Accurate Concentration Recovery for Quantitative Magnetic Particle Imaging Reconstruction via Nonconvex Regularization

Zhu, Tao; Yin, Lin; He, Jie; Wei, Zechen; Yang, Xin; Tian, Jie; Hui, Hui

doi:10.1109/tmi.2024.3383468

Cited by 3 publications

(2 citation statements)

References 49 publications

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“…Additionally, regularization may introduce a reconstruction bias, leading to a systematic discrepancy between true and reconstructed tracer distribution. (Nawwas et al 2021, Zhu et al 2024 proposed a method to mitigate this bias.…”

Section: Discussionmentioning

confidence: 99%

A fused LASSO operator for fast 3D magnetic particle imaging reconstruction

He,

Li,

Zhang

et al. 2024

Phys. Med. Biol.

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Objective. Magnetic particle imaging (MPI) is a promising imaging modality that leverages the nonlinear magnetization behavior of superparamagnetic iron oxide nanoparticles to determine their concentration distribution. Previous optimization models with multiple regularization terms have been proposed to achieve high-quality MPI reconstruction, but these models often result in increased computational burden, particularly for dense gridding 3D fields of view. In order to achieve faster reconstruction speeds without compromising reconstruction quality, we have developed a novel fused LASSO operator, total sum-difference (TSD), which effectively captures the sparse and smooth priors of MPI images. Methods. Through an analysissynthesis equivalence strategy and a constraint smoothing strategy, the TSD regularized model was solved using the fast iterative soft-thresholding algorithm (FISTA). The resulting reconstruction method, TSD-FISTA, boasts low computational complexity and quadratic convergence rate over iterations. Results. Experimental results demonstrated that TSD-FISTA required only 10% and 37% of the time to achieve comparable or superior reconstruction quality compared to commonly used fused LASSO-based ADMM and Tikhonov-based algebraic reconstruction techniques, respectively. Significance. TSD-FISTA shows promise for enabling real-time 3D MPI reconstruction at high frame rates for large fields of view.

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Section: Discussionmentioning

confidence: 99%

A fused LASSO operator for fast 3D magnetic particle imaging reconstruction

He,

Li,

Zhang

et al. 2024

Phys. Med. Biol.

Self Cite

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“…To achieve fast reconstruction, it is important that the reconstruction speed and quality is high enough for the MPI system to run continuously. In MPI, reconstruction methods can be mainly divided into x-space methods (Li et al 2024), which yield images of limited quality, and system matrix methods (Knopp et al 2010, Storath et al 2016, Lieb and Knopp 2021, Zhu et al 2024. The system matrix reconstruction method enables reconstruction of MPI signals with rapid sampling trajectories, such as Lissajous sequences, into high-quality MPI images.…”

Section: Introductionmentioning

confidence: 99%

A greedy regularized block Kaczmarz method for accelerating reconstruction in magnetic particle imaging

Shen,

Zhang,

Zhang

et al. 2024

Phys. Med. Biol.

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Objective. Magnetic Particle Imaging (MPI) is an emerging medical tomographic imaging modality that enables real-time imaging with high sensitivity and high spatial and temporal resolution. For the system matrix reconstruction method, the MPI reconstruction problem is an ill-posed inverse problem that is commonly solved using the Kaczmarz algorithm. However, the high computation time of the Kaczmarz algorithm, which restricts MPI reconstruction speed, has limited the development of potential clinical applications for real-time MPI. In order to achieve fast reconstruction in real-time MPI, we propose a greedy regularized block Kaczmarz method (GRBK) which accelerates MPI reconstruction. Approach. GRBK is composed of a greedy partition strategy for the system matrix, which enables preprocessing of the system matrix into well-conditioned blocks to facilitate the convergence of the block Kaczmarz algorithm, and a regularized block Kaczmarz algorithm, which enables fast and accurate MPI image reconstruction at the same time. Main results. We quantitatively evaluated our GRBK using simulation data from three phantoms at 20dB, 30dB, and 40dB noise levels. The results showed that GRBK can improve reconstruction speed by single orders of magnitude compared to the prevalent regularized Kaczmarz algorithm including Tikhonov regularization, the non-negative Fused Lasso, and wavelet-based sparse model. We also evaluated our method on OpenMPIData, which is real MPI data. The results showed that our GRBK is better suited for real-time MPI reconstruction than current state-of-the-art reconstruction algorithms in terms of reconstruction speed as well as image quality. Significance. Our proposed method is expected to be the preferred choice for potential applications of real-time MPI.

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