Joint Registration and Limited-Angle Reconstruction of Digital Breast Tomosynthesis

Digital breast tomosynthesis (DBT), an emerging imaging modality, provides a pseudo-3D image of the breast. Algorithms to aid the human observer process these large datasets involve two key tasks: reconstruction and registration. Previous studies separated these steps, solving each task independently. This can be effective if reconstructing using a complete set of data, e.g., in cone beam CT, assuming that only simple deformations exist. However, for ill-posed limited-angle problems such as DBT, estimating the deformation is complicated by the significant artefacts associated with DBT reconstructions, leading to severe inaccuracies in the registration. In this paper, we present an innovative algorithm, which combines reconstruction of a pair of temporal DBT acquisitions with their simultaneous registration. Using various computational phantoms and in vivo DBT simulations, we show that, compared to the conventional sequential method, jointly estimating image intensities and transformation parameters gives superior results with respect to reconstruction fidelity and registration accuracy

Numerical Approaches for Solving the Combined Reconstruction and Registration of Digital Breast Tomosynthesis

Heavy demands on the development of medical imaging modalities for breast cancer detection have been witnessed in the last three decades in an attempt to reduce the mortality associated with the disease. Recently, Digital Breast Tomosynthesis (DBT) shows its promising in the early diagnosis when lesions are small. In particular, it offers potential benefits over X-ray mammography - the current modality of choice for breast screening - of increased sensitivity and specificity for comparable X-ray dose, speed, and cost. An important feature of DBT is that it provides a pseudo-3D image of the breast. This is of particular relevance for heterogeneous dense breasts of young women, which can inhibit detection of cancer using conventional mammography. In the same way that it is difficult to see a bird from the edge of the forest, detecting cancer in a conventional 2D mammogram is a challenging task. Three-dimensional DBT, however, enables us to step through the forest, i.e., the breast, reducing the confounding effect of superimposed tissue and so (potentially) increasing the sensitivity and specificity of cancer detection. The workflow in which DBT would be used clinically, involves two key tasks: reconstruction, to generate a 3D image of the breast, and registration, to enable images from different visits to be compared as is routinely performed by radiologists working with conventional mammograms. Conventional approaches proposed in the literature separate these steps, solving each task independently. This can be effective if reconstructing using a complete set of data. However, for ill-posed limited-angle problems such as DBT, estimating the deformation is difficult because of the significant artefacts associated with DBT reconstructions, leading to severe inaccuracies in the registration. The aim of my work is to find and evaluate methods capable of allying these two tasks, which will enhance the performance of each process as a result. Consequently, I prove that the processes of reconstruction and registration of DBT are not independent but reciprocal. This thesis proposes innovative numerical approaches combining reconstruction of a pair of temporal DBT acquisitions with their registration iteratively and simultaneously. To evaluate the performance of my methods I use synthetic images, breast MRI, and DBT simulations with in-vivo breast compressions. I show that, compared to the conventional sequential method, jointly estimating image intensities and transformation parameters gives superior results with respect to both reconstruction fidelity and registration accuracy