Keywords: Artifacts, Artifacts
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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords