ORCID Profile
0000-0002-2511-3189
Current Organisation
University of Oxford
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Publisher: Elsevier BV
Date: 2014
Publisher: Wiley
Date: 25-05-2010
DOI: 10.1002/MRM.22318
Abstract: The inherent distortions in echo-planar imaging that arise due to inhomogeneities in the static magnetic field can lead to difficulties when attempting to obtain structurally accurate diffusion-tensor imaging data. Parallel acceleration techniques can reduce the magnitude of these distortions but do not remove them entirely. Images can be corrected using a measured field map, but this is prone to error. One approach to correcting for these distortions, referred to here as "blip-reversed" echo-planar imaging, involves collecting a second set of images with the phase encoding reversed. Here, a novel approach to collecting blip-reversed echo-planar imaging data for diffusion-tensor imaging is presented: a dual-echo sequence is used in which the phase-encoding direction of the second echo is swapped compared to the first echo. This allows benefits of the blip-reversed approach to be exploited, with only a modest increase in scan time and, due to the extra data acquired, no significant loss of signal-to-noise efficiency. A novel approach to recombining blip-reversed data is also presented, which involves refining the measured field map, using an algorithm to minimize the difference between the corrected images. The field map refinement is also applicable to conventionally acquired blip-reversed sequences.
Publisher: Cold Spring Harbor Laboratory
Date: 19-06-2023
DOI: 10.1101/2023.06.16.545260
Abstract: Despite the huge potential of magnetic resonance imaging (MRI) in mapping and exploring the brain, MRI measures can often be limited in their consistency, reproducibility and accuracy which subsequently restricts their quantifiability. Nuisance nonbiological factors, such as hardware, software, calibration differences between scanners, and post-processing options can contribute to, or drive trends in, neuroimaging features to an extent that interferes with biological variability. Such lack of consistency, known as lack of harmonisation, across neuroimaging datasets poses a great challenge for our capabilities in quantitative MRI. Here, we build a new resource for comprehensively mapping the extent of the problem and objectively evaluating neuroimaging harmonisation approaches. We use a travelling-heads paradigm consisting of multimodal MRI data of 10 travelling subjects, each scanned at 5 different sites on 6 different 3T scanners from all the 3 major vendors and using 5 neuroimaging modalities, providing more comprehensive coverage than before. We also acquire multiple within-scanner repeats for a subset of subjects, setting baselines for multi-modal scan-rescan variability. Having extracted hundreds of image-derived features, we compare three forms of variability: (i) between-scanner, (ii) within-scanner (within-subject), and (iii) biological (between-subject). We characterise the reliability of features across scanners and use our resource as a testbed to enable new investigations that until now have been relatively unexplored. Specifically, we identify optimal pipeline processing steps that minimise between-scanner variability in extracted features (implicit harmonisation). We also test the performance of post-processing harmonisation tools (explicit harmonisation) and specifically check their efficiency in reducing between-scanner variability against baseline standards provided by our data. Our explorations allow us to come up with good practice suggestions on processing steps and sets of features where results are more consistent, while our publicly-released datasets establish references for future studies in this field.
Publisher: Cold Spring Harbor Laboratory
Date: 26-11-2019
DOI: 10.1101/849570
Abstract: There is a need to understand the histopathological basis of MRI signal characteristics in complex biological matter. Microstructural imaging holds promise for sensitive and specific indicators of the early stages of human neurodegeneration but requires validation against traditional histological markers before it can be reliably applied in the clinical setting. Validation relies on a precise and preferably automatic method to align MRI and histological images of the same tissue, which poses unique challenges compared to more conventional MRI-to-MRI registration. A customisable open-source platform, Tensor Image Registration Library (TIRL) is presented. Based on TIRL, a fully automated pipeline was implemented to align small stained histological images with dissection photographs of corresponding tissue blocks and coronal brain slices, and further with high-resolution (0.5 mm) whole-brain post-mortem MRI data. The pipeline performed three separate deformable registrations to achieve accurate mapping between whole-brain MRI and small-slide histology coordinates. The robustness and accuracy of the in idual registration steps were evaluated using both simulated data and real-life images from 6 different anatomical locations of one post-mortem human brain. The automated registration method demonstrated sub-millimetre accuracy in all steps, robustness against tissue damage, and good reproducibility between experiments. The method also outperformed manual landmark-based slice-to-volume registration, also correcting for curvatures in the slicing plane. Due to the customisability of TIRL, the pipeline can be conveniently adapted for other research needs and is therefore suitable for the large-scale comparison of routinely collected histology and MRI data. TIRL: new framework for prototyping bespoke image registration pipelines Pipeline for automated registration of small-slide histology to whole-brain MRI Slice-to-volume registration accounting for through-plane deformations No need for serial histological s ling
Publisher: Springer Science and Business Media LLC
Date: 13-03-2018
Publisher: Cold Spring Harbor Laboratory
Date: 19-05-2021
DOI: 10.1101/2021.05.19.21257316
Abstract: SARS-CoV-2 infection has been shown to damage multiple organs, including the brain. Multiorgan MRI can provide further insight on the repercussions of COVID-19 on organ health but requires a balance between richness and quality of data acquisition and total scan duration. We adapted the UK Biobank brain MRI protocol to produce high-quality images while being suitable as part of a post-COVID-19 multiorgan MRI exam. The analysis pipeline, also adapted from UK Biobank, includes new imaging-derived phenotypes (IDPs) designed to assess the effects of COVID-19. A first application of the protocol and pipeline was performed in 51 COVID-19 patients post-hospital discharge and 25 controls participating in the Oxford C-MORE study. The protocol acquires high resolution T 1 , T 2 -FLAIR, diffusion weighted images, susceptibility weighted images, and arterial spin labelling data in 17 minutes. The automated imaging pipeline derives 1575 IDPs, assessing brain anatomy (including olfactory bulb volume and intensity) and tissue perfusion, hyperintensities, diffusivity, and susceptibility. In the C-MORE data, these quantitative measures were consistent with clinical radiology reports. Our exploratory analysis tentatively revealed that recovered COVID-19 patients had a decrease in frontal grey matter volumes, an increased burden of white matter hyperintensities, and reduced mean diffusivity in the total and normal appearing white matter in the posterior thalamic radiation and sagittal stratum, relative to controls. These differences were generally more prominent in patients who received organ support. Increased T 2 * in the thalamus was also observed in recovered COVID-19 patients, with a more prominent increase for non-critical patients. This initial evidence of brain changes in COVID-19 survivors prompts the need for further investigations. Follow-up imaging in the C-MORE study is currently ongoing, and this protocol is now being used in large-scale studies. The pipeline is widely applicable and will contribute to new analyses to hopefully clarify the medium to long-term effects of COVID-19. UK Biobank brain MRI protocol and pipeline was adapted for multiorgan MRI of COVID-19 High-quality brain MRI data from 5 modalities are acquired in 17 minutes Analysis pipeline derives 1575 IDPs of brain anatomy, perfusion, and microstructure Evidence of brain changes in COVID-19 survivors was found in the C-MORE study This MRI protocol is now being used in multiple large-scale studies on COVID-19
Publisher: Elsevier BV
Date: 2021
Publisher: Proceedings of the National Academy of Sciences
Date: 07-02-2012
Abstract: Resting-state functional magnetic resonance imaging has become a powerful tool for the study of functional networks in the brain. Even “at rest,” the brain's different functional networks spontaneously fluctuate in their activity level each network's spatial extent can therefore be mapped by finding temporal correlations between its different subregions. Current correlation-based approaches measure the average functional connectivity between regions, but this average is less meaningful for regions that are part of multiple networks one ideally wants a network model that explicitly allows overlap, for ex le, allowing a region's activity pattern to reflect one network's activity some of the time, and another network's activity at other times. However, even those approaches that do allow overlap have often maximized mutual spatial independence, which may be suboptimal if distinct networks have significant overlap. In this work, we identify functionally distinct networks by virtue of their temporal independence, taking advantage of the additional temporal richness available via improvements in functional magnetic resonance imaging s ling rate. We identify multiple “temporal functional modes,” including several that sub ide the default-mode network (and the regions anticorrelated with it) into several functionally distinct, spatially overlapping, networks, each with its own pattern of correlations and anticorrelations. These functionally distinct modes of spontaneous brain activity are, in general, quite different from resting-state networks previously reported, and may have greater biological interpretability.
Publisher: Oxford University Press (OUP)
Date: 10-2012
DOI: 10.1093/BRAIN/AWS242
Publisher: Elsevier BV
Date: 11-2020
Publisher: Springer Science and Business Media LLC
Date: 03-2007
Abstract: There is much interest in using magnetic resonance diffusion imaging to provide information on anatomical connectivity in the brain by measuring the diffusion of water in white matter tracts. Among the measures, the most commonly derived from diffusion data is fractional anisotropy (FA), which quantifies local tract directionality and integrity. Many multi-subject imaging studies are using FA images to localize brain changes related to development, degeneration and disease. In a recent paper, we presented a new approach, tract-based spatial statistics (TBSS), which aims to solve crucial issues of cross-subject data alignment, allowing localized cross-subject statistical analysis. This works by transforming the data from the centers of the tracts that are consistent across a study's subjects into a common space. In this protocol, we describe the MRI data acquisition and analysis protocols required for TBSS studies of localized change in brain connectivity across multiple subjects.
Publisher: Cold Spring Harbor Laboratory
Date: 24-04-2017
DOI: 10.1101/130385
Abstract: UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10,000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline.
Publisher: Elsevier BV
Date: 07-2011
Publisher: Elsevier BV
Date: 12-2013
Publisher: Elsevier BV
Date: 2007
DOI: 10.1016/J.NEUROIMAGE.2006.09.019
Abstract: Optimising the efficiency of an experimental design is known to be of great importance. However, existing methods for calculating design rank deficiency and contrast estimability (an important aspect of experimental design) relate to computational precision rather than image noise and are therefore not very meaningful. For ex le, a contrast between two experimental conditions may be mathematically "estimable" while requiring a huge differential BOLD response for statistical significance to be reached. In this paper we formulate standard efficiency equations in terms of required BOLD effect, and use this to generate measures of rank/estimability which are meaningful. This takes into account the strength and smoothness of the timeseries noise and is applicable to complex contrasts we show how to re-express several regressors and an associated contrast vector as a single equivalent regressor, so that we can calculate the contrast's effective peak-peak height unambiguously. We also present some ex le results on typical designs, and characterise noise results from a range of typical FMRI acquisitions, in order to allow experimenters to apply efficiency estimation in advance of acquiring data.
Publisher: Springer Science and Business Media LLC
Date: 30-05-2014
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Karla Miller.