Self-aligning manifolds for matching disparate medical image datasets - Baumgartner, Christian F.; Gomez, Alberto; Koch, Lisa M.; Housden, James R.; Kolbitsch, Christoph; McClelland, Jamie R.; Rueckert, Daniel; King, Andy P. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015)
Manifold alignment can be used to reduce the dimensionality of multiple medical image datasets into a single globally consistent lowdimensional space. This may be desirable in a wide variety of problems, from fusion of different imaging modalities for Alzheimer’s disease classification to 4DMR reconstruction from 2D MR slices. Unfortunately, most existing manifold alignment techniques require either a set of prior correspondences or comparability between the datasets in high-dimensional space, which is often not possible. We propose a novel technique for the ‘self-alignment’ of manifolds (SAM) from multiple dissimilar imaging datasets without prior correspondences or inter-dataset image comparisons. We quantitatively evaluate the method on 4DMR reconstruction from realistic, synthetic sagittal 2D MR slices from 6 volunteers and real data from 4 volunteers. Additionally, we demonstrate the technique for the compounding of two free breathing 3D ultrasound views from one volunteer. The proposed method performs significantly better for 4DMR reconstruction than state-of-the-art image-based techniques.
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