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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