Details

Autor(en) / Beteiligte

• 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.

Titel

PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images

Ist Teil von

• Magnetic resonance in medicine, 2021-07, Vol.86 (1), p.456-470

Ort / Verlag

United States: Wiley Subscription Services, Inc

Erscheinungsjahr

2021

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.