Rapid and robust image reconstruction for magnetic particle imaging

Magnetic Particle Imaging (MPI) is a rapidly developing medical imaging modality, which can image with high resolution, contrast, and sensitivity the spatial distribution of superparamagnetic iron oxide nanoparticles. Several applications of MPI have been introduced on angiography, cancer imaging, and stem cell tracking. Due to the safety limits on time-varying magnetic fields, MPI images are obtained by dividing the field-of-view (FOV) into numerous relatively small partial FOVs (pFOVs). Each pFOV suffers from a DC loss due to direct feedthrough interference caused by simultaneous excitation and signal reception. The standard x-space image reconstruction first processes each pFOV separately, and then combines them by enforcing smoothness and non-negativity constraints on the final image. These steps require the pFOVs to overlap, and can amplify the effects of non-ideal signal conditions, such as harmonic interference, noise, and nanoparticle relaxation. This thesis proposes two robust x-space image reconstruction techniques. The first technique, pFOV center imaging (PCI), first forms a raw image of the entire FOV by directly mapping the signal to pFOV centers. The final image is then reconstructed by deconvolving this raw image with a known, compact kernel. The second technique, harmonic dispersion x-space (HD-X), takes advantage of the dispersion in signal harmonics in the case of rapid scan trajectories. Followed by a sharp bandstop filtering of the fundamental harmonic, this technique directly grids the signal to form the final image, and does not require overlapping pFOVs. PCI offers robustness against harmonic interferences, and HD-X enables x-space reconstruction of rapid and sparse trajectories with non-overlapping pFOVs. Extensive simulations and imaging experiments show that both proposed methods outperform standard x-space reconstruction in terms of robustness against non-ideal signal conditions and image quality.