| JAMÁRIK, Jakub, Lubomír VOJTÍŠEK, Vendula CHUROVÁ, Tomáš KAŠPÁREK a Daniel SCHWARZ. Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm. Diagnostics. Basel: MDPI, 2022, roč. 12, č. 1, s. 1-12. ISSN 2075-4418. Dostupné z: https://dx.doi.org/10.3390/diagnostics12010024. |
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@article{1816862,
author = {Jamárik, Jakub and Vojtíšek, Lubomír and Churová, Vendula and Kašpárek, Tomáš and Schwarz, Daniel},
article_location = {Basel},
article_number = {1},
doi = {http://dx.doi.org/10.3390/diagnostics12010024},
keywords = {cortical layers; mathematical modeling; MR imaging; optimization algorithm; brain imaging},
language = {eng},
issn = {2075-4418},
journal = {Diagnostics},
title = {Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm},
url = {https://www.mdpi.com/2075-4418/12/1/24},
volume = {12},
year = {2022}
}
TY - JOUR
ID - 1816862
AU - Jamárik, Jakub - Vojtíšek, Lubomír - Churová, Vendula - Kašpárek, Tomáš - Schwarz, Daniel
PY - 2022
TI - Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm
JF - Diagnostics
VL - 12
IS - 1
SP - 1-12
EP - 1-12
PB - MDPI
SN - 20754418
KW - cortical layers
KW - mathematical modeling
KW - MR imaging
KW - optimization algorithm
KW - brain imaging
UR - https://www.mdpi.com/2075-4418/12/1/24
N2 - Pathological changes in the cortical lamina can cause several mental disorders. Visualization of these changes in vivo would enhance their diagnostics. Recently a framework for visualizing cortical structures by magnetic resonance imaging (MRI) has emerged. This is based on mathematical modeling of multi-component T1 relaxation at the sub-voxel level. This work proposes a new approach for their estimation. The approach is validated using simulated data. Sixteen MRI experiments were carried out on healthy volunteers. A modified echo-planar imaging (EPI) sequence was used to acquire 105 individual volumes. Data simulating the images were created, serving as the ground truth. The model was fitted to the data using a modified Trust Region algorithm. In single voxel experiments, the estimation accuracy of the T1 relaxation times depended on the number of optimization starting points and the level of noise. A single starting point resulted in a mean percentage error (MPE) of 6.1%, while 100 starting points resulted in a perfect fit. The MPE was <5% for the signal-to-noise ratio (SNR) ≥ 38 dB. Concerning multiple voxel experiments, the MPE was <5% for all components. Estimation of T1 relaxation times can be achieved using the modified algorithm with MPE < 5%.
ER -
JAMÁRIK, Jakub, Lubomír VOJTÍŠEK, Vendula CHUROVÁ, Tomáš KAŠPÁREK a Daniel SCHWARZ. Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm. \textit{Diagnostics}. Basel: MDPI, 2022, roč.~12, č.~1, s.~1-12. ISSN~2075-4418. Dostupné z: https://dx.doi.org/10.3390/diagnostics12010024.
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