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. Author manuscript; available in PMC: 2016 Jan 15.
Published in final edited form as: Neuroimage. 2014 Nov 1;105:156–170. doi: 10.1016/j.neuroimage.2014.10.052

Figure 2.

Figure 2

Graphical abstract of proposed method. Patches are sampled from image in modality 1 (here, T1) and SVD is used to learn optimal features (“eigenpatches”) to describe patches. Patches corresponding to each point in the image are then projected onto the “eigenpatches” to create a representation of the input image in feature space. We then use linear regression to predict the second image (here, perfusion image) from the feature-based description of the first image. This enables us to decompose the perfusion image into a component that is predicted from the structural image and the unique contribution of the perfusion image.