Feed-forward convolutional neural networks (CNN) have been successfully applied in the medical imaging domain. A 12 layer CNN model was constructed to extract features for image classification. A structured phantom was scanned on a Siemens DBT system at three dose levels: dose set by the automatic exposure control (AEC) system, half this dose and double. After training the CNN on 36 DBT acquisitions (51840 image segments), newly acquired test images were categorized by the algorithm into the dose categories with an accuracy of 99.7%. Parallel to that the standard methods as NNPS and pixel value (PV) mean and variance calculated for the projection and reconstructed planes also show ability to detect the dose level change with some limitations for the reconstructed planes. This result indicates the potential for further use of deep learning algorithms for DQC when using only the reconstructed DBT planes.
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