Iterative Methods for Fast Reconstruction of Undersampled Dynamic Contrast-Enhanced MRI Data

Iterative Methods for Fast Reconstruction of Undersampled Dynamic Contrast-Enhanced MRI Data

Paper in proceedings (conference paper)

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. DCE-MRI, iterative reconstruction techniques, compressed sensing.

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. DCE-MRI, iterative reconstruction techniques, compressed sensing.

DCE-MRI; Iterative reconstruction techniques; Compressed sensing

DCE-MRI; Iterative reconstruction techniques; Compressed sensing

WALNER, H.; BARTOŠ, M.; MANGOVÁ, M.; KEUNEN, O.; BJERKVIG, R.; JIŘÍK, R.; ŠOREL, M.

2019

02.01.2019

978-981-10-9035-6

World Congress on Medical Physics and Biomedical Engineering 2018

267

271

5

https://link.springer.com/chapter/10.1007/978-981-10-9035-6_48