Heavy quark medium polarization at next-to-leading order

We compute the imaginary part of the heavy quark contribution to the photon polarization tensor, i.e. the quarkonium spectral function in the vector channel, at next-to-leading order in thermal QCD. Matching our result, which is valid sufficiently far away from the two-quark threshold, with a previously determined resummed expression, which is valid close to the threshold, we obtain a phenomenological estimate for the spectral function valid for all non-zero energies. In particular, the new expression allows to fix the overall normalization of the previous resummed one. Our result may be helpful for lattice reconstructions of the spectral function (near the continuum limit), which necessitate its high energy behaviour as input, and can in principle also be compared with the dilepton production rate measured in heavy ion collision experiments. In an appendix analogous results are given for the scalar channel.Comment: 43 pages. v2: a figure and other clarifications added, published versio

Bayesian Transductive Markov Random Fields for Interactive Segmentation in Retinal Disorders

In the realm of computer aided diagnosis (CAD) interactive segmentation schemes have been well received by physicians, where the combination of human and machine intelligence can provide improved segmentation efficacy with minimal expert intervention [1-3]. Transductive learning (TL) or semi-supervised learning (SSL) is a suitable framework for learning-based interactive segmentation given the scarce label problem. In this paper we present extended work on Bayesian transduction and regularized conditional mixtures for interactive segmentation [3]. We present a Markov random field model integrating a semi-parametric conditional mixture model within a Bayesian transductive learning and inference setting. The model allows efficient learning and inference in a semi-supervised setting given only minimal approximate label information. Preliminary experimental results on multimodal images of retinal disorders such as drusen, geographic atrophy (GA), and choroidal neovascularisation (CNV) with exudates and subretinal fibrosis show promising segmentation performance

Learning Non-Homogenous Textures and the Unlearning Problem with Application to Drusen Detection in Retinal Images

In this work we present a novel approach for learning non- homogenous textures without facing the unlearning problem. Our learning method mimics the human behavior of selective learning in the sense of fast memory renewal. We perform probabilistic boosting and structural similarity clustering for fast selective learning in a large knowledge domain acquired over different time steps. Applied to non- homogenous texture discrimination, our learning method is the first approach that deals with the unlearning problem applied to the task of drusen segmentation in retinal imagery, which itself is a challenging problem due to high variability of non-homogenous texture appearance. We present preliminary results

A non-perturbative contribution to jet quenching

It has been argued by Caron-Huot that infrared contributions to the jet quenching parameter in hot QCD, denoted by qhat, can be extracted from an analysis of a certain static-potential related observable within the dimensionally reduced effective field theory. Following this philosophy, the order of magnitude of a non-perturbative contribution to qhat from the colour-magnetic scale, g^2T/pi, is estimated. The result is small; it is probably below the parametrically perturbative but in practice slowly convergent contributions from the colour-electric scale, whose all-orders resummation therefore remains an important challenge.Comment: 4 pages. v2: clarifications, published versio

Improving PET-Based Physiological Quantification Through Methods of Wavelet Denoising

The goal of this study was to evaluate methods of multidimensional wavelet denoising on restoring the fidelity of biological signals hidden within dynamic positron emission tomography (PET) images. A reduction of noise within pixels, between adjacent regions, and time-serial frames was achieved via redundant multiscale representations. In analyzing dynamic PET data of healthy volunteers, a multiscale method improved the estimate-to-error ratio of flows fivefold without loss of detail. This technique also maintained accuracy of flow estimates in comparison with the "gold standard," using dynamic PET with O15-water. In addition, in studies of coronary disease patients, flow patterns were preserved and infarcted regions were well differentiated from normal regions. The results show that a wavelet-based noise-suppression method produced reliable approximations of salient underlying signals and led to an accurate quantification of myocardial perfusion. The described protocol can be generalized to other temporal biomedical imaging modalities including functional magnetic resonance imaging and ultrasound

Retinal vessel segmentation using multi-scale wavelet frame analysis

Fundus imaging is a non-invasive technique for in vivo ophthalmoscopic inspection of retinal disorders. Quantitative information about the vascular network can facilitate clinical diagnosis of retinal diseases [1]. Goal: Segmentation of the vascular network in fundus images for further quantification and post processing as a binary classification into object and background. Approach: We perform an over-complete multi-scale wavelet frame expansion with selective channel rejection in the decomposition tree. Remaining channels undergo wavelet shrinkage and enhancement to separate retinal objects from background. Results: Comparison to expert gradings on a pixel by pixel basis show mean sensitivity and specificity of 0.8 and 0.9

Use of Wavelet Transforms in Analysis of Time–Activity Data from Cardiac PET

Because of its intrinsic quantitative properties, PET permits measurement of myocardial perfusion and metabolism in absolute terms (i.e., mL/g/min). However, quantification has been limited by errors produced in image acquisition, selection of regions of interest, and data analysis. The goal of this study was to evaluate a newly developed, novel, wavelet-based noise-reduction approach that can objectively extract biologic signals hidden within dynamic PET data. Methods: Quantification of myocardial perfusion using dynamic PET imaging with 82Rb, H215O, and 13NH3 was selected to evaluate the effects of the wavelet-based noise-reduction protocol. Dynamic PET data were fitted to appropriate mathematic models before and after wavelet-based noise reduction to get flow estimates. Time–activity curves, precision, accuracy, and differentiating capacity derived from the wavelet protocol were compared with those obtained from unmodified data processing. A total of 84 human studies was analyzed, including 43 at rest (18 82Rb scans, 18 H215O scans, and 7 13NH3 scans) and 41 after coronary hyperemia with dipyridamole (17 82Rb scans, 17 H215O scans, and 7 13NH3 scans). Results: For every tracer tested under all conditions, the wavelet method improved the shape of blood and tissue time–activity curves, increased estimate-to-error ratios, and maintained fidelity of flow in regions as small as 0.85 cm3. It also improved the accuracy of flow estimates derived from 82Rb to the level of that achieved with H215O, which was not affected markedly by the wavelet process. In studies of patients with coronary disease, regional heterogeneity of myocardial perfusion was preserved and flow estimates in infarcted regions were differentiated more easily from normal regions. Conclusion: The wavelet-based noise-reduction method effectively and objectively extracted tracer time–activity curves from data with low signal-to-noise ratios and improved the accuracy and precision of measurements with all tracer techniques studied. The approach should be generalizable to other image modalities such as functional MRI and CT and, therefore, improve the ability to quantify dynamic physiologic processes

Interactive Image Analysis in Age-related Macular Degeneration (AMD) and Stargardt Disease (STGD)

The literature of the last three decades is replete with automatic methods for retinal image analysis. Acceptance has been limited due to post-processing or tuning requirements that may be just as time consuming as the original manual methods. The point of view herein is that by taking advantage of the human visual system and expert knowledge from the outset, the promised efficiencies of digital methods can be achieved in practice as well as in theory. Thus, simple labeling of regions of interest that is accepted and easily performed in a few moments by the human can provide enormous advantage to an already well-developed algorithm. Three examples are provided: drusen segmentation, image registration, and geographic atrophy segmentation, with applications to disease understanding

Quantification of Myocardial Perfusion in Human Subjects Using 82Rb and Wavelet-Based Noise Reduction

Quantification of myocardial perfusion with 82Rb has been difficult to achieve because of the low signal-to-noise ratio of the dynamic data curves. This study evaluated the accuracy of flow estimates after the application of a novel multidimensional wavelet-based noise-reduction protocol. Methods: Myocardial perfusion was estimated using 82Rb and a two-compartment model from dynamic PET scans on 11 healthy volunteers at rest and after hyperemic stress with dipyridamole. Midventricular planes were divided into eight regions of interest, and a wavelet transform protocol was applied to images and time–activity curves. Flow estimates without and with the wavelet approach were compared with those obtained using H215O. Results: Over a wide flow range (0.45–2.75 mL/g/min), flow achieved with the wavelet approach correlated extremely closely with values obtained with H215O (y = 1.03 x -0.12; n = 23 studies, r = 0.94, P < 0.001). If the wavelet noise-reduction technique was not used, the correlation was less strong (y = 1.11 x + 0.24; n = 23 studies, r = 0.79, P < 0.001). In addition, the wavelet approach reduced the regional variation from 75% to 12% and from 62% to 11% (P < 0.001 for each comparison) for resting and stress studies, respectively. Conclusion: The use of a wavelet protocol allows near-optimal noise reduction, markedly enhances the physiologic flow signal within the PET images, and enables accurate measurement of myocardial perfusion with 82Rb in human subjects over a wide range of flows