Kvp-switching sparse-view ct images restoration with an edge prior map

A computed tomography (CT) apparatus, comprising:

• processing circuitry configured to
• receive dual-energy tomography data acquired by imaging an object using a CT apparatus, the dual-energy tomography data including a first set of tomography data at a first energy level and a second set of tomography data at a second energy level, the second energy level being greater than the first energy level;
• generate, based on the dual-energy tomography data, a first sparse view projection of the first set of tomography data at the first energy level and a second sparse view projection of the second set of tomography data at the second energy level;
• generate, based on the dual-energy tomography data, a texture map;
• and
• generate a predicted image at the first energy level by applying the first sparse view projection and the generated texture map to a trained machine learning model, wherein
• the trained machine learning model was previously trained to generate an image using a set of training data comprising a training sparse view projection of single-energy tomography data and a training texture map generated from the single-energy tomography data.

A non-transitory computer-readable storage medium for storing computer readable instructions that, when executed by a computer, cause the computer to perform a method, the method comprising:

• receiving dual-energy tomography data acquired by imaging an object using a CT apparatus, the dual-energy tomography data including a first set of tomography data at a first energy level and a second set of tomography data at a second energy level, the second energy level being greater than the first energy level;
• generating, based on the dual-energy tomography data, a first sparse view projection of the first set of tomography data at the first energy level and a second sparse view projection of the second set of tomography data at the second energy level;
• generating, based on the dual-energy tomography data, a texture map;
• and
• generating a predicted image at the first energy level by applying the first sparse view projection and the generated texture map to a trained machine learning model, wherein
• the trained machine learning model was previously trained to generate an image using a set of training data comprising a training sparse view projection of single-energy tomography data and a training texture map generated from the single-energy tomography data.