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

J 2022

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

JAMÁRIK, Jakub; Lubomír VOJTÍŠEK; Vendula CHUROVÁ; Tomáš KAŠPÁREK; Daniel SCHWARZ et. al.

Basic information

Original name

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

Authors

JAMÁRIK, Jakub (703 Slovakia, belonging to the institution); Lubomír VOJTÍŠEK (203 Czech Republic, belonging to the institution); Vendula CHUROVÁ (203 Czech Republic, belonging to the institution); Tomáš KAŠPÁREK (203 Czech Republic, belonging to the institution) and Daniel SCHWARZ (203 Czech Republic, guarantor, belonging to the institution)

Edition

Diagnostics, Basel, MDPI, 2022, 2075-4418

Other information

Language

English

Type of outcome

Article in a journal

Field of Study

30103 Neurosciences

Country of publisher

Switzerland

Confidentiality degree

is not subject to a state or trade secret

References:

URL

Impact factor

Impact factor: 3.600

RIV identification code

RIV/00216224:14110/22:00125139

Organization unit

Faculty of Medicine

DOI

http://dx.doi.org/10.3390/diagnostics12010024

UT WoS

000757263000001

EID Scopus

2-s2.0-85121692716

Keywords in English

cortical layers; mathematical modeling; MR imaging; optimization algorithm; brain imaging

Abstract

In the original language

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%.

Links

NV17-33136A, research and development project

Name: Neurominer: odhalování skrytých vzorů v datech ze zobrazování mozku

90129, large research infrastructures

Name: Czech-BioImaging II

Cite

JAMÁRIK, Jakub; Lubomír VOJTÍŠEK; Vendula CHUROVÁ; Tomáš KAŠPÁREK and Daniel SCHWARZ. Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm. Diagnostics. Basel: MDPI, 2022, vol. 12, No 1, p. 1-12. ISSN 2075-4418. Available from: https://dx.doi.org/10.3390/diagnostics12010024.

@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 and 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, vol.~12, No~1, p.~1-12. ISSN~2075-4418. Available from: https://dx.doi.org/10.3390/diagnostics12010024.

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