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SIIA Público
SISTEMA INTEGRAL DE INFORMACIÓN ACADÉMICA - PÚBLICO
Título del libro: Ieee International Symposium On Biomedical Imaging, Isbi 2024 Título del capítulo: IN-SILICO TRAINED AI FOR ENHANCED T2 SPECTRUM IMAGING AND MYELIN WATER FRACTION MAPPING IN PRECLINICAL 7T MRI
In-silico; Machine learning; Brain; MRI; Myelin water imaging; Pre-clinical data
Resumen:
This study introduces a Machine Learning (ML) approach for estimating the T-2 spectrum and myelin water fraction (MWF) using multi-echo T-2 (MET2) data from preclinical 7T Magnetic Resonance Imaging (MRI) scanners. ML methods have shown promise in MWF estimation, outperforming Regularized Non-Negative Least Squares (RNNLS). However, existing ML methods were optimized for high signal-to-noise ratios (SNR) typical of 3T clinical MET2 data with larger voxel sizes. We adapted the Model-Informed Machine Learning (MIML) method to handle challenges in preclinical 7T MRI, including reduced voxel sizes, elevated noise levels (SNR=30-60), and shifts in T-2 lobes. Results from in-silico simulated data demonstrate the superior performance of the proposed multi-layer-perceptron-based solution over RNNLS. Validation with MET2 data from two mice-a healthy control and a cuprizone-exposed pathological mouse-confirms the ML method's success in identifying cuprizone-induced demyelination. Our study showcases the adaptability and enhanced performance of the MIML approach under challenging preclinical 7T MRI conditions, contributing to the advancement of MWF estimation methods in high-field MRI settings.
