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The angular range and number of projections are parameters that directly influence the image quality and the visibility of lesions in digital breast tomosynthesis (DBT). The medical field is taking advantage of the increasing performance of machine learning algorithms with the use of complex data-driven models, known as deep learning (DL) networks. The use of DL has also been highlighted in the tasks of video frame interpolation (VFI) for the synthesis of new images in order to increase the frame rate per second. In the present work, we use a residual refinement interpolation network (RRIN) to generate new synthetic DBT projections from pairs of real projections. We studied two different approaches: first, we increased the number of projections before reconstruction using the synthetic images, with the aim of improving the quality of the reconstructed slices without increasing the radiation dose to the patient. In the second, we investigated the effect of replacing existing projections with synthetic ones, with the objective of reducing the radiation dose and acquisition time. In the first approach, we used virtual phantoms to generate sets of DBT projections to train the network. We then evaluated the contrast-to-noise ratio (CNR) of simulated microcalcifications after reconstruction. The CNR was higher for all sets where supplementary images were added compared to those with only real images. In the second approach, we trained the network with clinical data and tested it with images acquired with a physical anthropomorphic breast phantom. Both the projections and the slices showed good similarity with the real ones, suggesting that the use of VFI networks to generate DBT projections is promising. However, further studies should be carried out to assess the feasibility of this approach.
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