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- W4386514882 abstract "Abstract We evaluate a model-based deep learning reconstruction algorithm for optical projection tomography images. The method iterates over a data consistency step and an image domain artefact removal step achieved by a convolutional neural network. A preprocessing stage is also included to avoid potential misalignments between the sample center of rotation and the detector. Our algorithm is trained in a database of wild-type zebrafish (Denio Rerio) at different stages of development to minimise the mean square error for a fixed number of iterations. Using a cross-validation scheme, we compare the results to other methods, such as filtered backprojection, total variation minimization and a direct deep learning (DL) method where the pseudo inverse solution is corrected by a U-Net. We find our method performs equal or better than the alternatives. For a reduced number of projections in comparison to our ground truth, only U-Net is comparable, but we find that our proposal achieves a much better performance if the amount of data available for training is limited, given the much smaller number of parameters our trainable network has." @default.
- W4386514882 created "2023-09-08" @default.
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- W4386514882 date "2023-09-07" @default.
- W4386514882 modified "2023-10-18" @default.
- W4386514882 title "ToMoDL: A model-based deep learning framework for optical projection tomography" @default.
- W4386514882 doi "https://doi.org/10.21203/rs.3.rs-3318045/v1" @default.
- W4386514882 hasPublicationYear "2023" @default.
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