IEEE transactions on bio-medical engineering · 2026
Dynamic Sparse Mask Spatial-Frequency System Matrix Compression for Accelerated Reconstruction in Narrowband Magnetic Particle Imaging.
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Bian Zhongwei, Chen Ziwei, Li Jiasheng, Zhu Tao, Shi Gen, Zhang Haoran, Li Guanghui, An Yu, Tian Jie
Аннотация
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.
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.