MR Acquisition-Invariant Representation Learning
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Voxelwise classification is a popular and effective method for tissue quantification in brain magnetic resonance imaging (MRI) scans. However, there are often large differences over sets of MRI scans due to how they were acquired (i.e. field strength, vendor, protocol), that lead to variation in, among others, pixel intensities, tissue contrast, signal-to-noise ratio, resolution, slice thickness and magnetic field inhomogeneities. Classifiers trained on data from a specific scanner fail or under-perform when applied to data that was differently acquired. In order to address this lack of generalization, we propose a Siamese neural network (MRAI-net) to learn a representation that minimizes the between-scanner variation, while maintaining the contrast between brain tissues necessary for brain tissue quantification. The proposed MRAI-net was evaluated on both simulated and real MRI data. After learning the MR acquisition invariant representation, any supervised classifier can be applied. In this paper we showed that applying a linear classifier on the MRAI representation outperforms supervised convolutional neural network classifiers for tissue classification when little target training data is available.
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