Methods and systems for generating dual-energy images from a single-energy imaging system based on anatomical segmentation

A system, comprising:

• one or more processors;
• and
• memory storing instructions executable by the one or more processors to:
• obtain an image at a first energy level, the image reconstructed from projection data acquired at a single peak energy level;
• identify a contrast phase of the image with a contrast phase classifier model;
• enter the image as input to a segmentation model trained to output an anatomy mask that identifies each tissue type in the image;
• generate a guide image from the image and the anatomy mask using a regression model, the regression model selected from among a plurality of regression models based on the contrast phase;
• enter the image and the guide image as input into an energy transformation model trained to output a transformed image at a second energy level, different than the first energy level, the energy transformation model selected from among a plurality of energy transformation models based on the contrast phase;
• and
• display a final transformed image and/or save the final transformed image in memory, wherein the final transformed image is the transformed image or is generated based on the transformed image.

A method, comprising:

• obtaining an image of a subject at a first energy level, the image reconstructed from projection data acquired with a single-energy computed tomography (CT) imaging system;
• identifying a first contrast phase and a second contrast phase in the image with a contrast phase classifier;
• entering the image as input to a segmentation model trained to output an anatomy mask that identifies each tissue type in the image;
• selecting a first regression model for the first contrast phase and a second regression model for the second contrast phase;
• generating a first guide image from the image and the anatomy mask using the first regression model;
• generating a second guide image from the image and the anatomy mask using the second regression model;
• selecting a first energy transformation model for the first contrast phase and a second energy transformation model for the second contrast phase;
• entering the image and the first guide image as input to the first energy transformation model and entering the image and the second guide image as input to the second energy transformation model, each of the first energy transformation model and the second energy transformation model trained to output a respective transformed image at a second energy level based on the image at the first energy level and the respective guide image;
• blending each respective transformed image to form a final transformed image at the second energy level;
• and
• displaying the final transformed image on a display device and/or saving the final transformed image in memory.