result.
Within the RaM group at the University of Twente, a MR-compatible robotic platform has been developed to enable in-bore breast biopsies under MR image guidance. The Sunram 7 is the latest prototype with five degrees of freedom, actuated by MR safe pneumatic stepper motors developed at RaM.
Currently, the Sunram 7 is operated in a feed-forward manner which requires accurate calibration and characterization of the system. This approach is prone to errors for example when the motor would skip steps when overloaded, which would invalidate the last known position. This could be improved by establishing a feedback mechanism based on MRI such that the current pose of the end-effector can be measured, allowing to automatically calibrate the robot at start-up and correct positioning errors during the procedure.
The goal of this MSc thesis project is to develop a real-time tracking method based on MRI in order to derive bi-directional feedback and establish closed-loop control over the biopsy robot. The project will involve image processing, kinetic modeling and control theory. The experimental parts will be performed at the Siemens 1.5T MRI system located at the TechMed Centre.
This project will be co-supervised by Dr. Vincent Groenhuis (RaM) and Dr. Wyger Brink (Magenetic Detection & Imaging, TechMed Centre).
Requirements for this project:
- Fundamental knowledge of MRI imaging theory (course: imaging technique)
- Programming skills (image processing, data analysis)
- Lab skills (phantom preparation, 3D printing)
- Affinity with robotic systems and medical imaging.