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Volume 12
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10.3390/diagnostics12010024
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Article

Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm

by
Jakub Jamárik
1,
Lubomír Vojtíšek
2,
Vendula Churová
3,
Tomáš Kašpárek
1 and
Daniel Schwarz
3,*
1
Department of Psychiatry, Faculty of Medicine, Masaryk University, 625 00 Brno, Czech Republic
2
Neuroscience Centre, Central European Institute of Technology, Masaryk University, 625 00 Brno, Czech Republic
3
Department of Simulation Medicine, Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, 625 00 Brno, Czech Republic
*
Author to whom correspondence should be addressed.
Diagnostics 2022, 12(1), 24; https://doi.org/10.3390/diagnostics12010024
Submission received: 15 November 2021 / Revised: 13 December 2021 / Accepted: 20 December 2021 / Published: 23 December 2021
(This article belongs to the Special Issue Advancements in Neuroimaging)
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Abstract

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%.
Keywords: cortical layers; mathematical modeling; MR imaging; optimization algorithm; brain imaging cortical layers; mathematical modeling; MR imaging; optimization algorithm; brain imaging
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MDPI and ACS Style

Jamárik, J.; Vojtíšek, L.; Churová, V.; Kašpárek, T.; Schwarz, D. Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm. Diagnostics 2022, 12, 24. https://doi.org/10.3390/diagnostics12010024

AMA Style

Jamárik J, Vojtíšek L, Churová V, Kašpárek T, Schwarz D. Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm. Diagnostics. 2022; 12(1):24. https://doi.org/10.3390/diagnostics12010024

Chicago/Turabian Style

Jamárik, Jakub, Lubomír Vojtíšek, Vendula Churová, Tomáš Kašpárek, and Daniel Schwarz. 2022. "Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm" Diagnostics 12, no. 1: 24. https://doi.org/10.3390/diagnostics12010024

APA Style

Jamárik, J., Vojtíšek, L., Churová, V., Kašpárek, T., & Schwarz, D. (2022). Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm. Diagnostics, 12(1), 24. https://doi.org/10.3390/diagnostics12010024

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