Which K-Space Sampling Schemes is good for Motion Artifact Detection in Magnetic Resonance Imaging?

TL;DR

This study compares how different k-space sampling schemes in MRI affect motion artifact detection and image quality, revealing that spiral sampling reduces artifacts, radial sampling offers robustness, and Cartesian sampling enhances motion detection accuracy.

Contribution

It provides a comprehensive analysis of three conventional k-space sampling schemes and their impact on motion artifacts and detection in MRI, using synthetic motion data and deep learning.

Findings

Spiral k-space sampling reduces motion artifacts in images.

Radial sampling offers greater robustness against motion artifacts.

Cartesian sampling improves deep learning-based motion detection.

Abstract

Motion artifacts are a common occurrence in the Magnetic Resonance Imaging (MRI) exam. Motion during acquisition has a profound impact on workflow efficiency, often requiring a repeat of sequences. Furthermore, motion artifacts may escape notice by technologists, only to be revealed at the time of reading by the radiologists, affecting their diagnostic quality. Designing a computer-aided tool for automatic motion detection and elimination can improve the diagnosis, however, it needs a deep understanding of motion characteristics. Motion artifacts in MRI have a complex nature and it is directly related to the k-space sampling scheme. In this study we investigate the effect of three conventional k-space samplers, including Cartesian, Uniform Spiral and Radial on motion induced image distortion. In this regard, various synthetic motions with different trajectories of displacement and rotation are applied to T1 and T2-weighted MRI images, and a convolutional neural network is trained to show the difficulty of motion classification. The results show that the spiral k-space sampling method get less effect of motion artifact in image space as compared to radial k-space sampled images, and radial k-space sampled images are more robust than Cartesian ones. Cartesian samplers, on the other hand, are the best in terms of deep learning motion detection because they can better reflect motion.