Dynamic Sparse Mask Spatial-Frequency System Matrix Compression for Accelerated Reconstruction in Narrowband Magnetic Particle Imaging.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	Dynamic Sparse Mask Spatial-Frequency System Matrix Compression for Accelerated Reconstruction in Narrowband Magnetic Particle Imaging.
Συγγραφείς:	Bian Z, Chen Z, Li J, Zhu T, Shi G, Zhang H, Li G, An Y, Tian J
Πηγή:	IEEE transactions on bio-medical engineering [IEEE Trans Biomed Eng] 2026 Aug; Vol. 73 (8), pp. 2940-2952.
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 , Data Compression*/methods, Signal-To-Noise Ratio ; Algorithms ; Phantoms, Imaging ; Humans
Περίληψη:	Objective: Narrowband magnetic particle imaging (MPI), owing to its inherently high signal-to-noise ratio (SNR), has shown strong potential for enabling large-field-of-view (FOV) scanners suitable for human-scale imaging. System-matrix (SM)-based reconstruction can effectively suppress image blurring and negative artifacts. However, enlarging the FOV greatly increases the SM size, leading to prohibitive memory usage and prolonged reconstruction time. Methods: To address this challenge, we propose a Spatial-Frequency System Matrix (SF-SM) approach that exploits the sparse representation of calibration responses in the discrete cosine transform (DCT) domain to achieve substantial matrix reduction. Subsequently, a Dynamic Sparse Mask (DSM) strategy is introduced to retain the most informative spatial-frequency components: a global prior mask removes consistently low-contribution frequency components, while a signal-driven mask derived from the measured harmonic image captures object-dependent spectral characteristics. Their intersection serves to extract a task-adaptive specific SM that retains informative components and suppresses noise-dominated high-frequency terms. Results: Extensive validation using simulation studies, a public dataset, and an in-house narrowband MPI scanner demonstrates that the proposed approach compresses the SM to approximately 1% of its original size in the best cases, accelerates reconstruction by nearly two orders of magnitude, and maintains or even enhances image quality relative to the Raw-SM. Conclusion: The proposed DSM-based SF-SM enables substantial SM compression and highly efficient reconstruction while preserving image quality. Significance: DSM-based SF-SM provides an efficient and scalable solution for accelerating narrowband MPI reconstruction, particularly in large-FOV systems and applications requiring real-time imaging performance.
Entry Date(s):	Date Created: 20260527 Date Completed: 20260810 Latest Revision: 20260929
Update Code:	20260930
DOI:	10.1109/TBME.2026.3697397
PMID:	42202196
Βάση Δεδομένων:	MEDLINE

Περιγραφή
ISSN:	1558-2531
DOI:	10.1109/TBME.2026.3697397