Abstract
Magnetic particle imaging (MPI) is a promising new in vivo medical imaging modality in which distributions of super-paramagnetic nanoparticles are tracked based on their response in an applied magnetic field. In this paper we provide a mathematical analysis of the modeled MPI operator in the univariate situation. We provide a Hilbert space setup, in which the MPI operator is decomposed into simple building blocks and in which these building blocks are analyzed with respect to their mathematical properties. In turn, we obtain an analysis of the MPI forward operator and, in particular, of its ill-posedness properties. We further get that the singular values of the MPI core operator decrease exponentially. We complement our analytic results by some numerical studies which, in particular, suggest a rapid decay of the singular values of the MPI operator.
| Originalsprache | Englisch |
|---|---|
| Zeitschrift | Inverse Problems |
| Jahrgang | 34 |
| Ausgabenummer | 5 |
| Seiten (von - bis) | 055012 |
| ISSN | 0266-5611 |
| DOIs | |
| Publikationsstatus | Veröffentlicht - 20.04.2018 |
Fördermittel
All authors of this article were involved in the activities of the DFG-funded scientific network MathMPI and thank the German Research Foundation for the support (ER777/1-1). Wolfgang Erb and Andreas Weinmann thank Karlheinz Gröchenig for a valuable discussion at the Dolomites Workshop on Approximation 2017 and the Rete Italiana di Approssimazione (RITA) for supporting a research visit in Padova. Tobias Knopp and Martin Möddel gratefully acknowledge the financial support of the German Research Foundation (DFG, grant number KN 1108/2-1) and the Federal Ministry of Education and Research (BMBF, grant number 05M16GKA). Martin Storath acknowledges support by the German Research Foundation DFG (STO1126/2-1). Andreas Weinmann acknowledges support by the German Research Foundation DFG (WE5886/4-1, WE5886/3-1).
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SDG 3 – Gesundheit und Wohlergehen
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SDG 4 – Qualitativ hochwertige Bildung
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SDG 5 – Gender Equality
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SDG 9 – Industrie, Innovation und Infrastruktur
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SDG 10 – Weniger Ungleichheiten
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SDG 16 – Frieden, Gerechtigkeit und verlässliche Institutionen
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Optimierte Datenakquisition für die Compressed-Sensing-basierte Bildrekonstruktion bei Magnetic Particle Imaging (MPI)
Buzug, T. (Projektleiter*in (PI)) & Mertins, A. (Projektleiter*in (PI))
01.01.14 → 31.12.19
Projekt: DFG Einzelprojekte › DFG Einzelförderungen (Sachbeihilfen)
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