QSIPrep: An integrative platform for preprocessing and reconstructing diffusion MRI (original) (raw)
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Dipy, a library for the analysis of diffusion MRI data
Frontiers in Neuroinformatics, 2014
Imaging in Python (Dipy) is a free and open source software project for the analysis of data from diffusion magnetic resonance imaging (dMRI) experiments. dMRI is an application of MRI that can be used to measure structural features of brain white matter. Many methods have been developed to use dMRI data to model the local configuration of white matter nerve fiber bundles and infer the trajectory of bundles connecting different parts of the brain. Dipy gathers implementations of many different methods in dMRI, including: diffusion signal pre-processing; reconstruction of diffusion distributions in individual voxels; fiber tractography and fiber track post-processing, analysis and visualization. Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface. We have implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography. In addition, cutting edge novel reconstruction techniques are implemented, such as constrained spherical deconvolution and diffusion spectrum imaging (DSI) with deconvolution, as well as methods for probabilistic tracking and original methods for tractography clustering. Many additional utility functions are provided to calculate various statistics, informative visualizations, as well as file-handling routines to assist in the development and use of novel techniques. In contrast to many other scientific software projects, Dipy is not being developed by a single research group. Rather, it is an open project that encourages contributions from any scientist/developer through GitHub and open discussions on the project mailing list. Consequently, Dipy today has an international team of contributors, spanning seven different academic institutions in five countries and three continents, which is still growing.
Camino: Diffusion MRI reconstruction and processing
2005
Camino is an open-source, object-oriented software package for processing diffusion MRI data. Camino implements a data processing pipeline, which allows for easy scripting and flexible integration with other software. This paper summarises the features of Camino at each stage of the pipeline from the raw data to the statistics used by clinicians and researchers. The paper also discusses the role of Camino in the paper "An Automated Approach to Connectivity-based Partitioning of Brain Structures", published at MICCAI 2005.
A review of diffusion tensor magnetic resonance imaging computational methods and software tools
Computers in Biology and Medicine, 2011
In this work we provide an up-to-date short review of computational magnetic resonance imaging (MRI) and software tools that are widely used to process and analyze diffusion-weighted MRI data. A review of different methods used to acquire, model and analyze diffusion-weighted imaging data (DWI) is first provided with focus on diffusion tensor imaging (DTI). The major preprocessing, processing and post-processing procedures applied to DTI data are discussed. A list of freely available software packages to analyze diffusion MRI data is also provided.
Magnetic Resonance in Medicine, 2021
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 slicewise 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. Conclusion The proposed pipeline is a single integrated pipeline that combines established diffusion preprocessing tools from major MRI-focused software packages with intuitive QA.
2020
ABSTRACTPurposeDiffusion weighted imaging (DWI) allows investigators to identify structural, microstructural, and connectivitybased differences between subjects, but variability due to session and scanner biases is a challenge.MethodsTo 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 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de-identified dataset. 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 (FA), mean...
Representing Diffusion MRI in 5-D Simplifies Regularization and Segmentation of White Matter Tracts
IEEE Transactions on Medical Imaging, 2000
We present a new five-dimensional (5-D) space representation of diffusion magnetic resonance imaging (dMRI) of high angular resolution. This 5-D space is basically a non-Euclidean space of position and orientation in which crossing fiber tracts can be clearly disentangled, that cannot be separated in three-dimensional position space. This new representation provides many possibilities for processing and analysis since classical methods for scalar images can be extended to higher dimensions even if the spaces are not Euclidean. In this paper, we show examples of how regularization and segmentation of dMRI is simplified with this new representation. The regularization is used with the purpose of denoising and but also to facilitate the segmentation task by using several scales, each scale representing a different level of resolution. We implement in five dimensions the Chan-Vese method combined with active contours without edges for the segmentation and the total variation functional for the regularization. The purpose of this paper is to explore the possibility of segmenting white matter structures directly as entirely separated bundles in this 5-D space. We will present results from a synthetic model and results on real data of a human brain acquired with diffusion spectrum magnetic resonance imaging (MRI), one of the dMRI of high angular resolution available. These results will lead us to the conclusion that this new high-dimensional representation indeed simplifies the problem of segmentation and regularization. Index Terms-diffusion magnetic resonance imaging (MRI), five dimensional level sets, white matter segmentation and position orientation space. I. INTRODUCTION D IFFUSION magnetic resonance imaging (dMRI) is a modality that permits noninvasive quantification of water diffusion in living tissues. The tissue structure affects the Brownian motion of the water molecules, which leads to an anisotropic diffusion. Today, a diffusion tensor (DT) model [1],
RadioGraphics, 2006
The complex structural organization of the white matter of the brain can be depicted in vivo in great detail with advanced diffusion magnetic resonance (MR) imaging schemes. Diffusion MR imaging techniques are increasingly varied, from the simplest and most commonly used technique-the mapping of apparent diffusion coefficient values-to the more complex, such as diffusion tensor imaging, q-ball imaging, diffusion spectrum imaging, and tractography. The type of structural information obtained differs according to the technique used. To fully understand how diffusion MR imaging works, it is helpful to be familiar with the physical principles of water diffusion in the brain and the conceptual basis of each imaging technique. Knowledge of the technique-specific requirements with regard to hardware and acquisition time, as well as the advantages, limitations, and potential interpretation pitfalls of each technique, is especially useful.
2021
ABSTRACTThe validation of advanced methods in diffusion MRI requires finer acquisition resolutions, which is hard to acquire with decent Signal-to-Noise Ratio (SNR) in humans. The use of Non-Human Primates (NHP) and anaesthesia is key to unlock valid microstructural maps, but tools must be adapted and configured finely for them to work well. Here, we propose a novel processing pipeline implemented in Nextflow, designed for robustness and scalability, in a modular fashion to allow for maintainability and a high level of customization and parametrization, tailored for the analysis of diffusion data acquired on multiple spatial resolutions. Modules of processes and workflows were implemented upon cutting edge and state-of-the-art MRI processing technologies and diffusion modelling algorithms, namely Diffusion Tensor Imaging (DTI), Constrained Spherical Deconvolution (CSD) and DIstribution of Anisotropic MicrOstructural eNvironments in Diffusion-compartment imaging (DIAMOND), a multi-te...