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Circular cone-beam (CCB) Computed Tomography (CT) has become an integral partof industrial quality control, materials science and medical imaging. The needto acquire and process each scan in a short time naturally leads to trade-offsbetween speed and reconstruction quality, creating a need for fastreconstruction algorithms capable of creating accurate reconstructions fromlimited data.  In this paper we introduce the Neural Network Feldkamp-Davis-Kress (NN-FDK)algorithm. This algorithm adds a machine learning component to the FDKalgorithm to improve its reconstruction accuracy while maintaining itscomputational efficiency. Moreover, the NN-FDK algorithm is designed such thatit has low training data requirements and is fast to train. This ensures thatthe proposed algorithm can be used to improve image quality in high throughputCT scanning settings, where FDK is currently used to keep pace with theacquisition speed using readily available computational resources.  We compare the NN-FDK algorithm to two standard CT reconstruction algorithmsand to two popular deep neural networks trained to remove reconstructionartifacts from the 2D slices of an FDK reconstruction. We show that the NN-FDKreconstruction algorithm is substantially faster in computing a reconstructionthan all the tested alternative methods except for the standard FDK algorithmand we show it can compute accurate CCB CT reconstructions in cases of highnoise, a low number of projection angles or large cone angles. Moreover, weshow that the training time of an NN-FDK network is orders of magnitude lowerthan the considered deep neural networks, with only a slight reduction inreconstruction accuracy.

A computationally efficient reconstruction algorithm for circular cone-beam computed tomography using shallow neural networks