Deep-Learning Estimation of Second-Generation Pharmacokinetic-Model Parameters in DCE-MRI

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F24%3A00619162" target="_blank" >RIV/68081731:_____/24:00619162 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/abstract/document/10913501" target="_blank" >https://ieeexplore.ieee.org/abstract/document/10913501</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/BHI62660.2024.10913501" target="_blank" >10.1109/BHI62660.2024.10913501</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep-Learning Estimation of Second-Generation Pharmacokinetic-Model Parameters in DCE-MRI

Popis výsledku v původním jazyce

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising method for the evaluation of tissue perfusion. Current standard is fitting of a pharmacokinetic model to the acquired signals. Most commonly, first generation models are used (Tofts, extended Tofts model) providing stable results, however, only a limited set of parameters. Second generation models allow estimation of a larger parameter set, thus a more complete description of the perfusion state, however, they require high data quality and their application is more computationally demanding. Overall, the lack of standardization of DCE-MRI, its computational time and reliability hinders its routine clinical application. Deep learning methods allow fast parameter estimation and bring new possibilities into this field. In this study, we have explored the application of a convolutional neural network for the prediction of second-generation model parameters. The network was tested for different noise levels and sampling periods on a simulated dataset, and the results were validated on a real preclinical dataset. The proposed method provided more stable and robust results compared to the conventional model fitting.

Název v anglickém jazyce

Deep-Learning Estimation of Second-Generation Pharmacokinetic-Model Parameters in DCE-MRI

Popis výsledku anglicky

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising method for the evaluation of tissue perfusion. Current standard is fitting of a pharmacokinetic model to the acquired signals. Most commonly, first generation models are used (Tofts, extended Tofts model) providing stable results, however, only a limited set of parameters. Second generation models allow estimation of a larger parameter set, thus a more complete description of the perfusion state, however, they require high data quality and their application is more computationally demanding. Overall, the lack of standardization of DCE-MRI, its computational time and reliability hinders its routine clinical application. Deep learning methods allow fast parameter estimation and bring new possibilities into this field. In this study, we have explored the application of a convolutional neural network for the prediction of second-generation model parameters. The network was tested for different noise levels and sampling periods on a simulated dataset, and the results were validated on a real preclinical dataset. The proposed method provided more stable and robust results compared to the conventional model fitting.

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

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

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 EMBS International Conference on Biomedical and Health Informatics (BHI)

ISBN

979-8-3503-5156-9

ISSN

2641-3590

e-ISSN

—

Počet stran výsledku

8

Strana od-do

10913501

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Houston

Datum konání akce

10. 11. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001465723200004