A deep-learning-based framework for spark artifacts detection and correction

Motivation: The occasional occurrence of spark artifacts in MRI could significantly hinder diagnosis. Previous whole K-space segmentation methods are unstable and require stringent intensity normalization. Goal(s): In this study, we aim to improve the accuracy and stability of spark identification, especially for sparks with low intensities and near the K-space center. Approach: We propose a two-step deep learning-based framework consisting of spark patch classification and patch-level spark segmentation, which are further corrected by ESPIRiT. Results: The proposed methods are demonstrated to be effective and robust on various imaging protocols and body parts for different degrees of spark artifacts. Impact: Incidental spark artifacts in MRI can significantly hinder diagnosis. We developed a deep-learning-based two-step framework for robust spark detection and correction, which has been validated to be effective on a variety of imaging protocols for different degrees of spark artifacts.