Abstract: Metal artifacts are reduced in x-ray computed tomography (“CT”) images using a suitably trained neural network, such as a convolutional neural network (“CNN”). Virtual metal DATA objects are inserted to either the raw projection data or CT image data (e.g., from pre-procedural CT scans) to generate sets of matching artifact-corrupted and artifact-uncorrupted images, and a CNN, or other neural network, is trained to separate the contribution to each image pixel due to patient anatomy, metal object, or metal object-induced artifact. The contributions from metal object-induced artifacts can then be removed to generate a final, artifact-reduced image.

Abstract: Systems and methods for correcting images acquired with an imaging system for measurement bias from non-stationary noise in model observers are described. A biased detectability index is estimated from signal present and signal absent condition images and an estimate of the bias from non-stationary noise is also estimated. This bias is then removed from the detectability index to provide an assessment of detectability of the imaging system.

Abstract: Systems and methods for correcting images acquired with an imaging system for measurement bias from non-stationary noise in model observers are described. A biased detectability index is estimated from signal present and signal absent condition images and an estimate of the bias from non-stationary noise is also estimated. This bias is then removed from the detectability index to provide an assessment of detectability of the imaging system.