Computing material-selective projection images in MR

We detail a robust, general method for computing projection images of individual materials in a volume as linear combinations of MR projection images with different material-dependent weightings. Signal per unit volume for each material in each raw image is acquired directly for accurate cancellation of undesired, overlapping materials. The weighted sum of the input images is determined to maximize the signal-to-noise ratio (SNR) and minimize inhomogeneity effects in the material-selective images. We tested the implementation experimentally in both phantom and human studies, producing selective images with reasonable SNRs and material isolation. With further development of sequences to rapidly acquire input images having greater material differentiability, we envision the application of the selective projection imaging format to screening studies searching over large volumes for diseased tissues.