A Hypothesis-Test Framework for Quantitative Lesion Detection and Diagnosis

Abstract

A method is presented which quantifies the radiodensity of lesions in projection images, providing a diagnostic indicator to better inform the decisions of both human readers and computer algorithms. The models of image formation underlying the Standard Attenuation Rate (SAR) are used to facilitate the forward simulation of the appearance of a lesion in a breast. By forming hypotheses, informed from measurements on the acquired image, virtual 3D scenes are constructed which predict the size, position and radiodensity of a suspect lesion and the surrounding breast tissue. Comparisons between simulations of this scene, and the acquired image enable both the refinement of the hypothesis, and the assessment of the likelihood of the hypothesis being correct. In the event of a high likelihood of correctness, the hypothesised lesion informs diagnosis. The application of the method to a patient image containing a cyst shows it has an attenuation corresponding to water (SAR 1.246), and an invasive carcinoma which is considerably denser at SAR 2.27. Thus the technique yields a quantitative radiodensity measure for discrimination in diagnostic decision making.