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• W2809899887 abstract "This paper introduces new variational formulation for reconstruction from subsampled dynamic contrast-enhanced DCE-MRI data, that combines a data-driven approach using estimated temporal basis and total variation regularization (PCA TV). We also experimentally compares the performance of such model with two other state-of-the-art formulations. One models the shape of perfusion curves in time as a sum of a curve belonging to a low-dimensional space and a function sparse in a suitable domain (L + S model). The other possibility is to regularize both spatial and time domains (ICTGV). We are dealing with the specific situation of the DCE-MRI acquisition with a 9.4T small animal scanner, working with noisier signals than human scanners and with a smaller number of coil elements that can be used for parallel acquisition and small voxels. Evaluation of the selected methods is done through subsampled reconstruction of radially-sampled DCE-MRI data. Our analysis shows that compressed sensed MRI in the form of regularization can be used to increase the temporal resolution of acquisition while keeping a sufficient signal-to-noise ratio." @default.
• W2809899887 created "2018-07-10" @default.
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• W2809899887 date "2018-05-30" @default.
• W2809899887 modified "2023-10-17" @default.
• W2809899887 title "Iterative Methods for Fast Reconstruction of Undersampled Dynamic Contrast-Enhanced MRI Data" @default.
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• W2809899887 doi "https://doi.org/10.1007/978-981-10-9035-6_48" @default.
• W2809899887 hasPublicationYear "2018" @default.
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