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We present a direct method for limited angle tomographic reconstruction usingconvolutional networks. The key to our method is to first stretch every tiltview in the direction perpendicular to the tilt axis by the secant of the tiltangle. These stretched views are then fed into a 2-D U-Net which directlyoutputs the 3-D reconstruction. We train our networks by minimizing the meansquared error between the network's generated reconstruction and a ground truth3-D volume. To demonstrate and evaluate our method, we synthesize tilt viewsfrom a 3-D image of fly brain tissue acquired with Focused Ion Beam ScanningElectron Microscopy. We compare our method to using a U-Net to directlyreconstruct the unstretched tilt views and show that this simple stretchingprocedure leads to significantly better reconstructions. We also compare tousing a network to clean up reconstructions generated by backprojection andfiltered backprojection, and find that this simple stretching procedure alsogives lower mean squared error on previously unseen images.

Stretched sinograms for limited-angle tomographic reconstruction with neural networks