The effect of bandwidth on the quality of transmitted pediatric echocardiograms

Background: Remote access to pediatric cardiology diagnostic services is enabled by real-time transmission of echocardiographic images. Several transmission bandwidths have been used but there has been little analysis of image quality provided by different bandwidths. We designed a study of the quality of transmitted images at various bandwidths. Methods: Two echocardiographers viewed randomly a series of 13 recorded pediatric echocardiographic images either directly or after transmission using 1 of 4 bandwidths: 256; 384; 512; or 768 kbps. An image clarity scoring scale was used to assess image quality of cardiac structures. Results: Measurable differences were found in image quality with different transmission bandwidths; 512 kbps was the minimum for consistently clear imaging of all cardiac structures examined. Conclusion: Bandwidth greater than 512 kbps confers sharper images subjectively although this could not be quantified by our methods.

Myocardial blood volume and perfusion reserve responses to combined dipyridamole and exercise stress: A quantitative approach to contrast stress echocardiography

Background: Qualitative interpretation of myocardial contrast echocardiography (MCE) improves the accuracy of wall-motion analysis for assessment of coronary artery disease (CAD). We examined the feasibility and accuracy of quantitative MCE for diagnosis of CAD. Methods: Dipyridamole/exercise stress MCE (destruction-replenishment protocol with real-time imaging) was performed in 90 patients undergoing quantitative coronary angiography, 48 of whom had significant (> 50%) stenoses. MCE was repeated with exercise alone in 18 patients. Myocardial blood flow (A*beta) was obtained from blood volume (A) and time to refill (beta). Results: Quantification of flow reserve was feasible in 88%. The mean A*beta reserve in the anterior wall was significantly impaired for patients with left anterior descending coronary artery disease (n = 28) compared with those with no disease (1.6 +/- 1.2 vs; 4.0 +/- 2.5, P <=.001). This reflected impaired beta reserve, with no difference in the A reserve. Applying a receiver operating characteristic curve derived cutoff of 2.0 for A*beta reserve, quantitative MCE was 76% sensitive and 71% specific for the diagnosis of significant left anterior descending coronary artery stenosis. Posterior circulation results were similar, with 78% sensitivity and 59% specificity for detection of posterior CAD. Overall, quantitative MCE was similarly sensitive to qualitative approach for diagnosis of CAD (88% vs 93%), but with lower specificity (52% vs 65%, P =.07). In 18 patients restudied with pure exercise stress, the mean myocardial blood flow reserve was less than after combined stress (2.1 +/- 1.6 vs 3.7 +/- 1.9, P =.01). Conclusion: Quantitative MCE is feasible for the diagnosis of CAD with dipyridamole/exercise stress. Dipyridamole prolongs postexercise hyperemia, augmenting the degree of hyperemia at the time of imaging.

Use of real-time three-dimensional echocardiography to measure left atrial volume: Comparison with other echocardiographic techniques

Objectives: Left atrial (LA) volume (LAV) is a prognostically important biomarker for diastolic dysfunction, but its reproducibility on repeated testing is not well defined. LA assessment with 3-dimensional. (3D) echocardiography (3DE) has been validated against magnetic resonance imaging, and we sought to assess whether this was superior to existing measurements for sequential echocardiographic follow-up. Methods: Patients (n = 100; 81 men; age 56 +/- 14 years) presenting for LA evaluation were studied with M-mode (MM) echocardiography, 2-dimensional (2D) echocardiography, and 3DE. Test-retest variation was performed by a complete restudy by a separate sonographer within 1 hour without alteration of hemodynamics or therapy. In all, 20 patients were studied for interobserver and intraobserver variation. LAVs were calculated by using M-mode diameter and planimetered atrial area in the apical. 4-chamber view to calculate an assumed sphere, as were prolate ellipsoid, Simpson's biplane, and biplane area-length methods. All were compared with 3DE. Results: The average LAV was 72 +/- 27 mL by 3DE. There was significant underestimation of LAV by M-mode (35 +/- 20 mL, r = 0.66, P < .01). The 3DE and various 2D echocardiographic techniques were well correlated: LA planimetry (85 +/- 38 mL, r = 0.77, P < .01), prolate ellipsoid (73 +/- 36 mL, r = 0.73, P = .04), area-length (64 +/- 30 mL, r = 0.74, P < .01), and Simpson's biplane (69 +/- 31 mL, r = 0.78, P = .06). Test-retest variation for 3DE was most favorable (r = 0.98, P < .01), with the prolate ellipsoid method showing most variation. Interobserver agreement between measurements was best for 3DE (r = 0.99, P < .01), with M-mode the worst (r = 0.89, P < .01). Intraobserver results were similar to interobserver, the best correlation for 3DE (r = 0.99, P < .01), with LA planimetry the worst (r = 0.91, P < .01). Conclusions. The 2D measurements correlate closely with 3DE. Follow-up assessment in daily practice appears feasible and reliable with both 2D and 3D approaches.

RatSLAM: A hippocampal model for simultaneous localisation and mapping

The work presents a new approach to the problem of simultaneous localization and mapping - SLAM - inspired by computational models of the hippocampus of rodents. The rodent hippocampus has been extensively studied with respect to navigation tasks, and displays many of the properties of a desirable SLAM solution. RatSLAM is an implementation of a hippocampal model that can perform SLAM in real time on a real robot. It uses a competitive attractor network to integrate odometric information with landmark sensing to form a consistent representation of the environment. Experimental results show that RatSLAM can operate with ambiguous landmark information and recover from both minor and major path integration errors.

Evolving genetic regulatory networks using an artificial genome

Boolean models of genetic regulatory networks (GRNs) have been shown to exhibit many of the characteristic dynamics of real GRNs, with gene expression patterns settling to point attractors or limit cycles, or displaying chaotic behaviour, depending upon the connectivity of the network and the relative proportions of excitatory and inhibitory interactions. This range of behaviours is only apparent, however, when the nodes of the GRN are updated synchronously, a biologically implausible state of affairs. In this paper we demonstrate that evolution can produce GRNs with interesting dynamics under an asynchronous update scheme. We use an Artificial Genome to generate networks which exhibit limit cycle dynamics when updated synchronously, but collapse to a point attractor when updated asynchronously. Using a hill climbing algorithm the networks are then evolved using a fitness function which rewards patterns of gene expression which revisit as many previously seen states as possible. The final networks exhibit “fuzzy limit cycle” dynamics when updated asynchronously.

Hippocampal models for simultaneous localisation and mapping on an autonomous robot

To navigate successfully in a novel environment a robot needs to be able to Simultaneously Localize And Map (SLAM) its surroundings. The most successful solutions to this problem so far have involved probabilistic algorithms, but there has been much promising work involving systems based on the workings of part of the rodent brain known as the hippocampus. In this paper we present a biologically plausible system called RatSLAM that uses competitive attractor networks to carry out SLAM in a probabilistic manner. The system can effectively perform parameter self-calibration and SLAM in onedimension. Tests in two dimensional environments revealed the inability of the RatSLAM system to maintain multiple pose hypotheses in the face of ambiguous visual input. These results support recent rat experimentation that suggest current competitive attractor models are not a complete solution to the hippocampal modelling problem.

Description and training of neural network dynamics

Attractor properties of a popular discrete-time neural network model are illustrated through numerical simulations. The most complex dynamics is found to occur within particular ranges of parameters controlling the symmetry and magnitude of the weight matrix. A small network model is observed to produce fixed points, limit cycles, mode-locking, the Ruelle-Takens route to chaos, and the period-doubling route to chaos. Training algorithms for tuning this dynamical behaviour are discussed. Training can be an easy or difficult task, depending whether the problem requires the use of temporal information distributed over long time intervals. Such problems require training algorithms which can handle hidden nodes. The most prominent of these algorithms, back propagation through time, solves the temporal credit assignment problem in a way which can work only if the relevant information is distributed locally in time. The Moving Targets algorithm works for the more general case, but is computationally intensive, and prone to local minima.

The 'moving targets' training algorithm

A simple method for training the dynamical behavior of a neural network is derived. It is applicable to any training problem in discrete-time networks with arbitrary feedback. The method resembles back-propagation in that it is a least-squares, gradient-based optimization method, but the optimization is carried out in the hidden part of state space instead of weight space. A straightforward adaptation of this method to feedforward networks offers an alternative to training by conventional back-propagation. Computational results are presented for simple dynamical training problems, with varied success. The failures appear to arise when the method converges to a chaotic attractor. A patch-up for this problem is proposed. The patch-up involves a technique for implementing inequality constraints which may be of interest in its own right.

Parabolic optical pulses under the action of the third-order dispersion

Recent developments in nonlinear optics reveal an interesting class of pulses with a parabolic intensity profile in the energy-containing core and a linear frequency chirp that can propagate in a fiber with normal group-velocity dispersion. Parabolic pulses propagate in a stable selfsimilar manner, holding certain relations (scaling) between pulse power, width, and chirp parameter. In the additional presence of linear amplification, they enjoy the remarkable property of representing a common asymptotic state (or attractor) for arbitrary initial conditions. Analytically, self-similar (SS) parabolic pulses can be found as asymptotic, approximate solutions of the nonlinear Schr¨odinger equation (NLSE) with gain in the semi-classical (largeamplitude/small-dispersion) limit. By analogy with the well-known stable dynamics of solitary waves - solitons, these SS parabolic pulses have come to be known as similaritons. In practical fiber systems, inherent third-order dispersion (TOD) in the fiber always introduces a certain degree of asymmetry in the structure of the propagating pulse, eventually leading to pulse break-up. To date, there is no analytic theory of parabolic pulses under the action of TOD. Here, we develop aWKB perturbation analysis that describes the effect of weak TOD on the parabolic pulse solution of the NLSE in a fiber gain medium. The induced perturbation in phase and amplitude can be found to any order. The theoretical model predicts with sufficient accuracy the pulse structural changes induced by TOD, which are observed through direct numerical NLSE simulations.