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

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>

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

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Czech description

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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])

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)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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