Adaptive and Robust Frequency Selection Framework in Calibration-Based Magnetic Particle Imaging Reconstruction.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	Adaptive and Robust Frequency Selection Framework in Calibration-Based Magnetic Particle Imaging Reconstruction.
Συγγραφείς:	Zhu T, Zhang H, Wei Z, Yang X, Tian J, Hui H
Πηγή:	IEEE transactions on bio-medical engineering [IEEE Trans Biomed Eng] 2026 Sep; Vol. 73 (9), pp. 3235-3248.
Τύπος έκδοσης:	Journal Article
Γλώσσα:	English
Στοιχεία περιοδικού:	Publisher: Institute Of Electrical And Electronics Engineers Country of Publication: United States NLM ID: 0012737 Publication Model: Print Cited Medium: Internet ISSN: 1558-2531 (Electronic) Linking ISSN: 00189294 NLM ISO Abbreviation: IEEE Trans Biomed Eng Subsets: MEDLINE
Imprint Name(s):	Publication: New York, NY : Institute Of Electrical And Electronics Engineers Original Publication: New York, IEEE Professional Technical Group on Bio-Medical Engineering.
Ιατρικοί όροι (MeSH):	Magnetic Particle Imaging/methods , Image Processing, Computer-Assisted/methods, Phantoms, Imaging ; Calibration ; Signal-To-Noise Ratio ; Algorithms ; Signal Processing, Computer-Assisted
Περίληψη:	Objective: Frequency selection is a crucial step for calibration-based magnetic particle imaging (MPI) reconstruction, enabling improvement in computational efficiency and noise suppression. Current methods combine signal-to-noise ratio (SNR) feature with a selection threshold. However, the selection threshold determination is experience-dependent, and the utilization of the system matrix (SM) and the imaging phantom signal is insufficient. Method: To suppress these issues, an adaptive and robust frequency selection framework (AR-FSF) is proposed, including three modules: (i) Velocity-corrected feature calculation, which limits feature calculation to the calibration points with high field-free-region velocity, (ii) Adaptive threshold calculation, which adaptively calculates the noise level using the feature spectrum, (iii) Forward-backward selection, which selects high-SNR frequency components for both SM and imaging phantom for reconstruction. Results: Signal experiments validate the effectiveness and the robustness of the introduced modules respectively. Reconstruction experiments further validate that the AR-FSF method can provide a simple and robust frequency selection process for reconstruction. In experiments using in-house data, the AR-FSF method provides suitable frequency components for fast and high-quality imaging, requiring a minimum reconstruction time of 4.5% compare to current methods. Conclusion: The proposed AR-FSF method effectively simplifies the frequency selection process, enabling adaptive selection of frequency component for different phantoms, thereby achieving fast and high-quality reconstruction. Significance: The AR-FSF method simplifies the frequency component selection process and can be widely applied in calibration-based MPI reconstruction, laying a methodological foundation for future biomedical applications.
Entry Date(s):	Date Created: 20251230 Date Completed: 20260828 Latest Revision: 20260828
Update Code:	20260829
DOI:	10.1109/TBME.2025.3649711
PMID:	41468342
Βάση Δεδομένων:	MEDLINE

Περιγραφή
ISSN:	1558-2531
DOI:	10.1109/TBME.2025.3649711