Abstract We present MMORF—FSL’s MultiMOdal Registration Framework—a newly released nonlinear image registration tool designed primarily for application to magnetic resonance imaging (MRI) images of the brain. MMORF is capable of simultaneously optimising both displacement and rotational transformations within a single registration framework by leveraging rich information from multiple scalar and tensor modalities. The regularisation employed in MMORF promotes local rigidity in the deformation, and we have previously demonstrated how this effectively controls both shape and size distortion, leading to more biologically plausible warps. The performance of MMORF is benchmarked against three established nonlinear registration methods—FNIRT, ANTs, and DR-TAMAS—across four domains: FreeSurfer label overlap, diffusion tensor imaging (DTI) similarity, task-fMRI cluster mass, and distortion. The evaluation is based on 100 unrelated subjects from the Human Connectome Project (HCP) dataset registered to the Oxford-MultiModal-1 (OMM-1) multimodal template via either the T1w contrast alone or in combination with a DTI/DTI-derived contrast. Results show that MMORF is the most consistently high-performing method across all domains—both in terms of accuracy and levels of distortion. MMORF is available as part of FSL, and its inputs and outputs are fully compatible with existing workflows. We believe that MMORF will be a valuable tool for the neuroimaging community, regardless of the domain of any downstream analysis, providing state-of-the-art registration performance that integrates into the rich and widely adopted suite of analysis tools in FSL.
Abstract Background The medium-term effects of Coronavirus disease (COVID-19) on multiple organ health, exercise capacity, cognition, quality of life and mental health are poorly understood. Methods Fifty-eight COVID-19 patients post-hospital discharge and 30 comorbidity-matched controls were prospectively enrolled for multiorgan (brain, lungs, heart, liver and kidneys) magnetic resonance imaging (MRI), spirometry, six-minute walk test, cardiopulmonary exercise test (CPET), quality of life, cognitive and mental health assessments. Findings At 2-3 months from disease-onset, 64% of patients experienced persistent breathlessness and 55% complained of significant fatigue. On MRI, tissue signal abnormalities were seen in the lungs (60%), heart (26%), liver (10%) and kidneys (29%) of patients. COVID-19 patients also exhibited tissue changes in the thalamus, posterior thalamic radiations and sagittal stratum on brain MRI and demonstrated impaired cognitive performance, specifically in the executive and visuospatial domain relative to controls. Exercise tolerance (maximal oxygen consumption and ventilatory efficiency on CPET) and six-minute walk distance (405±118m vs 517±106m in controls, p<0.0001) were significantly reduced in patients. The extent of extra-pulmonary MRI abnormalities and exercise tolerance correlated with serum markers of ongoing inflammation and severity of acute illness. Patients were more likely to report symptoms of moderate to severe anxiety (35% versus 10%, p=0.012) and depression (39% versus 17%, p=0.036) and a significant impairment in all domains of quality of life compared to controls. Interpretation A significant proportion of COVID-19 patients discharged from hospital experience ongoing symptoms of breathlessness, fatigue, anxiety, depression and exercise limitation at 2-3 months from disease-onset. Persistent lung and extra-pulmonary organ MRI findings are common. In COVID-19 survivors, chronic inflammation may underlie multiorgan abnormalities and contribute to impaired quality of life. Funding NIHR Oxford and Oxford Health Biomedical Research Centres, British Heart Foundation Centre for Research Excellence, UKRI, Wellcome Trust, British Heart Foundation.
Abstract Purpose To evaluate the performance of an ensemble learning approach for fully automated cartilage segmentation on knee magnetic resonance images of patients with osteoarthritis. Materials and Methods This retrospective study of 88 participants with knee osteoarthritis involved the study of three-dimensional (3D) double echo steady state (DESS) MR imaging volumes with manual segmentations for 6 different compartments of cartilage (Data available from the Osteoarthritis Initiative). We propose ensemble learning to boost the sensitivity of our deep learning method by combining predictions from two models, a U-Net for the segmentation of two labels (cartilage vs background) and a multi-label U-Net for specific cartilage compartments. Segmentation accuracy is evaluated using Dice coefficient, while volumetric measures and Bland Altman plots provide complimentary information when assessing segmentation results. Results Our model showed excellent accuracy for all 6 cartilage locations: femoral 0.88, medial tibial 0.84, lateral tibial 0.88, patellar 0.85, medial meniscal 0.85 and lateral meniscal 0.90. The average volume correlation was 0.988, overestimating volume by 9% ± 14% over all compartments. Simple post processing creates a single 3D connected component per compartment resulting in higher anatomical face validity. Conclusion Our model produces automated segmentation with high Dice coefficients when compared to expert manual annotations and leads to the recovery of missing labels in the manual annotations, while also creating smoother, more realistic boundaries avoiding slice discontinuity artifacts present in the manual annotations. Key Results Combining a 2-label U-Net (cartilage vs background) with a multi-class U-Net for segmentation of cartilage compartment boosts the accuracy of our deep learning model leading to the recovery of missing annotations in the manual dataset. Automatically generated segmentations have high Dice coefficients (0.85-0.90) and reduce inter-slice discontinuity artefact caused by slice wise delineation. Model refinement yields more anatomically plausible segmentations where each cartilage label is composed of only a single 3D region of interest.
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. Highlights 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
Abstract Anatomical magnetic resonance imaging (MRI) templates of the brain are essential to group-level analyses and image processing pipelines, as they provide a reference space for spatial normalisation. While it has become common for studies to acquire multimodal MRI data, many templates are still limited to one type of modality, usually either scalar or tensor based. Aligning each modality in isolation does not take full advantage of the available complementary information, such as strong contrast between tissue types in structural images, or axonal organisation in the white matter in diffusion tensor images. Most existing strategies for multimodal template construction either do not use all modalities of interest to inform the template construction process, or do not use them in a unified framework. Here, we present multimodal, cross-sectional templates constructed from UK Biobank data: the Oxford-MultiModal-1 (OMM-1) template and age-dependent templates for each year of life between 45 and 81 years. All templates are fully unbiased to represent the average shape of the populations they were constructed from, and internally consistent through jointly informing the template construction process with T1-weighted (T1), T2-weighted fluid-attenuated inversion recovery (T2-FLAIR), and diffusion tensor imaging (DTI) data. The OMM-1 template was constructed with a multiresolution, iterative approach using 240 individuals in the 50–55-year age range. The age-dependent templates were estimated using a Gaussian process, which describes the change in average brain shape with age in 37,330 individuals. All templates show excellent contrast and alignment within and between modalities. The global brain shape and size are not preconditioned on existing templates, although maximal possible compatibility with MNI-152 space was maintained through rigid alignment. We showed benefits in registration accuracy across two datasets (UK Biobank and HCP), when using the OMM-1 as the template compared with FSL’s MNI-152 template, and found that the use of age-dependent templates further improved accuracy to a small but detectable extent. All templates are publicly available and can be used as a new reference space for uni- or multimodal spatial alignment.
ABSTRACT While diffusion MRI is typically used to estimate microstructural properties of tissue in volumetric elements (voxels), more specificity can be obtained by separately modelling the properties of individual fibre populations within a voxel. In the context of cross-subjects modelling, these so-called fixel-based analyses require identifying equivalent fibre populations. This is usually done post-hoc, after estimating fibre orientations for individual subjects independently and subsequently matching the fixels between subjects. This approach can fail due to individual differences in fibre orientation distributions. Here, we introduce a hierarchical framework for fitting crossing fibre models to diffusion MRI data in a population of subjects. This hierarchical setup guarantees that the crossing fibres are consistent by construction and, therefore, comparable across subjects. We propose an expectation-maximisation approach to fit the model, which can scale to large numbers of subjects. This approach produces a crossing-fibre white matter fibre template, which can be used to estimate fibre-specific parameters that are consistent across subjects and, hence, can be used in fixel-based statistical analyses.
Abstract While diffusion MRI is typically used to estimate microstructural properties of tissue in volumetric elements (voxels), more specificity can be obtained by separately modelling the properties of individual fibre populations within a voxel. In the context of cross-subjects modelling, these fixel-based analyses are usually performed in two stages. Crossing-fibre modelling is first performed in each subject to produce fixels, and these are subsequently modelled across subjects following registration and fibre population reassignment. Here, we introduce a new hierarchical framework for fitting crossing fibre models to diffusion MRI data in a population of subjects. This hierarchical setup guarantees that the crossing fibres are consistent by construction and, therefore, comparable across subjects. We propose an expectation-maximisation algorithm to fit the model, which can scale to large numbers of subjects. This approach produces a crossing-fibre white matter fibre population template which can be used to estimate fibre-specific parameters that are consistent across subjects, hence providing data that are by construction suitable for fixel-based statistical analyses.
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