Effect of socioeconomic deprivation as determined by the English deprivation deciles on the progression of diabetic retinopathy and maculopathy: a multivariate case-control analysis of 88 910 patients attending a South-East London diabetic eye screening service.

PURPOSE: To determine associations between deprivation using the Index of Multiple Deprivation (IMD and individual IMD subdomains) with incident referable diabetic retinopathy/maculopathy (termed rDR). METHODS: Anonymised demographic and screening data collected by the South-East London Diabetic Eye Screening Programme were extracted from September 2013 to December 2019. Multivariable Cox proportional models were used to explore the association between the IMD, IMD subdomains and rDR. RESULTS: From 118 508 people with diabetes who attended during the study period, 88 910 (75%) were eligible. The mean (± SD) age was 59.6 (±14.7) years; 53.94% were male, 52.58% identified as white, 94.28% had type 2 diabetes and the average duration of diabetes was 5.81 (±6.9) years; rDR occurred in 7113 patients (8.00%). Known risk factors of younger age, Black ethnicity, type 2 diabetes, more severe baseline DR and diabetes duration conferred a higher risk of incident rDR. After adjusting for these known risk factors, the multivariable analysis did not show a significant association between IMD (decile 1 vs decile 10) and rDR (HR: 1.08, 95% CI: 0.87 to 1.34, p=0.511). However, high deprivation (decile 1) in three IMD subdomains was associated with rDR, namely living environment (HR: 1.64, 95% CI: 1.12 to 2.41, p=0.011), education skills (HR: 1.64, 95% CI: 1.12 to 2.41, p=0.011) and income (HR: 1.19, 95% CI: 1.02 to 1.38, p=0.024). CONCLUSION: IMD subdomains allow for the detection of associations between aspects of deprivation and rDR, which may be missed when using the aggregate IMD. The generalisation of these findings outside the UK population requires corroboration internationally.

Generalised boundary shift integral for longitudinal assessment of spinal cord atrophy.

Spinal cord atrophy measurements obtained from structural magnetic resonance imaging (MRI) are associated with disability in many neurological diseases and serve as in vivo biomarkers of neurodegeneration. Longitudinal spinal cord atrophy rate is commonly determined from the numerical difference between two volumes (based on 3D surface fitting) or two cross-sectional areas (CSA, based on 2D edge detection) obtained at different time-points. Being an indirect measure, atrophy rates are susceptible to variable segmentation errors at the edge of the spinal cord. To overcome those limitations, we developed a new registration-based pipeline that measures atrophy rates directly. We based our approach on the generalised boundary shift integral (GBSI) method, which registers 2 scans and uses a probabilistic XOR mask over the edge of the spinal cord, thereby measuring atrophy more accurately than segmentation-based techniques. Using a large cohort of longitudinal spinal cord images (610 subjects with multiple sclerosis from a multi-centre trial and 52 healthy controls), we demonstrated that GBSI is a sensitive, quantitative and objective measure of longitudinal spinal cord volume change. The GBSI pipeline is repeatable, reproducible, and provides more precise measurements of longitudinal spinal cord atrophy than segmentation-based methods in longitudinal spinal cord atrophy studies.

PET/MRI attenuation estimation in the lung: A review of past, present, and potential techniques.

Positron emission tomography/magnetic resonance imaging (PET/MRI) potentially offers several advantages over positron emission tomography/computed tomography (PET/CT), for example, no CT radiation dose and soft tissue images from MR acquired at the same time as the PET. However, obtaining accurate linear attenuation correction (LAC) factors for the lung remains difficult in PET/MRI. LACs depend on electron density and in the lung, these vary significantly both within an individual and from person to person. Current commercial practice is to use a single-valued population-based lung LAC, and better estimation is needed to improve quantification. Given the under-appreciation of lung attenuation estimation as an issue, the inaccuracy of PET quantification due to the use of single-valued lung LACs, the unique challenges of lung estimation, and the emerging status of PET/MRI scanners in lung disease, a review is timely. This paper highlights past and present methods, categorizing them into segmentation, atlas/mapping, and emission-based schemes. Potential strategies for future developments are also presented.

Cortical grey matter sodium accumulation is associated with disability and secondary progressive disease course in relapse-onset multiple sclerosis.

OBJECTIVE: Sodium (23Na)-MRI is an emerging imaging technique to investigate in vivo changes in tissue viability, reflecting neuroaxonal integrity and metabolism. Using an optimised 23Na-MRI protocol with smaller voxel sizes and improved tissue contrast, we wanted to investigate whether brain total sodium concentration (TSC) is a biomarker for long-term disease outcomes in a cohort of patients with relapse-onset multiple sclerosis (MS), followed from disease onset. METHODS: We performed a cross-sectional study in 96 patients followed up ~ 15 years after a clinically isolated syndrome (CIS) and 34 healthy controls. Disease course was classified as CIS, relapsing-remitting MS or secondary progressive MS (SPMS). We acquired 1H-MRI and 23Na-MRI and calculated the TSC in cortical grey matter (CGM), deep grey matter, normal-appearing white matter (WM) and WM lesions. Multivariable linear regression was used to identify independent associations of tissue-specific TSC with physical disability and cognition, with adjustment for tissue volumes. RESULTS: TSC in all tissues was higher in patients with MS compared with healthy controls and patients who remained CIS, with differences driven by patients with SPMS. Higher CGM TSC was independently associated with Expanded Disability Status Scale (R2=0.26), timed 25-foot walk test (R2=0.23), 9-hole peg test (R2=0.23), Paced Auditory Serial Addition Test (R2=0.29), Symbol Digit Modalities Test (R2=0.31) and executive function (R2=0.36) test scores, independent of grey matter atrophy. CONCLUSIONS: Sodium accumulation in CGM reflects underlying neuroaxonal metabolic abnormalities relevant to disease course heterogeneity and disability in relapse-onset MS. TSC and should be considered as an outcome measure in future neuroprotection trials.

Spinal cord grey matter segmentation challenge.

An important image processing step in spinal cord magnetic resonance imaging is the ability to reliably and accurately segment grey and white matter for tissue specific analysis. There are several semi- or fully-automated segmentation methods for cervical cord cross-sectional area measurement with an excellent performance close or equal to the manual segmentation. However, grey matter segmentation is still challenging due to small cross-sectional size and shape, and active research is being conducted by several groups around the world in this field. Therefore a grey matter spinal cord segmentation challenge was organised to test different capabilities of various methods using the same multi-centre and multi-vendor dataset acquired with distinct 3D gradient-echo sequences. This challenge aimed to characterize the state-of-the-art in the field as well as identifying new opportunities for future improvements. Six different spinal cord grey matter segmentation methods developed independently by various research groups across the world and their performance were compared to manual segmentation outcomes, the present gold-standard. All algorithms provided good overall results for detecting the grey matter butterfly, albeit with variable performance in certain quality-of-segmentation metrics. The data have been made publicly available and the challenge web site remains open to new submissions. No modifications were introduced to any of the presented methods as a result of this challenge for the purposes of this publication.

Erratum: Cortical folding of the preterm brain: a longitudinal analysis of extremely preterm born neonates using spectral matching.

[This corrects the article DOI: 10.1002/brb3.488.].

A multi-time-point modality-agnostic patch-based method for lesion filling in multiple sclerosis.

Multiple sclerosis lesions influence the process of image analysis, leading to tissue segmentation problems and biased morphometric estimates. Existing techniques try to reduce this bias by filling all lesions as normal-appearing white matter on T1-weighted images, considering each time-point separately. However, due to lesion segmentation errors and the presence of structures adjacent to the lesions, such as the ventricles and deep grey matter nuclei, filling all lesions with white matter-like intensities introduces errors and artefacts. In this paper, we present a novel lesion filling strategy inspired by in-painting techniques used in computer graphics applications for image completion. The proposed technique uses a five-dimensional (5D), patch-based (multi-modality and multi-time-point), Non-Local Means algorithm that fills lesions with the most plausible texture. We demonstrate that this strategy introduces less bias, fewer artefacts and spurious edges than the current, publicly available techniques. The proposed method is modality-agnostic and can be applied to multiple time-points simultaneously. In addition, it preserves anatomical structures and signal-to-noise characteristics even when the lesions are neighbouring grey matter or cerebrospinal fluid, and avoids excess of blurring or rasterisation due to the choice of the segmentation plane, shape of the lesions, and their size and/or location.

Cortical folding of the preterm brain: a longitudinal analysis of extremely preterm born neonates using spectral matching.

INTRODUCTION: Infants born extremely preterm (<28 weeks of gestation) are at risk of significant neurodevelopmental sequelae. In these infants birth coincides with a period of rapid brain growth and development, when the brain is also vulnerable to a range of insults. Mapping these changes is crucial for identifying potential biomarkers to predict early impairment. METHODS: In this study we use surface-based spectral matching techniques to find an intrasubject longitudinal surface correspondence between the white-grey matter boundary at 30 and 40 weeks equivalent gestational age in nine extremely preterm born infants. RESULTS: Using the resulting surface correspondence, we identified regions that undergo more cortical folding of the white-grey matter boundary during the preterm period by looking at changes in well-known curvature measures. We performed Hotelling T(2) statistics to evaluate the significance of our findings. DISCUSSION: The prefrontal and temporal lobes exhibit most development during the preterm period, especially in the left hemisphere. Such correspondences are a promising result as longitudinal measurements of change in cortical folding could provide insightful information about the mechanical properties of the underlying tissue and may be useful in inferring changes during growth and development in this vulnerable period.

Measuring brain atrophy with a generalized formulation of the boundary shift integral.

Brain atrophy measured using structural magnetic resonance imaging (MRI) has been widely used as an imaging biomarker for disease diagnosis and tracking of pathologic progression in neurodegenerative diseases. In this work, we present a generalized and extended formulation of the boundary shift integral (gBSI) using probabilistic segmentations to estimate anatomic changes between 2 time points. This method adaptively estimates a non-binary exclusive OR region of interest from probabilistic brain segmentations of the baseline and repeat scans to better localize and capture the brain atrophy. We evaluate the proposed method by comparing the sample size requirements for a hypothetical clinical trial of Alzheimer's disease to that needed for the current implementation of BSI as well as a fuzzy implementation of BSI. The gBSI method results in a modest but reduced sample size, providing increased sensitivity to disease changes through the use of the probabilistic exclusive OR region.

Multi-modal measurement of the myelin-to-axon diameter g-ratio in preterm-born neonates and adult controls.

Infants born prematurely are at increased risk of adverse functional outcome. The measurement of white matter tissue composition and structure can help predict functional performance and this motivates the search for new multi-modal imaging biomarkers. In this work we develop a novel combined biomarker from diffusion MRI and multi-component T2 relaxation measurements in a group of infants born very preterm and scanned between 30 and 40 weeks equivalent gestational age. We also investigate this biomarker on a group of seven adult controls, using a multi-modal joint model-fitting strategy. The proposed emergent biomarker is tentatively related to axonal energetic efficiency (in terms of axonal membrane charge storage) and conduction velocity and is thus linked to the tissue electrical properties, giving it a good theoretical justification as a predictive measurement of functional outcome.

Longitudinal measurement of the developing thalamus in the preterm brain using multi-modal MRI.

Preterm birth is a significant public health concern. For infants born very preterm (≤ 32 weeks completed gestation), there is a high instance of developmental disability. Due to the heterogeneity of patient outcomes, it is important to investigate early markers of future ability to provide effective and targeted intervention. As a neuronal relay centre, the thalamus is critical for effective cognitive function and, thus, development of white matter connections between the thalamus and cortex is vital. By non-invasively examining the state of the thalamus we can monitor development in the preterm period. To track the development we develop a novel registration technique to combine data from multiple modalities, in order to derive the transformation from a preterm scan, to a scan of the same infant at term-equivalent age. By measuring the changes in diffusion parameters over this period on a per-voxel basis, we hope to provide unique insight into neurodevelopment.

SEEG trajectory planning: combining stability, structure and scale in vessel extraction.

StereoEEG implantation is performed in patients with epilepsy to determine the site of the seizure onset zone. Intracranial haemorrhage is the most common complication associated to implantation carrying a risk that ranges from 0.6 to 2.7%, with significant associated morbidity. SEEG planning is done pre-operatively to identify avascular trajectories for the electrodes. In current practice neurosurgeons have no assistance in the planning of the electrode trajectories. There is great interest in developing computer assisted planning systems that can optimize the safety profile of electrode trajectories, maximizing the distance to critical brain structures. In this work, we address the problem of blood vessel extraction for SEEG trajectory planning. The proposed method exploits the availability of multi-modal images within a trajectory planning system to formulate a vessel extraction framework that combines the scale and the neighbouring structure of an object. We validated the proposed method in twelve multi-modal patient image sets. The mean Dice similarity coefficient (DSC) was 0.88 ± 0.03, representing a statistically significantly improvement when compared to the semi-automated single rater, single modality segmentation protocol used in current practice (DSC = 0.78 ± 0.02).

A modality-agnostic patch-based technique for lesion filling in multiple sclerosis.

Multiple Sclerosis lesions influence the process of image analysis, leading to tissue segmentation problems and biased morphometric estimates. With the aim of reducing this bias, existing techniques fill segmented lesions as normal appearing white matter. However, due to lesion segmentation errors or the presence of neighbouring structures, such as the ventricles and deep grey matter structures, filling all lesions as white matter like intensities is prone to introduce errors and artefacts. In this paper, we present a novel lesion filling strategy based on in-painting techniques for image completion. This technique makes use of a patch-based Non-Local Means algorithm that fills the lesions with the most plausible texture, rather than normal appearing white matter. We demonstrate that this strategy introduces less bias and fewer artefacts and spurious edges than previous techniques. The advantages of the proposed methodology are that it preserves both anatomical structure and signal-to-noise characteristics even when the lesions are neighbouring grey matter and cerebrospinal fluid, and avoids excess blurring or rasterisation due to the choice of segmentation plane, and lesion shape, size and/or position.

Simulating neurodegeneration through longitudinal population analysis of structural and diffusion weighted MRI data.

Neuroimaging biomarkers play a prominent role for disease diagnosis or tracking neurodegenerative processes. Multiple methods have been proposed by the community to extract robust disease specific markers from various imaging modalities. Evaluating the accuracy and robustness of developed methods is difficult due to the lack of a biologically realistic ground truth. We propose a proof-of-concept method for a patient- and disease-specific brain neurodegeneration simulator. The proposed scheme, based on longitudinal multi-modal data, has been applied to a population of normal controls and patients diagnosed with Alzheimer's disease or frontotemporal dementia. We simulated follow-up images from baseline scans and compared them to real repeat images. Additionally, simulated maps of volume change are generated, which can be compared to maps estimated from real longitudinal data. The results indicate that the proposed simulator reproduces realistic patient-specific patterns of longitudinal brain change for the given populations.

Bayesian model selection for pathological data.

The detection of abnormal intensities in brain images caused by the presence of pathologies is currently under great scrutiny. Selecting appropriate models for pathological data is of critical importance for an unbiased and biologically plausible model fit, which in itself enables a better understanding of the underlying data and biological processes. Besides, it impacts on one's ability to extract pathologically meaningful imaging biomarkers. With this aim in mind, this work proposes a fully unsupervised hierarchical model selection framework for neuroimaging data which permits the stratification of different types of abnormal image atterns without prior knowledge about the subject's pathological status.

A Bayesian approach for spatially adaptive regularisation in non-rigid registration.

This paper introduces a novel method for inferring spatially varying regularisation in non-rigid registration. This is achieved through full Bayesian inference on a probabilistic registration model, where the prior on transformations is parametrised as a weighted mixture of spatially localised components. Such an approach has the advantage of allowing the registration to be more flexibly driven by the data than a more traditional global regularisation scheme, such as bending energy. The proposed method adaptively determines the influence of the prior in a local region. The importance of the prior may be reduced in areas where the data better supports deformations, or can enforce a stronger constraint in less informative areas. Consequently, the use of such a spatially adaptive prior may reduce the unwanted impact of regularisation on the inferred deformation field. This is especially important for applications such as tensor based morphometry, where the features of interest are directly derived from the deformation field. The proposed approach is demonstrated with application to tensor based morphometry analysis of subjects with Alzheimer's disease and healthy controls. The results show that using the proposed spatially adaptive prior leads to deformation fields that have a substantially lower average complexity, but which also provide more accurate localisation of statistical group differences.

Quantitative airway analysis in longitudinal studies using groupwise registration and 4D optimal surfaces.

Quantifying local changes to the airway wall surfaces from computed tomography images is important in the study of diseases such as chronic obstructive pulmonary disease. Current approaches segment the airways in the individual time point images and subsequently aggregate per airway generation or perform branch matching to assess regional changes. In contrast, we propose an integrated approach analysing the time points simultaneously using a subject-specific groupwise space and 4D optimal surface segmentation. The method combines information from all time points and measurements are matched locally at any position on the resulting surfaces. Visual inspection of the scans of 10 subjects showed increased tree length compared to the state of the art with little change in the amount of false positives. A large scale analysis of the airways of 374 subjects including a total of 1870 images showed significant correlation with lung function and high reproducibility of the measurements.

Measurement of myelin in the preterm brain: multi-compartment diffusion imaging and multi-component T2 relaxometry.

Measurements of myelination and indicators of myelination status in the preterm brain could be predictive of later neurological outcome. Quantitative imaging of myelin could thus serve to develop predictive biomarkers; however, accurate estimation of myelin content is difficult. In this work we show that measurement of the myelin water fraction (MWF) is achievable using widely available pulse sequences and state-of-the-art algorithmic modelling of the MR imaging. We show results of myelin water fraction measurement at both 30 (4 infants) and 40 (2 infants) weeks equivalent gestational age (EGA) and show that the spatial pattern of myelin is different between these ages. Furthermore we apply a multi-component fitting routine to multi-shell diffusion weighted data to show differences in neurite density and local spatial arrangement in grey and white matter. Finally we combine these results to investigate the relationships between the diffusion and myelin measurements to show that MWF in the preterm brain may be measured alongside multi-component diffusion characteristics using clinically feasible MR sequences.

Attenuation correction synthesis for hybrid PET-MR scanners.

The combination of functional and anatomical imaging technologies such as Positron Emission Tomography (PET) and Computed Tomography (CT) has shown its value in the preclinical and clinical fields. In PET/CT hybrid acquisition systems, CT-derived attenuation maps enable a more accurate PET reconstruction. However, CT provides only very limited soft-tissue contrast and exposes the patient to an additional radiation dose. In comparison, Magnetic Resonance Imaging (MRI) provides good soft-tissue contrast and the ability to study functional activation and tissue microstructures, but does not directly provide patient-specific electron density maps for PET reconstruction. The aim of the proposed work is to improve PET/MR reconstruction by generating synthetic CTs and attenuation-maps. The synthetic images are generated through a multi-atlas information propagation scheme, locally matching the MRI-derived patient's morphology to a database of pre-acquired MRI/CT pairs. Results show improvements in CT synthesis and PET reconstruction accuracy when compared to a segmentation method using an Ultrashort-Echo-Time MRI sequence.

Improved maximum a posteriori cortical segmentation by iterative relaxation of priors.

Thickness measurements of the cerebral cortex can aid diagnosis and provide valuable information about the temporal evolution of several diseases such as Alzheimer's, Huntington's, Schizophrenia, as well as normal ageing. The presence of deep sulci and 'collapsed gyri' (caused by the loss of tissue in patients with neurodegenerative diseases) complicates the tissue segmentation due to partial volume (PV) effects and limited resolution of MRI. We extend existing work to improve the segmentation and thickness estimation in a single framework. We model the PV effect using a maximum a posteriori approach with novel iterative modification of the prior information to enhance deep sulci and gyri delineation. We use a voxel based approach to estimate thickness using the Laplace equation within a Lagrangian-Eulerian framework leading to sub-voxel accuracy. Experiments performed on a new digital phantom and on clinical Alzheimer's disease MR images show improvements in both accuracy and robustness of the thickness measurements, as well as a reduction of errors in deep sulci and collapsed gyri.