This paper presents a new method for classification of neck movement patterns related to Whiplash-associated disorders (WAD) using a resilient backpropagation neural network (BPNN). WAD are a common diagnosis after neck trauma, typically caused by rear-end car accidents. Since physical injuries seldom are found with present imaging techniques, the diagnosis can be difficult to make. The active range of the neck is often visually inspected in patients with neck pain, but this is a subjective measure, and a more objective decision support system, that gives a reliable and more detailed analysis of neck movement pattern, is needed. The objective of this study was to evaluate the predictive ability of a BPNN, using neck movement variables as input. Three-dimensional (3-D) neck movement data from 59 subjects with WAD and 56 control subjects were collected with a ProReflex system. Rotation angle and angle velocity were calculated using the instantaneous helical axis method and motion variables were extracted. A principal component analysis was performed in order to reduce data and improve the BPNN performance. BPNNs with six hidden nodes had a predictivity of 0.89, a sensitivity of 0.90 and a specificity of 0.88, which are very promising results. This shows that neck movement analysis combined with a neural network could build the basis of a decision support system for classifying suspected WAD, even though further evaluation of the method is needed.
Plaque rupture is correlated with the plaque morphology, composition, mechanical properties, and with the blood pressure. Whereas the geometry can accurately be assessed with intravascular ultrasound (IVUS) imaging, intravascular elastography (IVE) is capable of extracting information on the plaque local mechanical properties and composition. This paper reports additional IVE validation data regarding reproducibility and potential to characterize atherosclerotic plaques and mural thrombi. In a first investigation, radio frequency (RF) data were acquired from the abdominal aorta of an atherosclerotic rabbit model. In a second investigation, IVUS RF data were recorded from the left coronary artery of a patient referred for angioplasty. In both cases, Galaxy IVUS scanners (Boston Scientific, Freemont, CA), equipped with 40 MHz Atlantis catheters, were used. Elastograms were computed using two methods, the Lagrangian speckle model estimator (LSME) and the scaling factor estimator (SFE). Corroborated with histology, the LSME and the SFE both clearly detected a soft thrombus attached to the vascular wall. Moreover, shear elastograms, only available with the LSME, confirmed the presence of the thrombus. Additionally, IVE was found reproducible with consistent elastograms between cardiac cycles (CCs). Regarding the human dataset, only the LSME was capable of identifying a plaque that presumably sheltered a lipid core. Whereas such an assumption could not be certified with histology, radial shear and tangential strain LSME elastograms enabled the same conclusion. It is worth emphasizing that this paper reports the first ever in vivo tangential strain elastogram with regards to vascular imaging, due to the LSME. It is concluded that the IVE was reproducible exhibiting consistent strain patterns between CCs. The IVE might provide a unique tool to assess coronary wall lesions.
Detection of white matter changes of the brain using magnetic resonance imaging (MRI) has increasingly been an active and challenging research area in computational neuroscience. There have rarely been any single image analysis methods that can effectively address the issue of automated quantification of neuroimages, which are subject to different interests of various medical hypotheses. This paper presents new image segmentation models for automated detection of white matter changes of the brain in an elderly population. The methods are based on the computational models of fuzzy clustering, possibilistic clustering, geostatistics, and knowledge combination. Experimental results on MRI data have shown that the proposed image analysis methodology can be applied as a very useful computerized tool for the validation of our particular medical question, where white matter changes of the brain are thought to be the most important social medical evidence.
This paper presents an assessment tool for objective neck movement analysis of subjects suffering from chronic whiplash-associated disorders (WAD). Three-dimensional (3-D) motion data is collected by a commercially available motion analysis system. Head rotation, defined in this paper as the rotation angle around the instantaneous helical axis (IHA), is used for extracting a number of variables (e.g., angular velocity and range, symmetry of motion). Statistically significant differences were found between controls and subjects with chronic WAD in a number of variables.