A novel image space formalism of Fourier domain interpolation neural networks for noise propagation analysis

arXiv:2402.17410v1 Announce Type: cross
Abstract: Purpose: To develop an image space formalism of multi-layer convolutional neural networks (CNNs) for Fourier domain interpolation in MRI reconstructions and analytically estimate noise propagation during CNN inference. Theory and Methods: Nonlinear activations in the Fourier domain (also known as k-space) using complex-valued Rectifier Linear Units are expressed as elementwise multiplication with activation masks. This operation is transformed into a convolution in the image space. After network training in k-space, this approach provides an algebraic …

abstract analysis arxiv cnn cnns convolutional neural networks cs.ai cs.cv cs.lg domain fourier image inference layer mri networks neural networks noise novel physics.med-ph propagation space theory type