Intravascular ultrasound (IVUS) is a catheter based medical imaging technique particularly useful for studying atherosclerotic disease. It produces cross-sectional images of blood vessels that provide quantitative assessment of the vascular wall, information about the nature of atherosclerotic lesions as well as plaque shape and size. Automatic processing of large IVUS data sets represents an important challenge due to ultrasound speckle, catheter artifacts or calcification shadows. A new three-dimensional (3-D) IVUS segmentation model, that is based on the fast-marching method and uses gray level probability density functions (PDFs) of the vessel wall structures, was developed. The gray level distribution of the whole IVUS pullback was modeled with a mixture of Rayleigh PDFs. With multiple interface fast-marching segmentation, the lumen, intima plus plaque structure, and media layers of the vessel wall were computed simultaneously. The PDF-based fast-marching was applied to 9 in vivo IVUS pullbacks of superficial femoral arteries and to a simulated IVUS pullback. Accurate results were obtained on simulated data with average point to point distances between detected vessel wall borders and ground truth <0.072 mm. On in vivo IVUS, a good overall performance was obtained with average distance between segmentation results and manually traced contours <0.16 mm. Moreover, the worst point to point variation between detected and manually traced contours stayed low with Hausdorff distances <0.40 mm, indicating a good performance in regions lacking information or containing artifacts. In conclusion, segmentation results demonstrated the potential of gray level PDF and fast-marching methods in 3-D IVUS image processing.

Statistical time series models are practical tools in public health surveillance. Their capacity to forecast future disease incidence values exemplifies their usefulness. Using these forecasts, one can develop strategies to trigger alerts to public health officials when irregular disease incidence values have occurred. Clearly, the better the forecasting performance of the model class used in the time series analysis, the more realistic are the alerts triggered. The time series analysis of disease incidence values has often entailed the Box and Jenkins model class. However, this class was designed to model real‐valued variables whereas disease incidences are integer‐valued variables. A new class of time series models, called integer‐valued autoregressive models, has been developed and studied over the past decade. The objective of this paper is to introduce this new class of models to the application of time series analysis of infectious disease incidence, and to demonstrate its advantages over the class of real‐valued Box and Jenkins models. The paper also presents a bootstrap method developed for the calculation of forecast interval limits. Copyright © 1999 John Wiley & Sons, Ltd.

Statistical time series models are practical tools in public health surveillance. Their capacity to forecast future disease incidence values exempli"es their usefulness. Using these forecasts, one can develop strategies to trigger alerts to public health o$cials when irregular disease incidence values have occurred. Clearly, the better the forecasting performance of the model class used in the time series analysis, the more realistic are the alerts triggered. The time series analysis of disease incidence values has often entailed the Box and Jenkins model class. However, this class was designed to model real-valued variables whereas disease incidences are integer-valued variables. A new class of time series models, called integer-valued autoregressive models, has been developed and studied over the past decade. The objective of this paper is to introduce this new class of models to the application of time series analysis of infectious disease incidence, and to demonstrate its advantages over the class of real-valued Box and Jenkins models. The paper also presents a bootstrap method developed for the calculation of forecast interval limits. forecasts of future incidence values. These forecasts estimate the expected incidence values under normal, or regular, circumstances. One way to construct a statistical alerting strategy is to compare such a forecast with the most recently observed disease incidence value. If statistically signi"cant, that is, if its value exceeds the upper limit of the 95 per cent forecast interval, then one triggers an alarm to re#ect occurrence of the irregular disease incidence value. One can then focus epidemiologic investigation on the corresponding time period to determine if an epidemiologic outbreak has in fact occurred. Clearly, the better the forecasting performance of the model class used in the time series analysis, the better is the performance of the statistical alerting strategy in detection of irregular disease incidence values.Real-valued time series models, introduced by Box and Jenkins, have been used in many applications including the analysis of infectious disease surveillance data.\ However, when modelling non-negative integer-valued data such as the time series of period incidence values (counts of new cases of disease occurring over consecutive time periods), Box and Jenkins models may be inappropriate. This is especially so for the analysis of a rare disease. Over the past decade, a new class of time series models, called integer-valued autoregressive models, has been studied by many authors.\ This class of models is particularly applicable to the analysis of count data and therefore o!ers an alternative to the real-valued time series models.Other classes of models for time series of counts have been proposed by Zeger and Zeger and Qaqish. Like the integer-valued autoregressive models, the Markov models proposed by Zeger and Qaqish are observation-driven, while the models proposed by Zeger are parameter-driven; the two types of models distinguished by Cox for time-depe...

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.