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Endothelial dysfunction is associated with cardiovascular diseases and their risk factors (CVRF), and flow-mediated dilation (FMD) is increasingly used to explore it. In this test, artery diameter changes after post-ischaemic hyperaemia are classically quantified using maximum peak vasodilation (FMDc). To obtain more detailed descriptors of FMD we applied principal component analysis (PCA) to diameter-time curves (absolute), vasodilation-time curves (relative) and blood-velocity-time curves. Furthermore, combined PCA of vessel size and blood-velocity curves allowed exploring links between flow and dilation. Vessel diameter data for PCA (post-ischaemic: 140 s) were acquired from brachial ultrasound image sequences of 173 healthy male subjects using a computerized technique previously reported by our team based on image registration (Frangi et al 2003 IEEE Trans. Med. Imaging 22 1458). PCA provides a set of axes (called eigenmodes) that captures the underlying variation present in a database of waveforms so that the first few eigenmodes retain most of the variation. These eigenmodes can be used to synthesize each waveform analysed by means of only a few parameters, as well as potentially any signal of the same type derived from tests of new patients. The eigenmodes obtained seemed related to visual features of the waveform of the FMD process. Subsequently, we used eigenmodes to parameterize our data. Most of the main parameters (13 out of 15) correlated with FMDc. Furthermore, not all parameters correlated with the same CVRF tested, that is, serum lipids (i.e., high LDL-c associated with slow vessel return to a baseline, while low HDL-c associated with a lower vasodilation in response to similar velocity stimulus), thus suggesting that this parameterization allows a more detailed and factored description of the process than FMDc.

Point Distribution Modelling (PDM) is an efficient generative technique that can be used to incorporate statistical shape priors into image analysis methods like Active Shape Models (ASMs) or Active Appearance Models (AAMs). They are described by a set of landmarks usually manually pinpointed in a training set. Frangi et al. [1] have proposed an automatic auto-landmarking technique capable of dealing with multi-object arrangements. In this paper, we present an experimental extension of this previous work, validating the method provided. Our contributions can be summarized as follows: A two-chamber shape model of the heart is constructed from a large data-set comprising 90 subjects and considering 5 phases of the cardiac cycle. The computational demand of our technique is addressed using Grid computing. The results of our experiments suggest that the method presented in [1] as a proof-of-concept, can truly cope with the large inter-subject and inter-phase deformations present in clinical cardiac data sets including pathologies. The achieved accuracy in our validation is comparable to the former tests.

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The use of affine image registration based on normalized mutual information (NMI) has recently been proposed by Frangi et al. as an automatic method for assessing brachial artery flow mediated dilation (FMD) for the characterization of endothelial function. Even though this method solves many problems of previous approaches, there are still some situations that can lead to misregistration between frames, such as the presence of adjacent vessels due to probe movement, muscle fibres or poor image quality. Despite its widespread use as a registration metric and its promising results, MI is not the panacea and can occasionally fail. Previous work has attempted to include spatial information into the image similarity metric. Among these methods the direct estimation of α-MI through Minimum Euclidean Graphs allows to include spatial information and it seems suitable to tackle the registration problem in vascular images, where well oriented structures corresponding to vessel walls and muscle fibres are present. The purpose of this work is twofold. Firstly, we aim to evaluate the effect of including spatial information in the performance of the method suggested by Frangi et al. by using α-MI of spatial features as similarity metric. Secondly, the application of image registration to long image sequences in which both rigid motion and deformation are present will be used as a benchmark to prove the value of α-MI as a similarity metric, and will also allow us to make a comparative study with respect to NMI.