Fast detection and reduction of local transient artifacts in resting-state fMRI

Hang Joon Jo; Richard C Reynolds; Stephen J Gotts; Daniel A Handwerker; Irena Balzekas; Alex Martin; Robert W Cox; Peter A Bandettini

doi:10.1016/j.compbiomed.2020.103742

Fast detection and reduction of local transient artifacts in resting-state fMRI

Comput Biol Med. 2020 May:120:103742. doi: 10.1016/j.compbiomed.2020.103742. Epub 2020 Apr 8.

Authors

Hang Joon Jo¹, Richard C Reynolds², Stephen J Gotts³, Daniel A Handwerker⁴, Irena Balzekas⁵, Alex Martin³, Robert W Cox², Peter A Bandettini⁴

Affiliations

¹ Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA; Department of Neurology, Mayo Clinic, Rochester, MN, USA; Department of Physiology, College of Medicine, Hanyang University, Seoul, South Korea. Electronic address: hangjoonjo@hanyang.ac.kr.
² Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
³ Section on Cognitive Neurophysiology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
⁴ Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
⁵ Department of Neurology, Mayo Clinic, Rochester, MN, USA.

Abstract

Image quality control (QC) is a critical and computationally intensive component of functional magnetic resonance imaging (fMRI). Artifacts caused by physiologic signals or hardware malfunctions are usually identified and removed during data processing offline, well after scanning sessions are complete. A system with the computational efficiency to identify and remove artifacts during image acquisition would permit rapid adjustment of protocols as issues arise during experiments. To improve the speed and accuracy of QC and functional image correction, we developed Fast Anatomy-Based Image Correction (Fast ANATICOR) with newly implemented nuisance models and an improved pipeline. We validated its performance on a dataset consisting of normal scans and scans containing known hardware-driven artifacts. Fast ANATICOR's increased processing speed may make real-time QC and image correction feasible as compared with the existing offline method.

Keywords: Artifact detection; Functional MRI; Online denoising; Real-time fMRI; Resting-state connectivity; Sliding-windowed timeseries.

Publication types

Research Support, N.I.H., Intramural
Research Support, Non-U.S. Gov't

MeSH terms

Artifacts*
Brain / diagnostic imaging
Brain Mapping
Image Processing, Computer-Assisted
Magnetic Resonance Imaging*
Quality Control

Grants and funding

Z01 MH002888/ImNIH/Intramural NIH HHS/United States