Evaluation of accuracy in partial volume analysis of small objects

Accurate and robust assessment of quantitative parameters is a key issue in many fields of medical image analysis, and can have a direct impact on diagnosis and treatment monitoring. Especially for the analysis of small structures such as focal lesions in patients with MS, the finite spatial resolution of imaging devices is often a limiting factor that results in a mixture of different tissue types. We propose a new method that allows an accurate quantification of medical image data, focusing on a dedicated model for partial volume (PV) artifacts. Today, a widely accepted model assumption is that of a uniformly distributed linear mixture of pure tissues. However, several publications have clearly shown that this is not an appropriate choice in many cases. We propose a generalization of current PV models based on the Beta distribution, yielding a more accurate quantification. Furthermore, we present a new classification scheme. Prior knowledge obtained from a set of training data allows a robust initial estimate of the proper model parameters, even in cases of objects with predominant PV artifacts. A maximum likelihood based clustering algorithm is employed, resulting in a robust volume estimate. Experiments are carried out on more than 100 stylized software phantoms as well as on realistic phantom data sets. A comparison with current mixture models shows the capabilities of our approach.