Incorporating a-priori information in deep learning models for quantitative susceptibility mapping via adaptive convolution.

ABSTRACT: Quantitative susceptibility mapping (QSM) has attracted considerable interest for tissue characterization (e.g., iron and calcium accumulation, myelination, venous vasculature) in the human brain and relies on extensive data processing of gradient-echo MRI phase images. While deep learning-based field-to-susceptibility inversion has shown great potential, the acquisition parameters applied in clinical settings such as image resolution or image orientation with respect to the magnetic field have not been fully accounted for. Furthermore, the lack of comprehensive training data covering a wide range of acquisition parameters further limits the current QSM deep learning approaches. Here, we propose the integration of a priori information of imaging parameters into convolutional neural network

SUBMITTER: Graf S 

PROVIDER: S-EPMC10962327 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature