A Functional Approach to Deconvolve Dynamic Neuroimaging Data

Jiang, Ci-Ren; Aston, John A. D.; Wang, Jane-ling

doi:10.1080/01621459.2015.1060241

Cited by 11 publications

(9 citation statements)

References 35 publications

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“…To date there have been two similar approaches in basketball [13] and in football [14]. In these, the probabilities of success are also described continuously by means of a so-called expected position value (EPV) or an expected goal value (EGV).…”

Section: Introductionmentioning

confidence: 99%

Real Time Quantification of Dangerousity in Football Using Spatiotemporal Tracking Data

2016

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This study describes an approach to quantification of attacking performance in football. Our procedure determines a quantitative representation of the probability of a goal being scored for every point in time at which a player is in possession of the ball–we refer to this as dangerousity. The calculation is based on the spatial constellation of the player and the ball, and comprises four components: (1) Zone describes the danger of a goal being scored from the position of the player on the ball, (2) Control stands for the extent to which the player can implement his tactical intention on the basis of the ball dynamics, (3) Pressure represents the possibility that the defending team prevent the player from completing an action with the ball and (4) Density is the chance of being able to defend the ball after the action. Other metrics can be derived from dangerousity by means of which questions relating to analysis of the play can be answered. Action Value represents the extent to which the player can make a situation more dangerous through his possession of the ball. Performance quantifies the number and quality of the attacks by a team over a period of time, while Dominance describes the difference in performance between teams. The evaluation uses the correlation between probability of winning the match (derived from betting odds) and performance indicators, and indicates that among Goal difference (r = .55), difference in Shots on Goal (r = .58), difference in Passing Accuracy (r = .56), Tackling Rate (r = .24) Ball Possession (r = .71) and Dominance (r = .82), the latter makes the largest contribution to explaining the skill of teams. We use these metrics to analyse individual actions in a match, to describe passages of play, and to characterise the performance and efficiency of teams over the season. For future studies, they provide a criterion that does not depend on chance or results to investigate the influence of central events in a match, various playing systems or tactical group concepts on success.

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Section: Introductionmentioning

confidence: 99%

Real Time Quantification of Dangerousity in Football Using Spatiotemporal Tracking Data

2016

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“…SVD can however be sensitive to potential delay and dispersion of the injected bolus [ 38 , 39 ]. More robust approaches to nonparametric deconvolution [ 39 , 40 ] or functional principal components analysis [ 41 ] may further improve HYDECA performance. Here we provide a framework for HYDECA and comparison between different implementations of the algorithm is beyond our scope.…”

Section: Discussionmentioning

confidence: 99%

A hybrid deconvolution approach for estimation of in vivo non-displaceable binding for brain PET targets without a reference region

2017

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Background and aimEstimation of a PET tracer’s non-displaceable distribution volume (VND) is required for quantification of specific binding to its target of interest. VND is generally assumed to be comparable brain-wide and is determined either from a reference region devoid of the target, often not available for many tracers and targets, or by imaging each subject before and after blocking the target with another molecule that has high affinity for the target, which is cumbersome and involves additional radiation exposure. Here we propose, and validate for the tracers [11C]DASB and [11C]CUMI-101, a new data-driven hybrid deconvolution approach (HYDECA) that determines VND at the individual level without requiring either a reference region or a blocking study.MethodsHYDECA requires the tracer metabolite-corrected concentration curve in blood plasma and uses a singular value decomposition to estimate the impulse response function across several brain regions from measured time activity curves. HYDECA decomposes each region’s impulse response function into the sum of a parametric non-displaceable component, which is a function of VND, assumed common across regions, and a nonparametric specific component. These two components differentially contribute to each impulse response function. Different regions show different contributions of the two components, and HYDECA examines data across regions to find a suitable common VND. HYDECA implementation requires determination of two tuning parameters, and we propose two strategies for objectively selecting these parameters for a given tracer: using data from blocking studies, and realistic simulations of the tracer. Using available test-retest data, we compare HYDECA estimates of VND and binding potentials to those obtained based on VND estimated using a purported reference region.ResultsFor [11C]DASB and [11C]CUMI-101, we find that regardless of the strategy used to optimize the tuning parameters, HYDECA provides considerably less biased estimates of VND than those obtained, as is commonly done, using a non-ideal reference region. HYDECA test-retest reproducibility is comparable to that obtained using a VND determined from a non-ideal reference region, when considering the binding potentials BPP and BPND.ConclusionsHYDECA can provide subject-specific estimates of VND without requiring a blocking study for tracers and targets for which a valid reference region does not exist.

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“…When the IRF is computed nonparametrically, however, the IRF is estimated only up to the end time of the PET scan. A different definition is therefore necessary, as proposed by Jiang et al, 4 in which a surrogate 'volume of distribution' for the tracer is calculated by integrating the IRF only up to the end time T end of the scan:…”

Section: Outcome Measuresmentioning

confidence: 99%

Model-Free Quantification of Dynamic PET Data Using Nonparametric Deconvolution

Zanderigo

Parsey

Ogden

2015

J Cereb Blood Flow Metab

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Dynamic positron emission tomography (PET) data are usually quantified using compartment models (CMs) or derived graphical approaches. Often, however, CMs either do not properly describe the tracer kinetics, or are not identifiable, leading to nonphysiologic estimates of the tracer binding. The PET data are modeled as the convolution of the metabolite-corrected input function and the tracer impulse response function (IRF) in the tissue. Using nonparametric deconvolution methods, it is possible to obtain model-free estimates of the IRF, from which functionals related to tracer volume of distribution and binding may be computed, but this approach has rarely been applied in PET. Here, we apply nonparametric deconvolution using singular value decomposition to simulated and test-retest clinical PET data with four reversible tracers well characterized by CMs (

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A Functional Approach to Deconvolve Dynamic Neuroimaging Data

Cited by 11 publications

References 35 publications

Real Time Quantification of Dangerousity in Football Using Spatiotemporal Tracking Data

Real Time Quantification of Dangerousity in Football Using Spatiotemporal Tracking Data

A hybrid deconvolution approach for estimation of in vivo non-displaceable binding for brain PET targets without a reference region

Model-Free Quantification of Dynamic PET Data Using Nonparametric Deconvolution

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