Sensitive and automated detection of iron-oxide-labeled cells using phase image cross-correlation analysis

Superparamagnetic iron oxide (SPIO) nanoparticles are increasingly being used to noninvasively track cells, target specific molecules and monitor gene expression in vivo. Contrast changes that are subtle relative to intrinsic sources of contrast present a significant detection challenge. Here, we describe a postprocessing algorithm, called Phase map cross-correlation Detection and Quantification (PDQ), with the purpose of automating identification and quantification of localized accumulations of SPIO agents. The method is designed to sacrifice little flexibility - it works on previously acquired data and allows the use of conventional high-SNR pulse sequences with no extra scan time. We first investigated the theoretical detection limits of PDQ using a simulated dipole field. This method was then applied to three-dimensional (3D) MRI data sets of agarose gel containing isolated dipoles and ex vivo transplanted allogenic rat hearts infiltrated by numerous iron-oxide-labeled macrophages as a result of organ rejection. A simulated dipole field showed this method to be robust in very low signal-to-noise ratio images. Analysis of agarose gel and allogenic rat heart shows that this method can automatically identify and count dipoles while visualizing their biodistribution in 3D renderings. In the heart, this information was used to calculate a quantitative index that may indicate its degree of cellular infiltration.