The Human Connectome Project Lifespan studies cover the Development (5-21) and Aging (36-100+) phases of life. Arterial spin labelling (ASL) was included in the imaging protocol, resulting in one of the largest datasets collected to-date of high spatial resolution multiple delay ASL covering 3,000 subjects. The HCP-ASL minimal processing pipeline was developed specifically for this dataset to pre-process the image data and produce perfusion estimates in both volumetric and surface template space, though quality control is not performed. Applied to the whole dataset, the outputs of the pipeline revealed significant and expected differences in perfusion between the Development and Ageing cohorts. Visual inspection of the group average surface maps showed that cortical perfusion often followed cortical areal boundaries, suggesting differential regulation of cerebral perfusion within brain areas at rest. Group average maps of arterial transit time also showed differential transit times in core and watershed areas of the cerebral cortex, which are useful for interpreting haemodynamics of functional MRI images. The pre-processed dataset will provide a valuable resource for understanding haemodynamics across the human lifespan.

Multiple-timepoint arterial spin labelling MRI is a non-invasive imaging technique that permits measurement of both cerebral blood flow and arterial transit time, the latter of which is an emerging biomarker of interest for cerebrovascular health. Quantification of arterial spin labelling data is challenging due to the low signal to noise ratio and non-linear tracer kinetics of this technique. In this work, we introduce a new quantification method called SSVB that addresses limitations in existing methods and demonstrate its performance using simulations and acquisition data. Simulations showed that the method is more accurate, particularly for estimating arterial transit time, and more robust to noise than existing techniques. On high spatial resolution data acquired at 3 T, the method produced less noisy parameter maps than the comparator method and captured greater variation in arterial transit time on a cross-sectional cohort.

AbstractData normalisation is an important approach to reduce inter-subject variability in group studies, but care must be taken when choosing a normalisation strategy to not introduce further confounds or artefacts into the data. Normalisation of arterial spin labelling perfusion measurements remains challenging, especially in the context of Alzheimer’s disease, where there may be global hypoperfusion present. We propose that using the thalamus as a reference region for normalisation could improve the detectability of hypoperfusion in Alzheimer’s disease and alleviate the pseudo-hyperperfusion artefacts caused by the commonly-used strategy of normalisation using global mean perfusion. Evaluation on an Alzheimer’s disease dataset found this strategy was able to reduce coefficient of variation in perfusion measurements by around 60% and yield increases in statistical power of comparisons against healthy controls.

The arterial spin labelling (ASL) data from the Human Connectome Project (HCP) Lifespan studies can provide a source of unusually high-resolution hemodynamic measures from >2500 individuals with ages 5-21 and 37-100+. This work presents a summary of the minimal ASL processing pipeline used to provide pre-processed calibrated perfusion and arterial arrival time measurements for the cortical surface and subcortical volumes, from these individuals. The pipeline accounts for slice-wise image intensity variations resulting from the simultaneous multi-slice acquisition used to achieve high spatial resolution. These measures and this pipeline will be made available to the global neuroscience and neuroimaging communities.

Arterial Spin Labelling (ASL) MRI is now an established non-invasive method to quantify cerebral blood flow and is increasingly being used in a variety of neuroimaging applications. With standard ASL acquisition protocols widely available, there is a growing interest in advanced options that offer added quantitative precision and information about haemodynamics beyond perfusion. In this article, we introduce the BASIL toolbox, a research tool for the analysis of ASL data included within the FMRIB Software Library (FSL), and explain its operation in a variety of typical use cases. BASIL is not offered as a clinical tool, and nor is this work intended to guide the clinical application of ASL. Built around a Bayesian model-based inference algorithm, the toolbox is designed to quantify perfusion and other haemodynamic measures, such as arterial transit times, from a variety of possible ASL input data, particularly exploiting the information available in more advanced multi-delay acquisitions. At its simplest, the BASIL toolbox offers a graphical user interface that provides the analysis options needed by most users; through command line tools, it offers more bespoke options for users needing customised analyses. As part of FSL, the toolbox exploits a range of complementary neuroimaging analysis tools so that ASL data can be easily integrated into neuroimaging studies and used alongside other modalities.

Surface-based analysis methods for functional imaging data have been shown to offer substantial benefits for the study of the human cortex, namely in the localisation of functional areas and the establishment of inter-subject correspondence. A new approach for surface-based parameter estimation via non-linear model fitting on functional timeseries data is presented. It treats the different anatomies within the brain in the manner that is most appropriate: surface-based for the cortex, volumetric for white matter, and using regions-of-interest for subcortical grey matter structures. The mapping between these different domains is incorporated using a novel algorithm that accounts for partial volume effects. A variational Bayesian framework is used to perform parameter inference in all anatomies simultaneously rather than separately. This approach, called hybrid inference, has been implemented using stochastic optimisation techniques. A comparison against a conventional volumetric workflow with post-projection on simulated perfusion data reveals improvements parameter recovery, preservation of spatial detail and consistency between spatial resolutions. At 4 mm isotropic resolution, the following improvements were obtained: 2.7% in SSD error of perfusion, 16% in SSD error of Z-score perfusion, and 27% in Bhattacharyya distance of perfusion distribution.

During the United Kingdom’s Covid-19 vaccination campaign, general practitioners (GPs) have held responsibility for vaccinating housebound patients. This presented them with a large, complex and unfamiliar logistical challenge, namely determining the most time-efficient route to visit multiple patients at their home address. In response to a lack of existing solutions tailored specifically to vaccination, and in light of overwhelming demand, Vaximap ( https://www.vaximap.org ) was created in January 2021 to automate the process of route planning. It is free of charge for all users and has been used to-date to plan vaccinations for over 470,000 patients. This article analyses usage data to estimate the time savings (3 work years) and financial savings (£110,000) the service has yielded for GP surgeries, thus demonstrating that it helped to accelerate the UK’s Covid-19 vaccination campaign at critical moments.

Projection of volumetric data onto the cortical surface is an important precursor to performing surface-based analysis. Numerous projection methods have been reported in the literature, many of which make assumptions which tie them to use with specific modalities, notably blood oxygenation level dependent (BOLD) imaging. This means that they may not be appropriate for use with modalities where subcortical tissue contributes a signal of interest. This work details a new projection that provides a number of generalisations and extensions to existing methods. Namely, it may be used to project arbitrary data without making modality-specific assumptions and can produce unified surface and volumetric representations of data (a concept also known as grayordinates space). When constrained to the same assumptions as existing methods, a comparison using simulation data shows that it produces similar outputs. When these assumptions are relaxed to project simulation data containing both cortical and subcortical signals to and from a unified surface and volume space, substantial and statistically significant differences in recovery of ground truth are observed compared to existing methods.

BACKGROUND:The manufacturing of any standard mechanical ventilator cannot rapidly be upscaled to several thousand units per week, largely due to supply chain limitations. The aim of this study was to design, verify and perform a pre-clinical evaluation of a mechanical ventilator based on components not required for standard ventilators, and that met the specifications provided by the Medicines and Healthcare Products Regulatory Agency (MHRA) for rapidly-manufactured ventilator systems (RMVS). METHODS:The design utilises closed-loop negative feedback control, with real-time monitoring and alarms. Using a standard test lung, we determined the difference between delivered and target tidal volume (VT) at respiratory rates between 20 and 29 breaths per minute, and the ventilator's ability to deliver consistent VT during continuous operation for >14 days (RMVS specification). Additionally, four anaesthetised domestic pigs (3 male-1 female) were studied before and after lung injury to provide evidence of the ventilator's functionality, and ability to support spontaneous breathing. FINDINGS:Continuous operation lasted 23 days, when the greatest difference between delivered and target VT was 10% at inspiratory flow rates >825 mL/s. In the pre-clinical evaluation, the VT difference was -1 (-90 to 88) mL [mean (LoA)], and positive end-expiratory pressure (PEEP) difference was -2 (-8 to 4) cmH2O. VT delivery being triggered by pressures below PEEP demonstrated spontaneous ventilation support. INTERPRETATION:The mechanical ventilator presented meets the MHRA therapy standards for RMVS and, being based on largely available components, can be manufactured at scale. FUNDING:Work supported by Wellcome/EPSRC Centre for Medical Engineering,King's Together Fund and Oxford University.

Arterial spin labeling (ASL) is a non-invasive MRI technique that allows for quantitative measurement of cerebral perfusion. Incomplete or inaccurate reporting of acquisition parameters complicates quantification, analysis, and sharing of ASL data, particularly for studies across multiple sites, platforms, and ASL methods. There is a strong need for standardization of ASL data storage, including acquisition metadata. Recently, ASL-BIDS, the BIDS extension for ASL, was developed and released in BIDS 1.5.0. This manuscript provides an overview of the development and design choices of this first ASL-BIDS extension, which is mainly aimed at clinical ASL applications. Discussed are the structure of the ASL data, focussing on storage order of the ASL time series and implementation of calibration approaches, unit scaling, ASL-related BIDS fields, and storage of the labeling plane information. Additionally, an overview of ASL-BIDS compatible conversion and ASL analysis software and ASL example datasets in BIDS format is provided. We anticipate that large-scale adoption of ASL-BIDS will improve the reproducibility of ASL research.

Background. Throughout the UK's Covid-19 vaccination campaign, responsibility for vaccinating housebound patients has rested with individual GP surgeries, posing them a difficult logistical challenge (the travelling salesman problem). In response to demand from GPs, and a lack of existing solutions tailored specifically to vaccination, VaxiMap was created. This tool provides optimal routes for vaccine delivery and has been free to all users since its inception in January 2021. Methods. VaxiMap generates optimal routes subject to the constraint that the number of patients per route should be fixed. This ensures that a known quantity of vaccine can be set aside for each route and minimises wastage. The user need only upload an Excel spreadsheet of patient postcodes to be visited. A divide-and-conquer approach of iterative k-means clustering followed by within-cluster route optimisation is used to generate the routes. Findings. We find substantial savings in the time taken to plan vaccinations, as well as savings in the time taken to visit housebound patients. We estimate total savings to date of 4,700 hours of practitioner time, equivalent to 2.5 work-years, or approximately GBP 91k at typical practitioner salaries. Interpretation. The adoption of VaxiMap yielded both time and cost savings for GP surgeries and accelerated the UK's Covid-19 vaccination campaign at a critical moment. Funding. Financial support was provided by Magdalen College, Oxford, Oxford University Innovation, and JHubMed, part of UK Strategic Command. These parties were not involved in the preparation of this manuscript.

Partial volume effects (PVE) present a source of confound for the analysis of functional imaging data. Correction for PVE requires estimates of the partial volumes (PVs) present in an image. These estimates are conventionally obtained via volumetric segmentation, but such an approach may not be accurate for complex structures such as the cortex. An alternative is to use surface-based segmentation, which is well-established within the literature. Toblerone is a new method for estimating PVs using such surfaces. It uses a purely geometric approach that considers the intersection between a surface and the voxels of an image. In contrast to existing surface-based techniques, Toblerone is not restricted to use with any particular structure or modality. Evaluation in a neuroimaging context has been performed on simulated surfaces, simulated T1-weighted MRI images and finally a Human Connectome Project test-retest dataset. A comparison has been made to two existing surface-based methods; in all analyses Toblerone's performance either matched or surpassed the comparator methods. Evaluation results also show that compared to an existing volumetric method (FSL FAST), a surface-based approach with Toblerone offers improved robustness to scanner noise and field non-uniformity, and better inter-session repeatability in brain volume. In contrast to volumetric methods, a surface-based approach negates the need to perform resampling which is advantageous at the resolutions typically used for neuroimaging.

Motivation: Multi-delay Arterial Spin Labelling has application across multiple patient groups, but accurate quantification remains difficult, particularly for prolonged transit times and noisy data. Goal(s): Compare least-squares and Bayesian-inference model fitting for perfusion estimate accuracy. Approach: Least-squares and Bayesian-inference, specifically BASIL, pipelines were run on simulated and in-vivo ASL data with different SNR with three choices of prior/initial value for arterial transit time (ATT). The resulting cerebral blood flow (CBF) and ATT maps were compared. Results: ATT quantification is impacted by ATT prior/initial value in Bayesian-inference fitting more than least-squares fitting. Least-squares fitting is more susceptible to CBF overestimation at lower SNR. Impact: MD-ASL analysis method can impact ATT accuracy. Bayesian-inference fitting is better for lower SNR data when CBF is the primary interest. Least-squares fitting is better for higher SNR data, when prior/estimate is not well known, and for accurate ATT estimation.