Realistic simulation of artefacts in diffusion MRI for validating post-processing correction techniques

AbstractIn this paper we demonstrate a simulation framework that enables the direct and quantitative comparison of post-processing methods for diffusion weighted magnetic resonance (DW-MR) images. DW-MR datasets are employed in a range of techniques that enable estimates of local microstructure and global connectivity in the brain. These techniques require full alignment of images across the dataset, but this is rarely the case. Artefacts such as eddy-current (EC) distortion and motion lead to misalignment between images, which compromise the quality of the microstructural measures obtained from them. Numerous methods and software packages exist to correct these artefacts, some of which have become de-facto standards, but none have been subject to rigorous validation. In the literature, improved alignment is assessed using either qualitative visual measures or quantitative surrogate metrics. Here we introduce a simulation framework that allows for the direct, quantitative assessment of techniques, enabling objective comparisons of existing and future methods. DW-MR datasets are generated using a process that is based on the physics of MRI acquisition, which allows for the salient features of the images and their artefacts to be reproduced. We apply this framework in three ways. Firstly we assess the most commonly used method for artefact correction, FSL's eddy_correct, and compare it to a recently proposed alternative, eddy. We demonstrate quantitatively that using eddy_correct leads to significant errors in the corrected data, whilst eddy is able to provide much improved correction. Secondly we investigate the datasets required to achieve good correction with eddy, by looking at the minimum number of directions required and comparing the recommended full-sphere acquisitions to equivalent half-sphere protocols. Finally, we investigate the impact of correction quality by examining the fits from microstructure models to real and simulated data

The LONI QC System: A Semi-Automated, Web-Based and Freely-Available Environment for the Comprehensive Quality Control of Neuroimaging Data.

Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI): a web-based system featuring a workflow for the assessment of various modality and contrast brain imaging data. The design allows users to anonymously upload imaging data to the LONI-QC system. It then computes an exhaustive set of QC metrics which aids users to perform a standardized QC by generating a range of scalar and vector statistics. These procedures are performed in parallel using a large compute cluster. Finally, the system offers an automated QC procedure for structural MRI, which can flag each QC metric as being 'good' or 'bad.' Validation using various sets of data acquired from a single scanner and from multiple sites demonstrated the reproducibility of our QC metrics, and the sensitivity and specificity of the proposed Auto QC to 'bad' quality images in comparison to visual inspection. To the best of our knowledge, LONI-QC is the first online QC system that uniquely supports the variety of functionality where we compute numerous QC metrics and perform visual/automated image QC of multi-contrast and multi-modal brain imaging data. The LONI-QC system has been used to assess the quality of large neuroimaging datasets acquired as part of various multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer's Disease Neuroimaging Initiative (ADNI). LONI-QC's functionality is freely available to users worldwide and its adoption by imaging researchers is likely to contribute substantially to upholding high standards of brain image data quality and to implementing these standards across the neuroimaging community

Correction of spatial distortion in magnetic resonance imaging

Dissertation to Obtain the Degree of Master in Biomedical EngineeringMagnetic Resonance Imaging (MRI) has been a major investigation and research focus among scientific and medical communities. So, new hardware with superior magnetic fields and faster sequences has been developed. However, these improvements result in intensity and spatial distortions, particularly in fast sequences, as Echo Plana Imaging (EPI), used in functional and diffusion-weighed MRI (fMRI and DW-MRI). Therefore, correction of spatial distortion is useful to obtain a higher quality in this kind of images. This project contains two major parts. The first part consists in simulating MRI data required for assessing the performance of Registration methods and optimizing parameters. To assess the methods five evaluation metrics were calculated between the corrected data and an undistorted EPI, namely: Root Mean Square (RMS); Normalized Mutual Information (NMI), Squared Correlation Coefficient(SCC); Euclidean Distance of Centres of Mass (CM) and Dice Coefficient of segmented images. In brief, this part validates the applied Registration correction method. The project’s second part includes correction of real images, obtained at a Clinical Partner. Real images are diffusion weighted MRI data with different b-values (gradient strength coefficient), allowing performance assessment of different methods on images with increasing b-values and decreasing SNR. The methods tested on real data were Registration, Field Map correction and a new proposed pipeline, which consists in performing a Field Map correction after a registration process. To assess the accuracy of these methods on real data, we used the same evaluation metrics, as for simulated data, except RMS and Dice Coefficient. At the end, it was concluded that Registration-based methods are better than Field Map, and that the new proposed pipeline produces some improvements in the registration. Regarding the influence of b-value on the correction, it is important to say that the methods performed using images with higher b’s showed more improvements in regarding metric values, but the behaviour is similar for all b-values