Bryan Kressler

TL;DR: A bayesian regularization approach that adds spatial priors from the MR magnitude image is formulated for susceptibility imaging, introducing a new quantitative contrast in MRI that is directly linked to iron in the brain.

TL;DR: Numerical simulations and phantom and in vitro imaging validations demonstrated that COSMOS is a stable and precise approach to quantify a susceptibility distribution using MRI.

TL;DR: In this paper, a method and apparatus for magnetic source magnetic resonance imaging is described, which includes collecting energy signals from an object, providing additional information of characteristics of the object, and generating the image of an object from the energy signals and from the additional information such that the image includes a representation of a quantitative estimation of the characteristics, e.g. a quantitative estimate of magnetic susceptibility.

TL;DR: Methods for measuring the change in nodule size from two computed tomography image scans recorded at different times are presented; from this size change the growth rate may be established and isotropic resampling is shown to improve measurement accuracy.

TL;DR: A technique to estimate arbitrary magnetic susceptibility distributions by solving an ill-posed inversion problem from field maps obtained in an MRI scanner and initial experience indicates that the nonlinear regularization better suppresses noise and streaking artifacts common in susceptibility estimation.