Improving high frequency image features of deep learning reconstructions via k-space refinement with null-space kernel.

ABSTRACT:

Purpose

Deep learning (DL) based reconstruction using unrolled neural networks has shown great potential in accelerating MRI. However, one of the major drawbacks is the loss of high-frequency details and textures in the output. The purpose of the study is to propose a novel refinement method that uses null-space kernel to refine k-space and improve blurred image details and textures.

Methods

The proposed method constrains the output of the DL to comply to the linear neighborhood relationship calibrated in the auto-calibration lines. To demonstrate efficacy, we tested our refinement method on the DL reconstruction under a variety of conditions (i.e., dataset, unrolled neural networks, and under-sampling scheme). Specifically, the method was tested on three large-scale public

SUBMITTER: Ryu K 

PROVIDER: S-EPMC9246879 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature