International Journal on Magnetic Particle Imaging IJMPI
Vol. 9 No. 1 Suppl 1 (2023): Int J Mag Part Imag
https://doi.org/10.18416/IJMPI.2023.2303035
A physics-informed deep learning framework in multi-color magnetic particle signal analysis
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Abstract
Magnetic particle imaging (MPI) is an emerging and highly sensitive imaging method. Multi-color MPI allows simultaneous identification of different materials. Obtaining precise relaxation time is one of the key challenges in achieving multi-colored MPI. In this paper, we propose a physical information based deep learning framework to accurately decompose the mixed signal into the original independent relaxation signals. By transforming the Debye relaxation model into a differential loss function, our network is able to efficiently utilize physical prior information. In simulation experiments with different signal-to-noise ratios and different signal counts, our method shows better performance than the PDCO algorithm. The imaging effect of our algorithm and PDCO algorithm in the presence of multiple materials was evaluated by three-color imaging simulation experiment. In addition, spectral imaging of a digital vascular phantom was simulated by combining a field-free point with homogeneous pulsed excitation. In vascular phantom simulation experiment, our method images blood vessels, metal guidewires, and stents in a single imaging process, showing excellent application potential in cardiac stent surgery.
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References
C. Lu, L. Han, J. Wang et al., “Engineering of magnetic nanoparticles as magnetic particle imaging tracers,” Chem. Soc. Rev., vol. 50, no. 14, pp. 8102-8146, 2021.
Berman P, Levi O, Parmet Y, et al. Laplace inversion of low?resolution NMR relaxometry data using sparse representation methods[J]. Concepts in Magnetic Resonance Part A, 2013, 42(3): 72-88.
Croft L R, Goodwill P W, Konkle J J, et al. Low drive field amplitude for improved image resolution in magnetic particle imaging[J]. Medical physics, 2016, 43(1): 424-435.
Lu L, Jin P, Pang G, et al. Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators[J]. Nature Machine Intelligence, 2021, 3(3): 218-229.