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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%2F00216305%3A26220%2F18%3APU128544" target="_blank" >RIV/00216305:26220/18:PU128544 - isvavai.cz</a>

  • Result on the web

    <a href="https://link.springer.com/chapter/10.1007/978-981-10-9035-6_48" target="_blank" >https://link.springer.com/chapter/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. DCE-MRI, iterative reconstruction techniques, compressed sensing.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20602 - Medical laboratory technology (including laboratory samples analysis; diagnostic technologies) (Biomaterials to be 2.9 [physical characteristics of living material as related to medical implants, devices, sensors])

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

    2019

  • 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

    978-981-10-9035-6

  • ISSN

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    267-271

  • Publisher name

    Neuveden

  • Place of publication

    neuveden

  • Event location

    Prague

  • Event date

    Jun 3, 2018

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000450908300048