Multiresolution vessel detection in magnetic particle imaging using wavelets and a Gaussian mixture model

Abstract

Magnetic particle imaging is a tomographic imaging technique that allows one to measure the spatial distribution of superparamagnetic nanoparticles, which are used as tracer. The magnetic particle imaging scanner measures the voltage induced due to the nonlinear magnetization behavior of the nanoparticles. The tracer distribution can be reconstructed from the voltage signal by solving an inverse problem. A possible application is the imaging of vessel structures. In this and many other cases, the tracer is only located inside the structures and a large part of the image is related to background. A detection of the tracer support in early stages of the reconstruction process could improve reconstruction results. In this work, a multiresolution wavelet-based reconstruction combined with a segmentation of the foreground structures is performed. For this, different wavelets are compared with respect to their reconstruction quality. For the detection of the foreground, a segmentation with a Gaussian mixture model is performed, which leads to a threshold-based binary segmentation. This segmentation is done on a coarse level of the reconstruction and then transferred to the next finer level, where it is used as prior knowledge for the reconstruction. This is repeated until the finest resolution is reached. The approach is evaluated on simulated vessel phantoms and on two real measurements. The results show that this method improves the structural similarity index of the reconstructed images significantly. Among the compared wavelets, the 9/7 wavelets led to the best reconstruction results. The early detection of the vessel structures at low resolution helps to improve the image quality. For the wavelet decomposition, the use of 9/7 wavelets is recommended.