A Spatio-temporal Analysis of Contrast Ultrasound Image Sequences for Assessment of Tissue Perfusion

Abstract

Abstract. The evaluation of tissue perfusion in various parenchymatous organs is important in the diagnosis and determination of the severity of ischemic disease. Contrast ultrasound perfusion imaging can be used for this purpose. This paper describes a method that identifies different areas of perfusion in a contrast ultrasound perfusion study. Pixels in an image sequence are automatically classified into different classes, by analysing their distinct temporal relationships. A novel method is presented that uses a Bayesian Factor Analysis Model set in a Markov Random Field framework; utilising both the temporal and spatial characteristics of the pixels for classification. Preliminary results are demonstrated for simulated data, and a myocardial perfusion in-vivo dataset.

Improved Modelling of Ultrasound Contrast Agent Diminution for Blood Perfusion Analysis

“…An improvement was made by [6] to be able to directly calculate the parameters and avoid the time-consuming fitting process. Other approaches tried to determine the perfusion in a general way, independent of the actual UCA concentration and insonation pattern used [7]. However, they do not seem to have found wide acceptance.…”