Ecosystem complexity on the Kerguelen Axis: the need for integrated ecosystem studies and sustained coordinated monitoring

第6回極域科学シンポジウム[OB] 極域生物圏11月16日（月）　統計数理研究所 セミナー室1（D305

Exploring Southern Ocean ecosystem change through the use of a statistical sea ice emulator

第3回極域科学シンポジウム/第34回極域生物シンポジウム 11月26日（月） 統計数理研究所 ３階セミナー

Optimisation of arterial spin labelling using bayesian experimental design

Large-scale neuroimaging studies often use multiple individual imaging contrasts. Due to the finite time available for imaging,there is intense competition for the time allocated to the individual modalities; thus it is crucial to maximise the utility of each method given the resources available. Arterial Spin Labelled (ASL) MRI often forms part of such studies. Measuring perfusion of oxygenated blood in the brain is valuable for several diseases,but quantification using multiple inversion time ASL is time-consuming due to poor SNR and consequently slow acquisitions. Here,we apply Bayesian principles of experimental design to clinical-length ASL acquisitions,resulting in significant improvements to perfusion estimation. Using simulations and experimental data,we validate this approach for a five-minute ASL scan. Our design procedure can be constrained to any chosen scan duration,making it well-suited to improve a variety of ASL implementations. The potential for adaptation to other modalities makes this an attractive method for optimising acquisition in the time-pressured environment of neuroimaging studies

Flow around a cube in a turbulent boundary layer: LES and experiment

We present a numerical simulation of flow around a surface mounted cube placed in a turbulent boundary layer which, although representing a typical wind environment, has been specifically tailored to match a series of wind tunnel observations. The simulations were carried out at a Reynolds number, based on the velocity U at the cube height h, of 20,000—large enough that many aspects of the flow are effectively Reynolds number independent. The turbulence intensity was about 18% at the cube height, and the integral length scale was about 0.8 times the cube height h. The Jenson number Je=h/z0, based on the approach flow roughness length z0, was 600, to match the wind tunnel situation. The computational mesh was uniform with a spacing of h/32, aiding rapid convergence of the multigrid solver, and the governing equations were discretised using second-order finite differences within a parallel multiblock environment. The results presented include detailed comparison between measurements and LES computations of both the inflow boundary layer and the flow field around the cube including mean and fluctuating surface pressures. It is concluded that provided properly formulated inflow and surface boundary conditions are used, LES is now a viable tool for use in wind engineering problems concerning flow over isolated bodies. In particular, both mean and fluctuating surface pressures can be obtained with a similar degree of uncertainty as usually associated with wind tunnel modelling

Deep convolutional filtering for spatio-temporal denoising and artifact removal in arterial spin labelling MRI

Arterial spin labelling (ASL) is a noninvasive imaging modality, used in the clinic and in research, which can give quantitative measurements of perfusion in the brain and other organs. However, because the signal-to-noise ratio is inherently low and the ASL acquisition is particularly prone to corruption by artifact, image processing methods such as denoising and artifact filtering are vital for generating accurate measurements of perfusion. In this work, we present a new simultaneous approach to denoising and artifact removal, using a novel deep convolutional joint filter architecture to learn and exploit spatio-temporal properties of the ASL signal. We proceed to show, using data from 15 healthy subjects, that our approach achieves state of the art performance in both denoising and artifact removal, improving peak signal-to-noise ratio by up to 50%. By allowing more accurate estimation of perfusion, even in challenging datasets, this technique offers an exciting new approach for ASL pipelines, and might be used both for improving individual images and to increase the power of research studies using ASL