Deep-Learning in Simultaneous DCE-DSC-MRI Perfusion Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F24%3A00617204" target="_blank" >RIV/68081731:_____/24:00617204 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10822538" target="_blank" >https://ieeexplore.ieee.org/document/10822538</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/BIBM62325.2024.10822538" target="_blank" >10.1109/BIBM62325.2024.10822538</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep-Learning in Simultaneous DCE-DSC-MRI Perfusion Analysis

Popis výsledku v původním jazyce

The paper aims at improved reliability of magnetic resonance perfusion imaging and estimation of an extended set of biomarkers using these techniques. Magnetic resonance perfusion imaging is an important experimental methodology with main applications in diagnostics and therapy monitoring in oncology. The main two methods are Dynamic Contrast-Enhanced (DCE) Magnetic Resonance Imaging (MRI) and Dynamic Susceptibility Contrast (DSC) MRI. We combine these two methods in a simultaneous acquisition and data processing approach. For simultaneous DCE-DSC-MRI data processing, we suggest a conventional non-linear least-squares method and a method based on convolutional neural networks. We evaluated the proposed methods on realistically simulated synthetic datasets and illustrated their performance on a real dataset. Compared to the standard approach, the methods of simultaneous DCE-DSC-MRI analysis were more reliable. The two proposed methods of simultaneous DCE-DSC-MRI analysis were comparable, with the neural-network approach being computationally far more effective.

Název v anglickém jazyce

Deep-Learning in Simultaneous DCE-DSC-MRI Perfusion Analysis

Popis výsledku anglicky

The paper aims at improved reliability of magnetic resonance perfusion imaging and estimation of an extended set of biomarkers using these techniques. Magnetic resonance perfusion imaging is an important experimental methodology with main applications in diagnostics and therapy monitoring in oncology. The main two methods are Dynamic Contrast-Enhanced (DCE) Magnetic Resonance Imaging (MRI) and Dynamic Susceptibility Contrast (DSC) MRI. We combine these two methods in a simultaneous acquisition and data processing approach. For simultaneous DCE-DSC-MRI data processing, we suggest a conventional non-linear least-squares method and a method based on convolutional neural networks. We evaluated the proposed methods on realistically simulated synthetic datasets and illustrated their performance on a real dataset. Compared to the standard approach, the methods of simultaneous DCE-DSC-MRI analysis were more reliable. The two proposed methods of simultaneous DCE-DSC-MRI analysis were comparable, with the neural-network approach being computationally far more effective.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

ISBN

979-8-3503-8622-6

ISSN

2156-1133

e-ISSN

2156-1133

Počet stran výsledku

9

Strana od-do

4933-4941

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Lisbon

Datum konání akce

3. 12. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—