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

机译：基于共享图像结构的多模式地下数据反演与融合的变分方法

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摘要

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.
机译：在许多地下传感问题中，由于诸如传感几何形状受限和能量渗透受限等因素，单传感器信息质量很差。在这种情况下，有兴趣组合来自多个互补感测模态的信息。在这项工作中，我们描述了一种联合多模态成像的变型方法，该方法融合了一组异质成像模态之间共享的边界信息。推动这项工作的特定应用是易损动脉粥样斑块的成像。尚无单一的成像方式能够可靠地检测出这些脆弱的病变。我们通过将来自MR和CT动脉粥样硬化病变图像的共享边界场估计融合到单个估计的基础组织边界场中，同时估计和增强原始图像来证明我们的方法。更笼统地说，我们提出了一种基于共享图像结构的多模式地下数据反演和融合方法。这种方法可以更好地估计基础场景的特征和结构。

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