Quality assessment software tools | Magnetic resonance imaging analysis
Quality control of MRI is essential for excluding problematic acquisitions and avoiding bias in subsequent image processing and analysis. Visual inspection is subjective and impractical for large scale datasets. Although automated quality assessments have been demonstrated on single-site datasets, it is unclear that solutions can generalize to unseen data acquired at new sites.
Hosts a Simulated Brain Database (SBD) and allows users to run custom MRI simulations with any of several pulse sequences and source digital phantoms, and arbitrary values of the acquisition artifacts. BrainWeb database contains a set of realistic magnetic resonance imaging (MRI) data volumes produced by an MRI simulator. These data can be used by the neuroimaging community to evaluate the performance of various image analysis methods in a setting where the truth is known. The SBD contains simulated brain MRI data based on two anatomical models: normal and multiple sclerosis (MS). For both of these, full 3-dimensional data volumes have been simulated using three sequences (T1-, T2-, and proton-density- (PD-) weighted) and a variety of slice thicknesses, noise levels, and levels of intensity non-uniformity. These data are available for viewing in three orthogonal views (transversal, sagittal, and coronal), and for downloading.
A framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). A BIDS App is a container image capturing a neuroimaging pipeline that takes a BIDS-formatted dataset as input. Each BIDS App has the same core set of command line arguments, making them easy to run and integrate into automated platforms. BIDS Apps are constructed in a way that does not depend on any software outside of the container mage other than the container engine.
Offers a set of methods for computational anatomy. CAT extends segmentation methods supplied by the SPM software by furnishing two approaches to reduce noise, an internal interpolation as well as multiple techniques encompassing local adaptive segmentation (LAS). The application enables application in: (i) voxel-based morphometry (VBM); (ii) deformation-based morphometry (DBM); (iii) region- or label-based morphometry (RBM) as well as (iv) surface-based morphometry (SBM).
Provides an open-source web application for the collaborative quality control of neuroimaging processing outputs. The Mindcontrol platform consists of a dashboard to organize data, descriptive visualizations to explore the data, an imaging viewer, and an in-browser annotation and editing toolbox for data curation and quality control. Mindcontrol is flexible and can be configured for the outputs of any software package in any data organization structure. Example configurations for three large, open-source datasets are tested: the 1000 Functional Connectomes Project (FCP), the Consortium for Reliability and Reproducibility (CoRR), and the Autism Brain Imaging Data Exchange (ABIDE) Collection. These demo applications link descriptive quality control metrics, regional brain volumes, and thickness scalars to a 3D imaging viewer and editing module, resulting in an easy-to-implement quality control protocol that can be scaled for any size and complexity of study.
Provides a series of image processing workflows to extract and compute a series of no-reference, image quality metrics (IQMs) to be used in quality assessment protocols for magnetic resonance imaging (MRI). MRIQC extracts a series of IQMs from structural and functional MRI data. It takes as principal input the path of the dataset that is to be processed.
Permits to reduce rater bias and misclassification in manual quality control (QC) procedures. Qoala-T is a data-driven approach that uses a supervised-learning model. It requires manual QC only on a subset of data that is subsequently used as input for the supervised learning model. It also includes R code of all steps used in the development of this tool in order to advance the reproducibility of the analysis.