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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Deep-Learning in Simultaneous DCE-DSC-MRI Perfusion Analysis
Original language description
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.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

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

Article name in the collection
2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
ISBN
979-8-3503-8622-6
ISSN
2156-1133
e-ISSN
2156-1133
Number of pages
9
Pages from-to
4933-4941
Publisher name
IEEE
Place of publication
Piscataway
Event location
Lisbon
Event date
Dec 3, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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