Subject-specific and hardware-specific bias removal from functional magnetic resonance imaging signals using deep learning

• Format: Patent
• Language: English
• Published: 24.10.2023

Anatomical, physiological, instrumental, and other related biases are removed from functional magnetic resonance imaging ("fMRI") signal data using deep learning algorithms and/or models, such as a neural network. Bias characterization data are used as an auxiliary input to the neural network. The bias characterization data can be subject-specific bias characterization data (e.g., cortical thickness maps, cortical orientation angle maps, vasculature maps), hardware-specific bias characterization data (e.g., coil sensitivity maps, coil transmission profiles), or both. The subject-specific bias characterization data can be extracted from the fMRI signal data using a second neural network. The bias-reduced fMRI signal data can include time-series signals, functional activation maps, functional connectivity maps, or combinations thereof.