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

PurposeDiffusion weighted MRI imaging (DWI) is often subject to low signal-to-noise ratios (SNRs) and artifacts. Recent work has produced software tools that can each correct an individual problem, but these tools have not been combined with each other or with quality assurance (QA). Thus, a single integrated pipeline is proposed to perform DWI preprocessing and tensor fitting with a spectrum of tools and produce an intuitive QA document. MethodsThe proposed pipeline is built around the FSL, MRTrix3, and ANTs software packages to perform DWI denoising; inter-scan intensity normalization; susceptibility-, eddy current-, and motion-induced artifact correction; slice-wise signal drop-out imputation; and tensor fitting. For QA, each operation is visualized alongside qualitative analyses, gradient and fractional anisotropy verifications, and a tensor goodness-of-fit analysis. ResultsRaw 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. ConclusionThe proposed pipeline is a single integrated pipeline that combines established diffusion preprocessing tools from major MRI-focused software packages with intuitive QA.