Abstract
Magnetic particle imaging (MPI) is a quantitative method for determining the spatial distribution of magnetic nanoparticles, which can be used as tracers for cardiovascular imaging. For reconstructing a spatial map of the particle distribution, the system matrix describing the magnetic particle imaging equation has to be known. Due to the complex dynamic behavior of the magnetic particles, the system matrix is commonly measured in a calibration procedure. In order to speed up the reconstruction process, recently, a matrix compression technique has been proposed that makes use of a basis transformation in order to compress the MPI system matrix. By thresholding the resulting matrix and storing the remaining entries in compressed row storage format, only a fraction of the data has to be processed when reconstructing the particle distribution. In the present work, it is shown that the image quality of the algorithm can be considerably improved by using a local threshold for each matrix row instead of a global threshold for the entire system matrix.
Highlights
The quantitative imaging method magnetic particle imaging (MPI) allows resolving the spatial distribution of superparamagnetic iron oxide (SPIO) nanoparticles at high spatiotemporal resolution and high sensitivity [1,2,3]
As a reference for the proposed sparse reconstruction framework using local and global thresholding, we use the common MPI reconstruction method, which uses the dense representation of the system matrix
We have investigated the sparse reconstruction framework for magnetic particle imaging that allows the processing of large MPI datasets for which the full system matrix would not fit into the main memory of the reconstruction computer
Summary
The quantitative imaging method magnetic particle imaging (MPI) allows resolving the spatial distribution of superparamagnetic iron oxide (SPIO) nanoparticles at high spatiotemporal resolution and high sensitivity [1,2,3]. Due to an insufficient understanding of the particle physics, the system matrix is usually measured in a calibration procedure and stored on permanent memory for further processing. When increasing the image size and in turn the density of the sampling trajectory, the size of the system matrix increases quadratically such that for 643 image voxels more than 3 TB of main memory are required. While in current MPI reconstruction methods [5], only less than half of the full system is used and in turn is loaded into main memory, with better scanner hardware, the noise in the measured system matrix will be reduced so that more matrix rows have sufficient SNR and can be considered for reconstruction
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