Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging.

ABSTRACT:

Purpose

To develop an improved k-space reconstruction method using scan-specific deep learning that is trained on autocalibration signal (ACS) data.

Theory

Robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction trains convolutional neural networks on ACS data. This enables nonlinear estimation of missing k-space lines from acquired k-space data with improved noise resilience, as opposed to conventional linear k-space interpolation-based methods, such as GRAPPA, which are based on linear convolutional kernels.

Methods

The training algorithm is implemented using a mean square error loss function over the target points in the ACS region, using a gradient descent algorithm. The neural network contains 3 layers of convolutional operators, with 2

SUBMITTER: Akcakaya M 

PROVIDER: S-EPMC6258345 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature