Joint Sino-German Research projekt: Feature based bi-modal image reconstruction

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Biomedical imaging aims at visualizing structural or functional information necessary for biological and pharmaceutical research or clinical diagnosis. The associated mathematical challenge is to reconstruct the information of interest from measured data, which typically poses an ill-posed inverse problem. Some recent developments focus on multi-modality technologies to enrich image information by fusing multiple imaging modalities. The strategy is to conduct imaging with multiple modalities, for example, performing diffuse optical tomography (DOT) and X-ray computerized tomography (XCT), simultaneously or sequentially. Classical approaches solve the related inverse problems separately and sequentially, such as computing an XCT reconstruction first followed by adding a DOT reconstruction.The natural observation that motivates the current project is that images of the same object, though obtained from different modalities, possess similar complementary feature information. Such feature information is in particular image edges and learned dictionaries for efficient representations. Hence, the complementary feature information from one modality can improve and steer the reconstruction of another modality, and vice versa, in an iterative manner. Our hypothesis is that image reconstructions for multi-modality systems can be jointly performed with enhanced image quality with less measured data via the communication through their feature information.The innovation of this project is to jointly solve the multiple inverse imaging problems rather than sequentially as previous approaches. Specific aims and milestones of this project are: M1) to develop a mathematical theory for feature representations from multiple imaging domains including similarity definitions of features and to apply them in regularization schemes for joint reconstruction from multi-modality data; M2) to establish efficient algorithms and their implementations for multi-modality feature regularized inverse problem; M3) validation of the methods for MALDI plus XCT and for DOT plus XCT. Upon the completion of this project, a new theory for imaging modality fusion will be established together with efficient algorithms and implementations.