Federico Schlüter

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The performance of helical CT requires several user-defined parameters that exceed the requirements of conventional CT. One needs to carefully select the collimation, table increment, and reconstruction interval. Minimizing these parameters maximizes longitudinal resolution but with various trade-offs. Decreasing the collimation decreases the effective(More)
PURPOSE To assess the feasibility of digital deconvolution techniques to improve longitudinal resolution of spiral computed tomography (CT) multiplanar reformations and evaluate how technical factors in deconvolution affect longitudinal resolution, noise, and edge ringing. MATERIALS AND METHODS Longitudinal line spread function (LSF) of the system was(More)
PURPOSE To compare the diagnostic performance of digital subtraction angiography (DSA) to that of film-screen angiography (FSA) for detecting acute pulmonary embolism (PE) in a porcine model. MATERIALS AND METHODS DSA and FSA were performed in 13 pigs before and after central venous administration of autologous emboli. Results were compared to findings at(More)
In this work we consider the problem of learning the structure of Markov networks from data. We present an approach for tackling this problem called IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC algorithm, designed for avoiding important limitations of existing independence-based algorithms. These algorithms proceed by(More)
PURPOSE To determine whether computed tomography (CT) can help predict which patients will require surgical or bronchoscopic intervention during healing of bronchial anastomotic dehiscence after lung transplantation. MATERIALS AND METHODS The authors followed up 25 bronchoscopically proved dehiscent anastomoses through healing in 19 patients who underwent(More)
Markov random fields provide a compact representation of joint probability distributions by representing its independence properties in an undirected graph. The well-known Hammersley-Clifford theorem uses these conditional independences to factorize a Gibbs distribution into a set of factors. However, an important issue of using a graph to represent(More)
The problem of learning the Markov network structure from data has become increasingly important in machine learning, and in many other application fields. Markov networks are probabilistic graphical models, a widely used formalism for handling probability distributions in intelligent systems. This document focuses on a technology called independence-based(More)
This work focuses on learning the structure of Markov networks. Markov networks are parametric models for compactly representing complex probability distributions. These models are composed by: a structure and numerical weights. The structure describes in-dependences that hold in the distribution. Depending on the goal of learning intended by the user,(More)
Learning the Markov network structure from data is a problem that has received considerable attention in machine learning, and in many other application fields. This work focuses on a particular approach for this purpose called Independence-Based learning. Such approach guarantees the learning of the correct structure efficiently, whenever data is(More)