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Equilibrium Model with Anisotropy for Model-Based Reconstruction in Magnetic Particle Imaging (2403.00602v2)

Published 1 Mar 2024 in eess.IV, physics.comp-ph, and physics.med-ph

Abstract: 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 N\'eel 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.

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References (52)
  1. B. Gleich and J. Weizenecker, “Tomographic imaging using the nonlinear response of magnetic particles,” Nature, vol. 435, no. 7046, pp. 1214–1217, 2005.
  2. M. Graeser, T. Knopp, P. Szwargulski, T. Friedrich, A. von Gladiss, M. Kaul, K. M. Krishnan, H. Ittrich, G. Adam, and T. M. Buzug, “Towards picogram detection of superparamagnetic iron-oxide particles using a gradiometric receive coil,” Sci. Rep., vol. 7, no. 1, p. 6872, 2017.
  3. S. Herz, P. Vogel, T. Kampf, M. A. Rückert, S. Veldhoen, V. C. Behr, and T. A. Bley, “Magnetic particle imaging for quantification of vascular stenoses: A phantom study,” IEEE Trans. Med. Imaging, vol. 37, no. 1, pp. 61–67, 2018.
  4. M. G. Kaul, J. Salamon, T. Knopp, H. Ittrich, G. Adam, H. Weller, and C. Jung, “Magnetic particle imaging for in vivo blood flow velocity measurements in mice,” Phys. Med. Biol., vol. 63, no. 6, p. 064001, 2018.
  5. P. Vogel, M. A. Rückert, T. Kampf, S. Herz, A. Stang, L. Wöckel, T. A. Bley, S. Dutz, and V. C. Behr, “Superspeed bolus visualization for vascular magnetic particle imaging,” IEEE Trans. Med. Imaging, vol. 39, no. 6, pp. 2133–2139, 2020.
  6. W. Tong, Y. Zhang, H. Hui, X. Feng, B. Ning, T. Yu, W. Wang, Y. Shang, G. Zhang, S. Zhang, F. Tian, W. He, Y. Chen, and J. Tian, “Sensitive magnetic particle imaging of haemoglobin degradation for the detection and monitoring of intraplaque haemorrhage in atherosclerosis,” eBioMedicine, vol. 90, p. 104509, 2023.
  7. X. Zhu, J. Li, P. Peng, N. Hosseini Nassab, and B. R. Smith, “Quantitative drug release monitoring in tumors of living subjects by magnetic particle imaging nanocomposite,” Nano Lett., vol. 19, no. 10, pp. 6725–6733, 2019.
  8. X. Huang, H. Hui, W. Shang, P. Gao, Y. Zhou, W. Pang, C. M. Woo, J. Tian, and P. Lai, “Deep penetrating and sensitive targeted magnetic particle imaging and photothermal therapy of early-stage glioblastoma based on a biomimetic nanoplatform,” Adv. Sci., vol. 10, no. 19, p. 2300854, 2023.
  9. J. Salamon, M. Hofmann, C. Jung, M. G. Kaul, F. Werner, K. Them, R. Reimer, P. Nielsen, A. vom Scheidt, G. Adam, T. Knopp, and H. Ittrich, “Magnetic particle/magnetic resonance imaging: In-vitro MPI-guided real time catheter tracking and 4D angioplasty using a road map and blood pool tracer approach,” PloS one, vol. 11, no. 6, p. e0156899, 2016.
  10. J. Rahmer, D. Wirtz, C. Bontus, J. Borgert, and B. Gleich, “Interactive magnetic catheter steering with 3-D real-time feedback using multi-color magnetic particle imaging,” IEEE Trans. Med. Imaging, vol. 36, no. 7, pp. 1449–1456, 2017.
  11. S. Herz, P. Vogel, T. Kampf, P. Dietrich, S. Veldhoen, M. A. Rückert, R. Kickuth, V. C. Behr, and T. A. Bley, “Magnetic particle imaging–guided stenting,” J. Endovasc. Ther., vol. 26, no. 4, pp. 512–519, 2019.
  12. T. März and A. Weinmann, “Model-based reconstruction for magnetic particle imaging in 2D and 3D,” Inverse Probl. Imaging, vol. 10, no. 4, pp. 1087–1110, 2016.
  13. T. Kluth, B. Jin, and G. Li, “On the degree of ill-posedness of multi-dimensional magnetic particle imaging,” Inverse Probl., vol. 34, no. 9, p. 095006, 2018.
  14. W. Erb, A. Weinmann, M. Ahlborg, C. Brandt, G. Bringout, T. M. Buzug, J. Frikel, C. Kaethner, T. Knopp, T. März, M. Möddel, M. Storath, and A. Weber, “Mathematical analysis of the 1D model and reconstruction schemes for magnetic particle imaging,” Inverse Probl., vol. 34, no. 5, p. 055012, 2018.
  15. F. Thieben, M. Boberg, M. Graeser, and T. Knopp, “Efficient 3D drive-field characterization fo magnetic particle imaging systems,” Int. J. Magn. Part. Imag., vol. 8, no. 1 Suppl. 1, p. 2203015, 2022.
  16. J. Weizenecker, B. Gleich, J. Rahmer, and J. Borgert, “Micro-magnetic simulation study on the magnetic particle imaging performance of anisotropic mono-domain particles,” Phys. Med. Biol., vol. 57, no. 22, p. 7317, 2012.
  17. T. Kluth, “Mathematical models for magnetic particle imaging,” Inverse Probl., vol. 34, no. 8, p. 083001, 2018.
  18. J. Weizenecker, “The Fokker-Planck equation for coupled Brown-Néel-rotation,” Phys. Med. Biol., vol. 63, no. 3, p. 035004, 2018.
  19. H. Albers, T. Kluth, and T. Knopp, “Simulating magnetization dynamics of large ensembles of single domain nanoparticles: Numerical study of Brown/Néel dynamics and parameter identification problems in magnetic particle imaging,” J. Magn. Magn. Mater., vol. 541, p. 168508, 2022.
  20. T. Knopp, N. Gdaniec, and M. Möddel, “Magnetic particle imaging: From proof of principle to preclinical applications,” Phys. Med. Biol., vol. 62, no. 14, pp. R124–R178, 2017.
  21. J. Rahmer, J. Weizenecker, B. Gleich, and J. Borgert, “Signal encoding in magnetic particle imaging: properties of the system function,” BMC Med. Imaging, vol. 9, no. 4, pp. 1–21, 2009.
  22. T. Knopp, T. F. Sattel, S. Biederer, J. Rahmer, J. Weizenecker, B. Gleich, J. Borgert, and T. M. Buzug, “Model-based reconstruction for magnetic particle imaging,” IEEE Trans. Med. Imaging, vol. 29, no. 1, pp. 12–18, 2010.
  23. T. Knopp, S. Biederer, T. F. Sattel, J. Rahmer, J. Weizenecker, B. Gleich, J. Borgert, and T. M. Buzug, “2D model-based reconstruction for magnetic particle imaging,” Med. Phys., vol. 37, no. 2, pp. 485–491, 2010.
  24. P. W. Goodwill and S. M. Conolly, “The x-space formulation of the magnetic particle imaging process: One-dimensional signal, resolution, bandwidth, SNR, SAR, and magnetostimulation,” IEEE Trans. Med. Imaging, vol. 29, no. 11, pp. 1851–1859, 2010.
  25. L. R. Croft, P. W. Goodwill, and S. M. Conolly, “Relaxation in x-space magnetic particle imaging,” IEEE Trans. Med. Imaging, vol. 31, no. 12, pp. 2335–2342, 2012.
  26. T. Kluth, P. Szwargulski, and T. Knopp, “Towards accurate modeling of the multidimensional magnetic particle imaging physics,” New J. Phys., vol. 21, no. 10, p. 103032, 2019.
  27. H. Albers, T. Knopp, M. Möddel, M. Boberg, and T. Kluth, “Modeling the magnetization dynamics for large ensembles of immobilized magnetic nanoparticles in multi-dimensional magnetic particle imaging,” J. Magn. Magn. Mater., vol. 543, p. 168534, 2022.
  28. H. Albers, F. Thieben, M. Boberg, K. Scheffler, T. Knopp, and T. Kluth, “Model-based calibration and image reconstruction with immobilized nanoparticles,” Int. J. Magn. Part. Imag., vol. 9, no. 1 Suppl. 1, p. 2303002, 2023.
  29. T. Knopp, H. Albers, M. Grosser, M. Möddel, and T. Kluth, “Exploiting the Fourier neural operator for faster magnetization model evaluations based on the Fokker-Planck equation,” Int. J. Magn. Part. Imag., vol. 9, no. 1 Suppl. 1, p. 2303003, 2023.
  30. H. Albers and T. Kluth, “Immobilized nanoparticles with uniaxial anisotropy in multi-dimensional Lissajous-type excitation: An equilibrium model approach,” Int. J. Magn. Part. Imag., vol. 8, no. 1 Suppl. 1, p. 2203048, 2022.
  31. M. Maass, C. Droigk, M. Eulers, and A. Mertins, “An analytical equilibrium solution to the Néel relaxation Fokker-Planck equation,” Int. J. Magn. Part. Imag., vol. 8, no. 1 Suppl. 1, p. 2303027, 2022.
  32. C. Droigk, M. Maass, and A. Mertins, “Direct multi-dimensional Chebyshev polynomial based reconstruction for magnetic particle imaging,” Phys. Med. Biol., vol. 67, no. 4, p. 045014, 2022.
  33. C. Droigk, M. Maass, M. Eulers, and A. Mertins, “Adaption of direct Chebyshev reconstruction to an anisotropic particle model,” Int. J. Magn. Part. Imag., vol. 9, no. 1 Suppl. 1, p. 2303027, 2023.
  34. S. A. Shah, D. B. Reeves, R. M. Ferguson, J. B. Weaver, and K. M. Krishnan, “Mixed Brownian alignment and Néel rotations in superparamagnetic iron oxide nanoparticle suspensions driven by an ac field,” Phys. Rev. B, vol. 92, no. 9, p. 094438, Sep 2015.
  35. M. Graeser, K. Bente, A. Neumann, and T. M. Buzug, “Trajectory dependent particle response for anisotropic mono domain particles in magnetic particle imaging,” J. Phys. D: Appl. Phys., vol. 49, no. 4, p. 045007, 2016.
  36. A. Neumann and T. M. Buzug, “Simulations of magnetic particles with arbitrary anisotropies,” Int. J. Magn. Part. Imag., vol. 6, no. 2 Suppl. 1, p. 2009032, 2020.
  37. E. C. Stoner and E. P. Wohlfarth, “A mechanism of magnetic hysteresis in heterogeneous alloys,” Philos. Trans. R. Soc. A, vol. 240, no. 826, pp. 599–642, 1948.
  38. J. Weizenecker, J. Borgert, and B. Gleich, “A simulation study on the resolution and sensitivity of magnetic particle imaging,” Phys. Med. Biol., vol. 52, no. 21, pp. 6363–6374, 2007.
  39. P. J. Cregg and L. Bessais, “A single integral expression for the magnetisation of a textured superparamagnetic system,” J. Magn. Magn. Mater., vol. 203, no. 1–3, pp. 265–267, 1999.
  40. ——, “Series expansions for the magnetisation of a solid superparamagnetic system of non-interacting particles with anisotropy,” J. Magn. Magn. Mater., vol. 202, no. 2–3, pp. 554–564, 1999.
  41. D. Borwein, J. M. Borwein, and R. E. Crandall, “Effective Laguerre asymptotics,” SIAM J. Numer. Anal., vol. 46, no. 6, pp. 3285–3312, 2008.
  42. H. Albers, T. Kluth, and T. Knopp, “MNPDynamics: A computational toolbox for simulating magnetic moment behavior of ensembles of nanoparticles,” Int. J. Magn. Part. Imag., vol. 6, no. 2 Suppl. 1, p. 2009020, 2020.
  43. J. Rahmer, J. Weizenecker, B. Gleich, and J. Borgert, “Analysis of a 3-D system function measured for magnetic particle imaging,” IEEE Trans. Med. Imaging, vol. 31, no. 6, pp. 1289–1299, 2012.
  44. M. Maass and A. Mertins, “On the representation of magnetic particle imaging in Fourier space,” Int. J. Magn. Part. Imag., vol. 6, no. 1, p. 912001, 2020.
  45. M. Maass, C. Droigk, M. Eulers, and A. Mertins, “A system function component model for magnetic particle imaging with anisotropic particles,” Int. J. Magn. Part. Imag., vol. 9, no. 1 Suppl. 1, p. 2303076, 2023.
  46. M. Möddel, F. Griese, T. Kluth, and T. Knopp, “Estimating the spatial orientation of immobilized magnetic nanoparticles with parallel-aligned easy axes,” Phys. Rev. Applied, vol. 16, no. 4, p. L041003, 2021.
  47. T. Knopp, N. Gdaniec, R. Rehr, M. Gräser, and T. Gerkmann, “Correction of linear system drifts in magnetic particle imaging,” Phys. Med. Biol., vol. 64, no. 12, p. 125013, 2019.
  48. T. Kluth and B. Jin, “Enhanced reconstruction in magnetic particle imaging by whitening and randomized SVD approximation,” Phys. Med. Biol., vol. 64, no. 12, p. 125026, 2019.
  49. T. Knopp, J. Rahmer, T. F. Sattel, S. Biederer, J. Weizenecker, B. Gleich, J. Borgert, and T. M. Buzug, “Weighted iterative reconstruction for magnetic particle imaging,” Phys. Med. Biol., vol. 55, no. 6, pp. 1577–1589, 2010.
  50. T. Knopp and K. Scheffler, “MPIData: EquilibriumModelWithAnisotropy,” Feb. 2024. [Online]. Available: https://doi.org/10.5281/zenodo.10646064
  51. M. Maass, C. Droigk, and A. Mertins, “A novel representation of the MPI system function,” Int. J. Magn. Part. Imag., vol. 6, no. 1 Suppl. 1, p. 2009050, 2020.
  52. M. Graeser, K. Bente, and T. M. Buzug, “Dynamic single-domain particle model for magnetite particles with combined crystalline and shape anisotropy,” J. Phys. D: Appl. Phys., vol. 48, no. 27, p. 275001, 2015.

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