Simulation research on magneto-acoustic concentration tomography of magnetic nanoparticles based on truncated singular value decomposition (TSVD)

The existing magneto-acoustic concentration tomography with magnetic induction (MACT-MI) inverse problem algorithm has some problems such as the singularity of reconstructed boundary and poor anti-noise performance, which make it difficult to be applied to recognition of early breast cancer tumor. Therefore, a system matrix linking the concentration distribution information of magnetic nanoparticles (MNPs) to the ultrasonic signal was built in this paper, and a truncated singular value decomposition (TSVD) based MNPS concentration reconstruction algorithm was proposed. Firstly, a simulation model was established. Secondly, the magnetic field and acoustic field simulation data were substituted into the inverse problem algorithm based on TSVD for concentration reconstruction. Finally, the effects of the number of singular values, SNR and radius of MNPs on the reconstruction results were studied. The simulation results show that, the inverse problem algorithm based on TSVD proposed in this paper can maximize the use of ultrasonic signals, and has a good reconstruction effect on 1 mm small-radius MNPs, high resolution reconstructed images can also be obtained under the condition of low SNR, which can effectively promote the clinical application of this imaging method. Graphical abstract