Diffusion weighted MRI imaging (DWI) is often subject to low signalto-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.
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.
Purpose: Diffusion-weighted imaging allows investigators to identify structural, microstructural, and connectivity-based differences between subjects, but variability due to session and scanner biases is a challenge. Methods: To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm 2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de-identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi-compartment neurite orientation dispersion and density model, (3) white-matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region-wise fractional anisotropy, mean diffusivity, and principal eigenvector; region-wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle-wise shape, | 3305 CAI et Al. F I G U R E 8Overall trends in coefficient of variation (CoV) across DTI, NODDI, bundle segmentation, and connectomics. Visualization of median CoV across all four processing approaches on the intrasession, intersession, interscanner, and intersubject levels illustrates consistently increased variability with session, scanner, and subject effects. Statistical significance was determined with the Wilcoxon signed-rank test with and without Bonferroni correction. The outlying points correspond to the NODDI cVF approach in white matter where absolute cVF values are expected to be low.
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.
Diffusion weighted imaging (DWI) allows investigators to identify microstructural differences between subjects, but variability due to session and scanner biases is still a challenge. To investigate DWI variability, we present MASiVar, a multisite dataset consisting of 319 diffusion scans acquired at 3T from b = 1000 to 3000 s/mm2 across 97 different healthy subjects and four different scanners as a publicly available, preprocessed, and de-identified dataset. With these data we characterize variability on the intrasession intrascanner (N = 158), intersession intrascanner (N = 328), intersession interscanner (N = 53), and intersubject intrascanner (N = 80) levels. Our baseline analysis focuses on four common DWI processing approaches: (1) a tensor signal representation, (2) a multi-compartment neurite orientation dispersion and density model, (3) white matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region-wise fractional anisotropy (FA), mean diffusivity, and principal eigenvector; region-wise cerebral spinal fluid volume fraction, intracellular volume fraction, and orientation dispersion index; bundle-wise shape, volume, length and FA; and connectome correlation and maximized modularity, global efficiency, and characteristic path length. We plot the scan/re-scan discrepancies in these measures at each level and find that variability generally increases with intrasession to intersession to interscanner to intersubject effects and that sometimes interscanner variability can approach intersubject variability. This baseline study suggests harmonization between scanners for multisite analyses is critical prior to inference of group differences on subjects and demonstrates the potential of MASiVar to investigate DWI variability across multiple levels and processing approaches simultaneously.
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