Image space formalism of convolutional neural networks for k-space interpolation.

ABSTRACT:

Purpose

Noise resilience in image reconstructions by scan-specific robust artificial neural networks for k-space interpolation (RAKI) is linked to nonlinear activations in k-space. To gain a deeper understanding of this relationship, an image space formalism of RAKI is introduced for analyzing noise propagation analytically, identifying and characterizing image reconstruction features and to describe the role of nonlinear activations in a human-readable manner.

Theory and methods

The image space formalism for RAKI inference is employed by expressing nonlinear activations in k-space as element-wise multiplications with activation masks, which transform into convolutions in image space. Jacobians of the de-aliased, coil-combined image relative to the aliased coil images can be

SUBMITTER: Dawood P 

PROVIDER: S-EPMC12501693 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature