F Werner1, C Jung2, M Hofmann1, R Werner3, J Salamon2, D Säring3, M G Kaul2, K Them1, O M Weber4, T Mummert2, G Adam2, H Ittrich2, T Knopp1. 1. Section for Biomedical Imaging, University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany and Institute for Biomedical Imaging, Hamburg University of Technology, Hamburg 21073, Germany. 2. Department of Diagnostic and Interventional Radiology, University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany. 3. Institute for Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany. 4. Philips Medical Systems DMC GmbH, Hamburg 22335, Germany.
Abstract
PURPOSE: Magnetic particle imaging (MPI) is a quantitative imaging modality that allows the distribution of superparamagnetic nanoparticles to be visualized. Compared to other imaging techniques like x-ray radiography, computed tomography (CT), and magnetic resonance imaging (MRI), MPI only provides a signal from the administered tracer, but no additional morphological information, which complicates geometry planning and the interpretation of MP images. The purpose of the authors' study was to develop bimodal fiducial markers that can be visualized by MPI and MRI in order to create MP-MR fusion images. METHODS: A certain arrangement of three bimodal fiducial markers was developed and used in a combined MRI/MPI phantom and also during in vivo experiments in order to investigate its suitability for geometry planning and image fusion. An algorithm for automated marker extraction in both MR and MP images and rigid registration was established. RESULTS: The developed bimodal fiducial markers can be visualized by MRI and MPI and allow for geometry planning as well as automated registration and fusion of MR-MP images. CONCLUSIONS: To date, exact positioning of the object to be imaged within the field of view (FOV) and the assignment of reconstructed MPI signals to corresponding morphological regions has been difficult. The developed bimodal fiducial markers and the automated image registration algorithm help to overcome these difficulties.
PURPOSE: Magnetic particle imaging (MPI) is a quantitative imaging modality that allows the distribution of superparamagnetic nanoparticles to be visualized. Compared to other imaging techniques like x-ray radiography, computed tomography (CT), and magnetic resonance imaging (MRI), MPI only provides a signal from the administered tracer, but no additional morphological information, which complicates geometry planning and the interpretation of MP images. The purpose of the authors' study was to develop bimodal fiducial markers that can be visualized by MPI and MRI in order to create MP-MR fusion images. METHODS: A certain arrangement of three bimodal fiducial markers was developed and used in a combined MRI/MPI phantom and also during in vivo experiments in order to investigate its suitability for geometry planning and image fusion. An algorithm for automated marker extraction in both MR and MP images and rigid registration was established. RESULTS: The developed bimodal fiducial markers can be visualized by MRI and MPI and allow for geometry planning as well as automated registration and fusion of MR-MP images. CONCLUSIONS: To date, exact positioning of the object to be imaged within the field of view (FOV) and the assignment of reconstructed MPI signals to corresponding morphological regions has been difficult. The developed bimodal fiducial markers and the automated image registration algorithm help to overcome these difficulties.
Authors: Franz Wegner; Kerstin Lüdtke-Buzug; Sjef Cremers; Thomas Friedrich; Malte M Sieren; Julian Haegele; Martin A Koch; Emine U Saritas; Paul Borm; Thorsten M Buzug; Joerg Barkhausen; Mandy Ahlborg Journal: Nanomaterials (Basel) Date: 2022-05-21 Impact factor: 5.719
Authors: Anna C Bakenecker; Mandy Ahlborg; Christina Debbeler; Christian Kaethner; Thorsten M Buzug; Kerstin Lüdtke-Buzug Journal: Innov Surg Sci Date: 2018-10-09
Authors: Matthias Graeser; Tobias Knopp; Patryk Szwargulski; Thomas Friedrich; Anselm von Gladiss; Michael Kaul; Kannan M Krishnan; Harald Ittrich; Gerhard Adam; Thorsten M Buzug Journal: Sci Rep Date: 2017-07-31 Impact factor: 4.379