Keywords: Machine Learning/Artificial Intelligence, Parallel Imaging, complex-valued convolutional neural networks, RAKI, GRAPPA, ReLU
Motivation: Robust Artificial Neural Networks for k-space Interpolation (RAKI) exhibit superior image reconstructions compared to traditional Parallel Imaging. It is crucial to thoroughly characterize RAKI to gain insights into its functionality and stimulate further enhancements.
Goal(s): Exploring how k-space interpolation with convolutional neural networks can be transformed into image domain to obtain an analytical description of noise characteristics.
Approach: The nonlinear activation in k-space is expressed as elementwise multiplication. This can be transformed into convolution in image space.
Results: The proposed image space formalism yields image reconstructions quasi-equivalent to k-space interpolation. The analytical expression of noise characteristics is in correspondence with Monte Carlo simulations.
Impact: We propose an image space formalism for k-space interpolation with convolutional neural networks. This enables an analytical expression of the noise characteristics, analogous to g-factor maps in traditional parallel imaging methods.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords