Cargando…

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

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Jamárik, Jakub, Vojtíšek, Lubomír, Churová, Vendula, Kašpárek, Tomáš, Schwarz, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8774564/
https://www.ncbi.nlm.nih.gov/pubmed/35054191
http://dx.doi.org/10.3390/diagnostics12010024
_version_ 1784636377971818496
author Jamárik, Jakub
Vojtíšek, Lubomír
Churová, Vendula
Kašpárek, Tomáš
Schwarz, Daniel
author_facet Jamárik, Jakub
Vojtíšek, Lubomír
Churová, Vendula
Kašpárek, Tomáš
Schwarz, Daniel
author_sort Jamárik, Jakub
collection PubMed
description 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 T(1) 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 T(1) 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 T(1) relaxation times can be achieved using the modified algorithm with MPE < 5%.
format Online
Article
Text
id pubmed-8774564
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-87745642022-01-21 Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm Jamárik, Jakub Vojtíšek, Lubomír Churová, Vendula Kašpárek, Tomáš Schwarz, Daniel Diagnostics (Basel) Article 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 T(1) 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 T(1) 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 T(1) relaxation times can be achieved using the modified algorithm with MPE < 5%. MDPI 2021-12-23 /pmc/articles/PMC8774564/ /pubmed/35054191 http://dx.doi.org/10.3390/diagnostics12010024 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jamárik, Jakub
Vojtíšek, Lubomír
Churová, Vendula
Kašpárek, Tomáš
Schwarz, Daniel
Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm
title Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm
title_full Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm
title_fullStr Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm
title_full_unstemmed Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm
title_short Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm
title_sort identification of laminar composition in cerebral cortex using low-resolution magnetic resonance images and trust region optimization algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8774564/
https://www.ncbi.nlm.nih.gov/pubmed/35054191
http://dx.doi.org/10.3390/diagnostics12010024
work_keys_str_mv AT jamarikjakub identificationoflaminarcompositionincerebralcortexusinglowresolutionmagneticresonanceimagesandtrustregionoptimizationalgorithm
AT vojtiseklubomir identificationoflaminarcompositionincerebralcortexusinglowresolutionmagneticresonanceimagesandtrustregionoptimizationalgorithm
AT churovavendula identificationoflaminarcompositionincerebralcortexusinglowresolutionmagneticresonanceimagesandtrustregionoptimizationalgorithm
AT kasparektomas identificationoflaminarcompositionincerebralcortexusinglowresolutionmagneticresonanceimagesandtrustregionoptimizationalgorithm
AT schwarzdaniel identificationoflaminarcompositionincerebralcortexusinglowresolutionmagneticresonanceimagesandtrustregionoptimizationalgorithm