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

Článek v odborném periodiku

Field of Study

30103 Neurosciences

Country of publisher

Switzerland

Confidentiality degree

není předmětem státního či obchodního tajemství

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

Keywords in English

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

Abstract

V originále

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

Citovat

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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