  The purpose of this study was to present image reconstruction methods for
magnetic particle imaging (MPI) with a field-free-line (FFL) encoding scheme
and to propose the use of the maximum likelihood-expectation maximization
(ML-EM) algorithm for improving the image quality of MPI. The feasibility of
these methods was investigated by computer simulations, in which the projection
data were generated by summing up the Fourier harmonics obtained from the MPI
signals based on the Langevin function. Images were reconstructed from the
generated projection data using the filtered backprojection (FBP) method and
the ML-EM algorithm. The effects of the gradient of selection magnetic field
(SMF), the strength of drive magnetic field (DMF), the diameter of magnetic
nanoparticles (MNPs), and the number of projection data on the image quality of
the reconstructed images were investigated. The spatial resolution of the
reconstructed images became better with increasing gradient of SMF and with
increasing diameter of MNPs up to approximately 30 nm. For the diameter greater
than approximately 30 nm, the spatial resolution was almost constant. It also
became better with decreasing strength of DMF. The image quality was improved
by use of the ML-EM algorithm compared to the FBP method, especially when the
strength of DMF was weak and the number of projection data was small. In
conclusion, we presented image reconstruction methods for MPI with an FFL
encoding scheme and our preliminary results suggest that the ML-EM algorithm
will be useful for improving the image quality of MPI.
