Scan-specific, Parameter-free Artifact Reduction in K-space (SPARK)

We propose a convolutional neural network (CNN) approach that works synergistically with physics-based reconstruction methods to reduce artifacts in accelerated MRI. Given reconstructed coil k-spaces, our network predicts a k-space correction term for each coil. This is done by matching the differen...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2019-11
Main Authors: Beker, Onur, Liao, Congyu, Cho, Jaejin, Zhang, Zijing, Kawin Setsompop, Bilgic, Berkin
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Coils
High acceleration
Reconstruction
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We propose a convolutional neural network (CNN) approach that works synergistically with physics-based reconstruction methods to reduce artifacts in accelerated MRI. Given reconstructed coil k-spaces, our network predicts a k-space correction term for each coil. This is done by matching the difference between the acquired autocalibration lines and their erroneous reconstructions, and generalizing this error term over the entire k-space. Application of this approach on existing reconstruction methods show that SPARK suppresses reconstruction artifacts at high acceleration, while preserving and improving on detail in moderate acceleration rates where existing reconstruction algorithms already perform well; indicating robustness. Introduction Parallel
ISSN:2331-8422