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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F19%3A00325073" target="_blank" >RIV/68407700:21340/19:00325073 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68081731:_____/18:00490787 RIV/67985556:_____/18:00490787

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-981-10-9035-6_48" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-981-10-9035-6_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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 compare the performance of such a 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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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 compare the performance of such a 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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

World Congress on Medical Physics and Biomedical Engineering 2018 (Vol. 1)

ISBN

978-981-10-9034-9

ISSN

1680-0737

e-ISSN

—

Počet stran výsledku

5

Strana od-do

267-271

Název nakladatele

Springer Nature Singapore Pte Ltd.

Místo vydání

—

Místo konání akce

Prague

Datum konání akce

3. 6. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—