The following software is freely available for academic or non-profit use. Follow the appropriate link to request software access.
PSM (Phase Slope Magnitude) Image analysis
Reference: Mills PH, Ahrens ET. Enhanced positive-contrast visualization of paramagnetic contrast agents using phase images. Magn. Reson. Med. 62(5):1349-55, 2009.
Iron oxide–based MRI contrast agents are increasingly being used to noninvasively track cells, target molecular epitopes, and monitor gene expression in vivo. Detecting regions of contrast agent accumulation can be challenging if resulting contrast is subtle relative to endogenous tissue hypointensities. A postprocessing method is presented that yields enhanced positive-contrast images from the phase map associated with T2* weighted MRI data. As examples, the method was applied to an agarose gel phantom doped with superparamagnetic iron-oxide nanoparticles and in vivo and ex vivo mouse brains inoculated with recombinant viruses delivering transgenes that induce overexpression of paramagnetic ferritin. Overall, this approach generates images that exhibit a 1- to 8-fold improvement in contrast-to-noise ratio in regions where paramagnetic agents are present compared to conventional magnitude images. This approach can be used in conjunction with conventional T2* pulse sequences, requires no prescans or increased scan time, and can be applied retrospectively to previously acquired data.
Programming Code: Matlab
PDQ (Phase Map Cross-correlation Detection and Quantification)
References: Mills PH, Hitchens TK, Foley LM, Link T, Ye Q, Weiss CR, Thompson JD, Gilson WD, Arepally A, Melick JA, Kochanek PM, Ho C, Bulte JW, Ahrens ET. Automated detection and characterization of SPIO-labeled cells and capsules using magnetic field perturbations. Magn. Reson. Med. 67(1):278-89 7, 2012.
Mills PH, Wu YJ, Ho C, Ahrens ET. Sensitive and automated detection of iron-oxide-labeled cells using phase image cross-correlation analysis. Magn. Reson. Imaging. 26(5):618-28, 2008.
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-oxidelabeled 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.
Programming Code: Matlab