A Platform Integrating Acquisition, Reconstruction, Visualization, and Manipulator Control Modules for MRI-Guided Interventions
ΣυγγραφέαςVelazco Garcia, Jose D.
Navkar, Nikhil V.
Morales, Cristina M.
Christoforou, Eftychios G.
Tsekos, Nikolaos V.
SourceJournal of Digital Imaging
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MetadataΕμφάνιση πλήρους εγγραφής
This work presents a platform that integrates a customized MRI data acquisition scheme with reconstruction and three-dimensional (3D) visualization modules along with a module for controlling an MRI-compatible robotic device to facilitate the performance of robot-assisted, MRI-guided interventional procedures. Using dynamically-acquired MRI data, the computational framework of the platform generates and updates a 3D model representing the area of the procedure (AoP). To image structures of interest in the AoP that do not reside inside the same or parallel slices, the MRI acquisition scheme was modified to collect a multi-slice set of intraoblique to each other sliceswhich are termed composing slices. Moreover, this approach interleaves the collection of the composing slices so the same k-space segments of all slices are collected during similar time instances. This time matching of the k-space segments results in spatial matching of the imaged objects in the individual composing slices. The composing slices were used to generate and update the 3D model of the AoP. The MRI acquisition scheme was evaluated with computer simulations and experimental studies. Computer simulations demonstrated that k-space segmentation and time-matched interleaved acquisition of these segments provide spatial matching of the structures imaged with composing slices. Experimental studies used the platform to image the maneuvering of an MRI-compatible manipulator that carried tubing filled with MRI contrast agent. In vivo experimental studies to image the abdomen and contrast enhanced heart on free-breathing subjects without cardiac triggering demonstrated spatial matching of imaged anatomies in the composing planes. The described interventional MRI framework could assist in performing real-time MRI-guided interventions.