Abstract
Dynamic contrast-enhanced MRI (DCE-MRI) plays an important role as an imaging method for the diagnosis and evaluation of several diseases. Indeed, clinically relevant, per-voxel quantitative information may be extracted through the analysis of the enhanced MR signal. This paper presents a method for the automated analysis of DCE-MRI data that works by decomposing the enhancement curves as sparse linear combinations of elementary curves learned without supervision from the data. Experimental results show that performances in denoising and unsupervised segmentation improve over parametric methods.
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Chiusano, G., Staglianò, A., Basso, C., Verri, A. (2011). DCE-MRI Analysis Using Sparse Adaptive Representations. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2011. Lecture Notes in Computer Science, vol 7009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24319-6_9
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DOI: https://doi.org/10.1007/978-3-642-24319-6_9
Publisher Name: Springer, Berlin, Heidelberg
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