@article {796,
	title = {Smooth Scalar-on-Image Regression via Spatial Bayesian Variable Selection.},
	journal = {J Comput Graph Stat},
	volume = {23},
	year = {2014},
	month = {2014 Jan 1},
	pages = {46-64},
	abstract = {<p>We develop scalar-on-image regression models when images are registered multidimensional manifolds. We propose a fast and scalable Bayes inferential procedure to estimate the image coefficient. The central idea is the combination of an Ising prior distribution, which controls a latent binary indicator map, and an intrinsic Gaussian Markov random field, which controls the smoothness of the nonzero coefficients. The model is fit using a single-site Gibbs sampler, which allows fitting within minutes for hundreds of subjects with predictor images containing thousands of locations. The code is simple and is provided in less than one page in the Appendix. We apply this method to a neuroimaging study where cognitive outcomes are regressed on measures of white matter microstructure at every voxel of the corpus callosum for hundreds of subjects.</p>},
	issn = {1061-8600},
	doi = {10.1080/10618600.2012.743437},
	author = {Goldsmith, Jeff and Huang, Lei and Crainiceanu, Ciprian M}
}
@article {786,
	title = {Statistical normalization techniques for magnetic resonance imaging.},
	journal = {Neuroimage Clin},
	volume = {6},
	year = {2014},
	month = {2014},
	pages = {9-19},
	abstract = {<p>While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer{\textquoteright}s disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers.</p>},
	issn = {2213-1582},
	doi = {10.1016/j.nicl.2014.08.008},
	author = {Shinohara, Russell T and Sweeney, Elizabeth M and Goldsmith, Jeff and Shiee, Navid and Mateen, Farrah J and Calabresi, Peter A and Jarso, Samson and Pham, Dzung L and Reich, Daniel S and Crainiceanu, Ciprian M}
}
