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Item type:Publication, Access status: with files , Efficient Chebyshev reconstruction for the anisotropic equilibrium model in magnetic particle imaging(Institute of Physics (IOP), 2026-10-15); ; ; ; Objective. Magnetic particle imaging (MPI) is a tomographic imaging modality capable of real-time, high-sensitivity mapping of superparamagnetic iron oxide nanoparticles. Model-based image reconstruction provides an alternative to conventional methods that rely on a measured system matrix, eliminating the need for laborious calibration measurements. Nevertheless, model-based approaches must account for the complexities of the imaging chain to maintain high image quality. This work investigates and accelerates an adapted direct Chebyshev reconstruction (DCR) method that accounts for the physical effect of magnetic nanoparticle anisotropy. Approach. The adapted DCR method employs weighted Chebyshev polynomials in the frequency domain and incorporates a spatially variant deconvolution step to compensate for magnetic anisotropy effects. The method is evaluated on five experimental phantoms and compared with model-based and measured system matrix reconstructions. In addition, an efficient approximation of the spatially variant deconvolution is introduced to reduce computational runtime and memory consumption while maintaining reconstruction accuracy. The proposed approach is further investigated on simulated three-dimensional data to assess its scalability to volumetric MPI. Main results. The adapted DCR method significantly improves reconstruction quality in real measurements and achieves image fidelity comparable to that of the measured system matrix reconstruction and superior to that of the model-based system matrix reconstruction. The proposed approximation of the spatially variant deconvolution preserves the reconstruction accuracy while reducing computational and memory requirements. Its computational complexity is reduced to 𝓞 (𝘕log𝘕). The method also successfully reconstructs simulated 3D data, demonstrating its applicability to volumetric MPI. Significance. The results demonstrate the potential of the adapted DCR approach to improve model-based MPI reconstruction while avoiding the need for a system matrix. The efficient deconvolution approximation makes the method particularly attractive for high-resolution and three-dimensional imaging by substantially improving its computational efficiency and scalability.Publicationtype: Journal Article7 4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Equilibrium model with anisotropy for model-based reconstruction in magnetic particle imaging(IEEE, 2024-11-01); ; ; ; Magnetic particle imaging is a tracer-based tomographic imaging technique that allows the concentration of magnetic nanoparticles to be determined with high spatio-temporal resolution. To reconstruct an image of the tracer concentration, the magnetization dynamics of the particles must be accurately modeled. A popular ensemble model is based on solving the Fokker-Plank equation, taking into account either Brownian or Néel dynamics. The disadvantage of this model is that it is computationally expensive due to an underlying stiff differential equation. A simplified model is the equilibrium model, which can be evaluated directly but in most relevant cases it suffers from a non-negligible modeling error. In the present work, we investigate an extended version of the equilibrium model that can account for particle anisotropy. We show that this model can be expressed as a series of Bessel functions, which can be truncated based on a predefined accuracy, leading to very short computation times, which are about three orders of magnitude lower than equivalent Fokker-Planck computation times. We investigate the accuracy of the model for 2D Lissajous magnetic particle imaging sequences and show that the difference between the Fokker-Planck and the equilibrium model with anisotropy is sufficiently small so that the latter model can be used for image reconstruction on experimental data with only marginal loss of image quality, even compared to a system matrix-based reconstruction.Publicationtype: Journal Article13 7