Deep learning-based artifact detection for diagnostic CT images

Calibrated detector response is crucial to good image quality in diagnostic CT and imaging systems in general. Defects during manufacturing, component failures and system aging can introduce shift in detector response which, if left uncorrected, can lead to image artifacts. Such artifacts reduce the image quality and can cause misdiagnosis in clinical practice. In this work a deep learning (DL)-based artifact detection method is developed to automatically screen for common imaging detector induced artifacts such as rings, streaks and bands in images. To circumvent the difficulty in obtaining and annotating the artifact images, a diagnostic CT physics simulator is utilized to generate CT images across a range of acquisition and reconstruction settings. Artifacts are introduced in the projection view data by perturbing the detector gain relative to the gain normalization scan during the simulation. The artifact images and corresponding ground truth segmentation of the artifact type and location serve as the training dataset. Linear support vector machine with squared hinge loss (L2-SVM) was used as the loss function during training as early experiments showed small but consistent improvements over the more commonly used cross-entropy loss for segmentation. The trained network achieved ~97%, ~86% and ~93% independent test accuracy for ring, streak and band artifacts respectively. Since deep learning methods learn by example, the detection method is not limited to the imaging scenarios presented in this work and can be extended to other applications.