A Variational Approach to Multi-Modality Subsurface Data Inversion and Fusion Based on Shared Image Structure

Abstract

In many subsurface sensing problems single-sensor information quality is poor, due to factors such as constrained sensing geometries and limited energy penetration. In such cases there is interest in combining information from multiple complementary sensing modalities. In this work, we describe a variational approach to joint multi-modality image formation which fuses boundary information that is shared between a group of heterogeneous imaging modalities. The specific application that motivates this work is the imaging of vulnerable atherosclerotic plaques. No single imaging modality has yet demonstrated the ability to detect these vulnerable lesions reliably. We demonstrate our approach by fusing shared boundary field estimates from MR and CT atherosclerotic lesion imagery into a single estimated underlying tissue boundary field, while simultaneously estimating and enhancing the original imagery. More generally, we present an approach for multi-modality subsurface data inversion and fusion based on shared image structure. This approach allows for better estimates of the characteristics and structure of the underlying scene.