PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images. Issue 1 (3rd February 2021)

• Record Type: Journal Article
• Authors: Cai, Leon Y.; Yang, Qi; Hansen, Colin B.; Nath, Vishwesh; Ramadass, Karthik; Johnson, Graham W.; Conrad, Benjamin N.; Boyd, Brian D.; Begnoche, John P.; Beason‐Held, Lori L.; Shafer, Andrea T.; Resnick, Susan M.; Taylor, Warren D.; Price, Gavin R.; Morgan, Victoria L.; Rogers, Baxter P.; Schilling, Kurt G.; Landman, Bennett A.
• Is Part Of: Magnetic resonance in medicine. Volume 86:Issue 1(2021)
• Page Start: 456
• Page End: 470
• Publication Date: 2021-02-03
• Subjects: denoising -- diffusion weighted imaging -- distortion correction -- DTI -- preprocessing -- quality assurance; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.28678
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: Diffusion weighted MRI imaging (DWI) is often subject to low signal‐to‐noise ratios (SNRs) and artifacts. Recent work has produced software tools that can correct individual problems, but these tools have not been combined with each other and with quality assurance (QA). A single integrated pipeline is proposed to perform DWI preprocessing with a spectrum of tools and produce an intuitive QA document. Methods: The proposed pipeline, built around the FSL, MRTrix3, and ANTs software packages, performs DWI denoising; inter‐scan intensity normalization; susceptibility‐, eddy current‐, and motion‐induced artifact correction; and slice‐wise signal drop‐out imputation. To perform QA on the raw and preprocessed data and each preprocessing operation, the pipeline documents qualitative visualizations, quantitative plots, gradient verifications, and tensor goodness‐of‐fit and fractional anisotropy analyses. Results: Raw DWI data were preprocessed and quality checked with the proposed pipeline and demonstrated improved SNRs; physiologic intensity ratios; corrected susceptibility‐, eddy current‐, and motion‐induced artifacts; imputed signal‐lost slices; and improved tensor fits. The pipeline identified incorrect gradient configurations and file‐type conversion errors and was shown to be effective on externally available datasets. Conclusions: The proposed pipeline is a single integrated pipeline that combines established diffusion preprocessing tools from major MRI‐focused software packages with intuitive QA.