Keywords: Machine Learning/Artificial Intelligence, Quantitative Susceptibility mappingMRI data is inherently complex-valued, the vast majority of deep learning frameworks do not yet support complex-valued data. Most reconstruction networks separate real and imaginary components into two separate real-valued channels, which may not be the most efficient way to represent complex numbers. Phase is essential for many MRI applications, including phase contrast velocity mapping and Quantitative Susceptibility Mapping (QSM) etc. We propose a new crRAKI, a scan-specific complex-valued residual convolutional neural network for 2D/3D MRI data for accelerating phase mapping and QSM. A comparison is made with GRAPPA and rRAKI for the accelerated reconstruction of MRI images.
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