Volumetric Reconstruction from a Limited Number of Digitally Reconstructed Radiographs Using CNNs

We propose a method for 3D computed tomography (CT) image reconstruction from 3D digitally reconstructed radiographs (DRR). The 3D DRR images are generated from 2D projection images of the 3D CT image from different angles and used to train a convolutional neural network (CNN). Evaluating with a different number of input DRR images, we compare our resulting 3D CT reconstruction to those of the filtered backprojection (FBP), which represents the standard method for CT image reconstruction. The evaluation shows that our CNN based method is able to decrease the number of projection images necessary to reconstruct the original image without a significant reduction in image quality. This indicates the potential for accurate 3D reconstruction from a lower number of projection images leading to a reduced amount of ionizing radiation exposure during CT image acquisition.

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