A method and system for transforming low-quality projection data into higher quality projection data, using of a machine learning model. Regions are extracted from an input projection image acquired, for example, at a reduced x-ray radiation dose (lower-dose), and pixel values in the region are entered into the machine learning model as input. The output of the machine learning model is a region that corresponds to the input region. The output information is arranged to form an output high-quality projection image. A reconstruction algorithm reconstructs high-quality tomographic images from the output high-quality projection images. The machine learning model is trained with matched pairs of projection images, namely, input lower-quality (lower-dose) projection images together with corresponding desired higher-quality (higher-dose) projection images. Through the training, the machine learning model learns to transform lower-quality (lower-dose) projection images to higher-quality (higher-dose) projection images. Once trained, the trained machine learning model does not require the higher-quality (higher-dose) projection images anymore. When a new lower-quality (low radiation dose) projection image is entered, the trained machine learning model would output a region similar to its desired region, in other words, it would output simulated high-quality (high-dose) projection images where noise and artifacts due to low radiation dose are substantially reduced, i.e., a higher image quality. The reconstruction algorithm reconstructs simulated high-quality (high-dose) tomographic images from the output high-quality (high-dose) projection images. With the simulated high-quality (high-dose) tomographic images, the detectability of lesions and clinically important findings can be improved.
TRANSFORMING PROJECTION DATA IN TOMOGRAPHY BY MEANS OF MACHINE LEARNING