Thesis Details

Akcelerace neurostimulace pomocí metod umělé inteligence

Master's Thesis Student: Gaňo Martin Academic Year: 2021/2022 Supervisor: Jaroš Jiří, doc. Ing., Ph.D.
English title
Acceleration of Neurostimulation Using Artificial Intelligence Methods
Language
Czech
Abstract

Treatment using transcranial ultrasound is a rapidly arising domain of medicine. This method brings options for non-invasive brain therapies, including ablation, neuromodulation, or potentially opening the blood-brain barrier for the following treatment. The health officer needs to constantly receive feedback on the ultrasound wavefield in the human skull in real-time to accomplish the cure using these techniques. The traditional methods for simulating monochromous ultrasound waves are computationally too expensive. That is why their usage would be infeasible for these purposes, and it brings the need for alternative methods. This work proposed and implemented a method to solve the Helmholtz equation in 3D space using a neural network achieving a faster convergence rate. The neural network design uses lightweight architecture based on UNet. The main interest of this work is neuromodulation because, in this application, it is possible to ignore several variables and phenomena that would not be negligible in other use cases. Omitting them from the calculations increased the chances of accomplishing computations in a reasonable time. The method is fully unsupervised and uses exclusively artificially generated spherical harmonics and physics-based loss for training, with no required ground truth labels. Results showed a faster calculation with acceptable error than other traditional methods.

Keywords

Helmholtz equation, learned optimizer, unsupervised learning, physics-based loss function, transcranial ultrasound

Department
Degree Programme
Files
Status
defended, grade C
Date
20 June 2022
Reviewer
Committee
Čadík Martin, doc. Ing., Ph.D. (DCGM FIT BUT), předseda
Beran Vítězslav, Ing., Ph.D. (DCGM FIT BUT), člen
Juránek Roman, Ing., Ph.D. (DCGM FIT BUT), člen
Křivka Zbyněk, Ing., Ph.D. (DIFS FIT BUT), člen
Milet Tomáš, Ing., Ph.D. (DCGM FIT BUT), člen
Zemčík Pavel, prof. Dr. Ing. (DCGM FIT BUT), člen
Citation
GAŇO, Martin. Akcelerace neurostimulace pomocí metod umělé inteligence. Brno, 2022. Master's Thesis. Brno University of Technology, Faculty of Information Technology. 2022-06-20. Supervised by Jaroš Jiří. Available from: https://www.fit.vut.cz/study/thesis/23927/
BibTeX
@mastersthesis{FITMT23927,
    author = "Martin Ga\v{n}o",
    type = "Master's thesis",
    title = "Akcelerace neurostimulace pomoc\'{i} metod um\v{e}l\'{e} inteligence",
    school = "Brno University of Technology, Faculty of Information Technology",
    year = 2022,
    location = "Brno, CZ",
    language = "czech",
    url = "https://www.fit.vut.cz/study/thesis/23927/"
}
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