Geometry planning and image registration in magnetic particle imaging using bimodal fiducial markers. Issue 6 (30th November 2016)

• Record Type: Journal Article
• Authors: Werner, F.; Jung, C.; Hofmann, M.; Werner, R.; Salamon, J.; Säring, D.; Kaul, M. G.; Them, K.; Weber, O. M.; Mummert, T.; Adam, G.; Ittrich, H.; Knopp, T.
• Is Part Of: Medical physics. Volume 43:Issue 6(2016)
• Page Start: 2884
• Page End: 2893
• Publication Date: 2016-11-30
• Subjects: biomedical MRI -- image fusion -- image registration -- magnetic particles -- medical image processing -- nanomagnetics -- nanomedicine -- nanoparticles -- object detection -- phantoms -- superparamagnetism; Magnetic resonance imaging -- Registration -- Nanotechnologies‐applications; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Nano‐structures -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Nano‐biotechnology or nano‐medicine, e.g. protein engineering or drug delivery -- Nano‐magnetism, e.g. magnetoimpedance, anisotropic magnetoresistance, giant magnetoresistance or tunneling magnetoresistance; registration -- MPI -- MRI -- fiducialmarker -- geometry planning; Image registration -- Medical image reconstruction -- Image scanners -- Medical magnetic resonance imaging -- Flow visualization -- Medical image spatial resolution -- Spatial resolution -- Medical X‐ray imaging; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4948998
• Languages: English
• ISSNs: 0094-2405

Abstract

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.