TY - GEN
T1 - Learning-Based MRI Response Predictions from OCT Microvascular Models to Replace Simulation-Based Frameworks
AU - Rustamov, Jaloliddin
AU - Rustamov, Zahiriddin
AU - Badawi, Nadia
AU - Lesage, Frederic
AU - Zaki, Nazar
AU - Damseh, Rafat
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Computational quantification of magnetic resonance imaging (MRI) response from neurovascular structures is used to investigate potential biomarkers for different types of cerebrovascular deteriorations at the microscopic scale. Simulation-based MRI requires fully resolved microvascular structures, with geometric and physiological parameters, from tissue volumes captured using microscopic imaging modalities, e.g., optical coherence tomography (OCT). The preparation of such input models hinders large cohort studies and requires extensive manual effort. Here, we propose using 3D neural networks as an alternative learning-based solution over MRI simulation schemes. We trained state-of-the-art 3D neural networks to predict the spin echo (SE) MRI response from OCT microvascular volumes. By validating against simulated signals, our result demonstrates that the 3D ResNet-based regression network achieves a high accuracy to predict MRI signals with an average mean square error (MSE) <1%, R2 of 82.8% and explained variance score of 82.9%.
AB - Computational quantification of magnetic resonance imaging (MRI) response from neurovascular structures is used to investigate potential biomarkers for different types of cerebrovascular deteriorations at the microscopic scale. Simulation-based MRI requires fully resolved microvascular structures, with geometric and physiological parameters, from tissue volumes captured using microscopic imaging modalities, e.g., optical coherence tomography (OCT). The preparation of such input models hinders large cohort studies and requires extensive manual effort. Here, we propose using 3D neural networks as an alternative learning-based solution over MRI simulation schemes. We trained state-of-the-art 3D neural networks to predict the spin echo (SE) MRI response from OCT microvascular volumes. By validating against simulated signals, our result demonstrates that the 3D ResNet-based regression network achieves a high accuracy to predict MRI signals with an average mean square error (MSE) <1%, R2 of 82.8% and explained variance score of 82.9%.
KW - 3D Neural networks
KW - MRI prediction
KW - Magnetic resonance imaging
KW - Optical coherence tomography
KW - vascular imaging
UR - https://www.scopus.com/pages/publications/85200684216
UR - https://www.scopus.com/pages/publications/85200684216#tab=citedBy
U2 - 10.1007/978-3-031-66955-2_4
DO - 10.1007/978-3-031-66955-2_4
M3 - Conference contribution
AN - SCOPUS:85200684216
SN - 9783031669545
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 54
EP - 67
BT - Medical Image Understanding and Analysis - 28th Annual Conference, MIUA 2024, Proceedings
A2 - Yap, Moi Hoon
A2 - Kendrick, Connah
A2 - Behera, Ardhendu
A2 - Cootes, Timothy
A2 - Zwiggelaar, Reyer
PB - Springer Science and Business Media Deutschland GmbH
T2 - 28th Annual Conference on Medical Image Understanding and Analysis, MIUA 2024
Y2 - 24 July 2024 through 26 July 2024
ER -