This project aims to develop accurate and robust methods for tracking the motion of interventional devices during MRI-guided procedures. It will produce technologies for precise needle placement inside MRI scanners under real-time image guidance and control. This work will provide technology for minimally invasive image-guided therapy in a broad range of medical conditions, including cancer.
The specific aims are as follows: (Passive Fiducial Localization) To develop accurate and robust techniques for determining the position of needles and passive fiducials by detecting their characteristic susceptibility artifacts in MRI images. While fiducial segmentation has been demonstrated previously, the challenges in this work are rapid localization and high accuracy over a large range of motion. The susceptibility artifacts produced by different paramagnetic materials will be characterized under a variety of imaging conditions, in order to devise robust artifact segmentation and localization methods. (Real-Time Tracking) To develop real-time tracking algorithms?based on the passive fiducial detection developed in Aim 1?that require minimal image support and fast imaging to provide interactive navigation for maneuvering interventional devices and robotic mechanisms. This will be achieved by developing new line fiducial geometries, fast image acquisition protocols (in conjunction with Project 1 of the proposed research program) and fast fiducial segmentation algorithms. (System Integration) To integrate the passive fiducial tracking system (Aim 1) with real-time tracking and control (Aim 2) in an interactive planning and navigation software interface developed jointly with Projects 4 and 5 of this research program. Planning and navigation are closely coupled, and must be developed jointly. This work aims to advance the field of image-guided minimally invasive intervention by allowing the physician to maneuver within a specified surgical space with greater accuracy and confidence. The passive, image-based tracking technology will be applicable to a wide range of problems that require accurate registration of surgical devices to the image space, as well as real-time interventional tracking and navigation. While attractive for the visual-servo of robotic devices within a closed MRI-scanner, this approach will also provide valuable feedback for the positioning of manually actuated or hand-held devices. This work will provide technology that is applicable at a broad range of clinical sites, and will help to establish minimally invasive image-guided therapy more widely.
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