Analysis of deep complex-valued convolutional neural networks for MRI reconstruction and phase-focused applications.

ABSTRACT:

Purpose

Deep learning has had success with MRI reconstruction, but previously published works use real-valued networks. The few works which have tried complex-valued networks have not fully assessed their impact on phase. Therefore, the purpose of this work is to fully investigate end-to-end complex-valued convolutional neural networks (CNNs) for accelerated MRI reconstruction and in several phase-based applications in comparison to 2-channel real-valued networks.

Methods

Several complex-valued activation functions for MRI reconstruction were implemented, and their performance was compared. Complex-valued convolution was implemented and tested on an unrolled network architecture and a U-Net-based architecture over a wide range of network widths and depths with knee, body, an

SUBMITTER: Cole E 

PROVIDER: S-EPMC8291740 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature