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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Deep-Learning Estimation of Second-Generation Pharmacokinetic-Model Parameters in DCE-MRI
Original language description
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.
Czech name
—
Czech description
—

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2024 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)
ISBN
979-8-3503-5156-9
ISSN
2641-3590
e-ISSN
—
Number of pages
8
Pages from-to
10913501
Publisher name
IEEE
Place of publication
Piscataway
Event location
Houston
Event date
Nov 10, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001465723200004

Similar results(10)