Apparatus and method for dual-energy computed tomography (CT) image reconstruction using sparse kVp-switching and deep learning

An apparatus, comprising:

• processing circuitry configured to train an artifact-mitigating neural network having two channels by
• initializing the artifact-mitigating neural network,
• obtaining input images including pairs of sparse-view reconstructed images that have complementary streak artifacts, the sparse-view reconstructed images representing reconstructed images from projection data of a sparse-view kilo-voltage peak (kVp)-switching computed tomography (CT) scan,
• obtaining target images including pairs of images corresponding to the sparse-view reconstructed images except with the streak artifacts having been mitigated, and
• iteratively updating network coefficients to optimize a loss function representing agreement between the target images and output images resulting from the input images applied to the artifact-mitigating neural network.

A method of mitigating artifacts in computed tomography (CT) images from sparse-view kilo-voltage peak (kVp)-switching CT scans, comprising:

• obtaining projection data representing an intensity of X-ray radiation at a plurality of detector elements, the projection data representing first sparse-view data acquired using a first voltage applied to an X-ray source and second sparse-view data acquired using a second voltage applied to the X-ray source, the second voltage being greater than the first voltage and the first sparse-view data being acquired at views that are different from views at which the second sparse-view data is acquired,
• reconstructing, from the first sparse-view data, a first low-energy image,
• reconstructing, from the second sparse-view data, a first high-energy image,
• acquiring an artifact-mitigating neural network that has two channels, one input channel for a low-energy image and another input channel for a high-energy image, and the artifact-mitigating neural network having been trained using a training dataset that includes input images that are respective pairs of sparse-view reconstructed images that have complementary streak artifacts and corresponding target images in which the streak artifacts are mitigated, and
• applying the first low-energy image and the first high-energy image to the artifact-mitigating neural network to generate artifact-mitigated images.