Iterative Methods for Fast Reconstruction of Undersampled Dynamic Contrast-Enhanced MRI Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F18%3A00490787" target="_blank" >RIV/67985556:_____/18:00490787 - isvavai.cz</a>
Alternative codes found
RIV/68081731:_____/18:00490787 RIV/68407700:21340/19:00325073
Result on the web
<a href="http://dx.doi.org/10.1007/978-981-10-9035-6_48" target="_blank" >http://dx.doi.org/10.1007/978-981-10-9035-6_48</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-981-10-9035-6_48" target="_blank" >10.1007/978-981-10-9035-6_48</a>
Alternative languages
Result language
angličtina
Original language name
Iterative Methods for Fast Reconstruction of Undersampled Dynamic Contrast-Enhanced MRI Data
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA16-13830S" target="_blank" >GA16-13830S: Magnetic resonance perfusion imaging using compressed sensing</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
World Congress on Medical Physics and Biomedical Engineering 2018
ISBN
978-981-10-9034-9
ISSN
1680-0737
e-ISSN
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Number of pages
5
Pages from-to
267-271
Publisher name
Springer
Place of publication
Singapore
Event location
Praha
Event date
Jun 3, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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