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A Platform Integrating Acquisition, Reconstruction, Visualization, and Manipulator Control Modules for MRI-Guided Interventions

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Abstract

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 slices; which 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.

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Acknowledgements

This work was supported in part by the National Science Foundation grant CNS-1646566, a Greek Diaspora Fellowship granted by the Stavros Niarchos Foundation and administered by the Institute of International Education, as well as the Qatar National Research Fund award NPRP9-300-2-132 by Qatar Foundation.

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Authors and Affiliations

  1. Department of Computer Science, University of Houston, Houston, TX, USA

    Jose D. Velazco Garcia, Cristina M. Morales & Nikolaos V. Tsekos

  2. Department of Surgery, Hamad Medical Corporation, Doha, Qatar

    Nikhil V. Navkar, Julien Abinahed & Abdulla Al-Ansari

  3. GE Healthcare, Milwaukee, WI, USA

    Dawei Gui

  4. Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus

    Eftychios G. Christoforou

  5. Department of Biomedical Device Technologies, Acıbadem Mehmet Ali Aydınlar University, Istanbul, Turkey

    Alpay Ozcan

  6. C.J. Gorter Center for High Field MRI, Leiden University Medical Center, Leiden, Netherlands

    Andrew Webb

  7. Department of Medicine, Democritus University of Thrace, Alexandropoli, Greece

    Ioannis Seimenis

Authors
  1. Jose D. Velazco Garcia
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  2. Nikhil V. Navkar
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  3. Dawei Gui
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  4. Cristina M. Morales
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  5. Eftychios G. Christoforou
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  6. Alpay Ozcan
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  7. Julien Abinahed
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  8. Abdulla Al-Ansari
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  9. Andrew Webb
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  10. Ioannis Seimenis
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  11. Nikolaos V. Tsekos
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Correspondence to Nikolaos V. Tsekos.

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Velazco Garcia, J.D., Navkar, N.V., Gui, D. et al. A Platform Integrating Acquisition, Reconstruction, Visualization, and Manipulator Control Modules for MRI-Guided Interventions. J Digit Imaging 32, 420–432 (2019). https://doi.org/10.1007/s10278-018-0152-1

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  • DOI: https://doi.org/10.1007/s10278-018-0152-1

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