Image enhancement using convolutional neural network

One common interest in radiography is producing radiographs with as low as possible radiation exposures to patients. In clinical practices, radiation exposure factors are preset for optimal image qualities to avoid underexposures which will lead to repeating examinations hence increasing radiation exposures to patients. Underexposed radiographs mainly suffer from Poisson noises due to inadequate photons reaching the detector. Radiographs are often degraded by scatter radiations and the severity of image quality degradations depends on the amount of scatters reaching the detectors. In this work, a convolutional neural network (CNN) algorithm was used to predict scatters and reduce Poisson noises. Monte Carlo simulation images and an adult abdomen radiograph were used to evaluate this CNN algorithm. The radiograph was underexposed by 60% radiation exposures. The simulation images were produced with one-thousandth of a typical clinical exposure. The results show that Poisson noises are successfully reduced, and image contrast and details are improved. After the underexposed radiograph which is not useful for making a confident diagnosis was processed using the CNN algorithm, the contrast and details in the radiograph were greatly improved and are adequate for making a diagnosis, therefore a 60% radiation dose reduction was achieved. This work shows that radiograph qualities can be improved by reducing scatters and Poisson noises. A potential application of this CNN algorithm is for patient radiation dose reductions by reducing current preset optimal radiation exposures and then using this algorithm to enhance the image contrast and details by reducing both scatters and Poisson noises.