Blind Deconvolution via Lower-Bounded Logarithmic Image Priors

In this work we devise two novel algorithms for blind deconvolution based on a family of logarithmic image priors. In contrast to recent approaches, we consider a minimalistic formulation of the blind deconvolution problem where there are only two energy terms: a least-squares term for the data fidelity and an image prior based on a lower-bounded logarithm of the norm of the image gradients. We show that this energy formulation is sufficient to achieve the state of the art in blind deconvolution with a good margin over previous methods. Much of the performance is due to the chosen prior. On the one hand, this prior is very effective in favoring sparsity of the image gradients. On the other hand, this prior is non convex. Therefore, solutions that can deal effectively with local minima of the energy become necessary. We devise two iterative minimization algorithms that at each iteration solve convex problems: one obtained via the primal-dual approach and one via majorization-minimization. While the former is computationally efficient, the latter achieves state-of-the-art performance on a public dataset.

Taking personalized medicine seriously: Biomarker approaches in phase IIb/III studies in major depression and schizophrenia

The success rate in the development of psychopharmacological compounds is insufficient. Two main reasons for failure have been frequently identified: 1) treating the wrong patients and 2) using the wrong dose. This is potentially based on the known heterogeneity among patients, both on a syndromal and a biological level. A focus on personalized medicine through better characterization with biomarkers has been successful in other therapeutic areas. Nevertheless, obstacles toward this goal that exist are 1) the perception of a lack of validation, 2) the perception of an expensive and complicated enterprise, and 3) the perception of regulatory hurdles. The authors tackle these concerns and focus on the utilization of biomarkers as predictive markers for treatment outcome. The authors primarily cover examples from the areas of major depression and schizophrenia. Methodologies covered include salivary and plasma collection of neuroendocrine, metabolic, and inflammatory markers, which identified subgroups of patients in the Netherlands Study of Depression and Anxiety. A battery of vegetative markers, including sleep-electroencephalography parameters, heart rate variability, and bedside functional tests, can be utilized to characterize the activity of a functional system that is related to treatment refractoriness in depression (e.g., the renin-angiotensin-aldosterone system). Actigraphy and skin conductance can be utilized to classify patients with schizophrenia and provide objective readouts for vegetative activation as a functional marker of target engagement. Genetic markers, related to folate metabolism, or folate itself, has prognostic value for the treatment response in patients with schizophrenia. Already, several biomarkers are routinely collected in standard clinical trials (e.g., blood pressure and plasma electrolytes), and appear to be differentiating factors for treatment outcome. Given the availability of a wide variety of markers, the further development and integration of such markers into clinical research is both required and feasible in order to meet the benefit of personalized medicine. This article is based on proceedings from the "Taking Personalized Medicine Seriously-Biomarker Approaches in Phase IIb/III Studies in Major Depression and Schizophrenia" session, which was held during the 10th Annual Scientific Meeting of the International Society for Clinical Trials Meeting (ISCTM) in Washington, DC, February 18 to 20, 2014.

A Parallel Solver for the Time-Periodic Navier–Stokes Equations

We investigate parallel algorithms for the solution of the Navier–Stokes equations in space-time. For periodic solutions, the discretized problem can be written as a large non-linear system of equations. This system of equations is solved by a Newton iteration. The Newton correction is computed using a preconditioned GMRES solver. The parallel performance of the algorithm is illustrated.

Non-rigid Free-Form 2D-3D Registration Using Statistical Deformation Model

This paper presents a non-rigid free-from 2D-3D registration approach using statistical deformation model (SDM). In our approach the SDM is first constructed from a set of training data using a non-rigid registration algorithm based on b-spline free-form deformation to encode a priori information about the underlying anatomy. A novel intensity-based non-rigid 2D-3D registration algorithm is then presented to iteratively fit the 3D b-spline-based SDM to the 2D X-ray images of an unseen subject, which requires a computationally expensive inversion of the instantiated deformation in each iteration. In this paper, we propose to solve this challenge with a fast B-spline pseudo-inversion algorithm that is implemented on graphics processing unit (GPU). Experiments conducted on C-arm and X-ray images of cadaveric femurs demonstrate the efficacy of the present approach.

Impact of Hybrid Iterative Reconstruction on Agatston Coronary Artery Calcium Scores in Comparison to Filtered Back Projection in Native Cardiac CT

PURPOSE To investigate whether the effects of hybrid iterative reconstruction (HIR) on coronary artery calcium (CAC) measurements using the Agatston score lead to changes in assignment of patients to cardiovascular risk groups compared to filtered back projection (FBP). MATERIALS AND METHODS 68 patients (mean age 61.5 years; 48 male; 20 female) underwent prospectively ECG-gated, non-enhanced, cardiac 256-MSCT for coronary calcium scoring. Scanning parameters were as follows: Tube voltage, 120 kV; Mean tube current time-product 63.67 mAs (50 - 150 mAs); collimation, 2 × 128 × 0.625 mm. Images were reconstructed with FBP and with HIR at all levels (L1 to L7). Two independent readers measured Agatston scores of all reconstructions and assigned patients to cardiovascular risk groups. Scores of HIR and FBP reconstructions were correlated (Spearman). Interobserver agreement and variability was assessed with ĸ-statistics and Bland-Altmann-Plots. RESULTS Agatston scores of HIR reconstructions were closely correlated with FBP reconstructions (L1, R = 0.9996; L2, R = 0.9995; L3, R = 0.9991; L4, R = 0.986; L5, R = 0.9986; L6, R = 0.9987; and L7, R = 0.9986). In comparison to FBP, HIR led to reduced Agatston scores between 97 % (L1) and 87.4 % (L7) of the FBP values. Using HIR iterations L1 - L3, all patients were assigned to identical risk groups as after FPB reconstruction. In 5.4 % of patients the risk group after HIR with the maximum iteration level was different from the group after FBP reconstruction. CONCLUSION There was an excellent correlation of Agatston scores after HIR and FBP with identical risk group assignment at levels 1 - 3 for all patients. Hence it appears that the application of HIR in routine calcium scoring does not entail any disadvantages. Thus, future studies are needed to demonstrate whether HIR is a reliable method for reducing radiation dose in coronary calcium scoring.

Differentiating the Multipoint Expected Improvement for Optimal Batch Design

This work deals with parallel optimization of expensive objective functions which are modelled as sample realizations of Gaussian processes. The study is formalized as a Bayesian optimization problem, or continuous multi-armed bandit problem, where a batch of q > 0 arms is pulled in parallel at each iteration. Several algorithms have been developed for choosing batches by trading off exploitation and exploration. As of today, the maximum Expected Improvement (EI) and Upper Confidence Bound (UCB) selection rules appear as the most prominent approaches for batch selection. Here, we build upon recent work on the multipoint Expected Improvement criterion, for which an analytic expansion relying on Tallis’ formula was recently established. The computational burden of this selection rule being still an issue in application, we derive a closed-form expression for the gradient of the multipoint Expected Improvement, which aims at facilitating its maximization using gradient-based ascent algorithms. Substantial computational savings are shown in application. In addition, our algorithms are tested numerically and compared to state-of-the-art UCB-based batchsequential algorithms. Combining starting designs relying on UCB with gradient-based EI local optimization finally appears as a sound option for batch design in distributed Gaussian Process optimization.

Euch in der Heimat : (Gedicht)

Jakob Picard

Alle müssen helfen ... : ein Mahnruf

Picard

[Rezension]

L. Picard

Das Wasserproblem Palästinas

Leo Picard

Entgegnung

Leo Picard

M. A. Klausner

Lucien Picard