  The application of ionizing radiation for diagnostic imaging is common around
the globe. However, the process of imaging, itself, remains to be a relatively
hazardous operation. Therefore, it is preferable to use as low a dose of
ionizing radiation as possible, particularly in computed tomography (CT)
imaging systems, where multiple x-ray operations are performed for the
reconstruction of slices of body tissues. A popular method for radiation dose
reduction in CT imaging is known as the quarter-dose technique, which reduces
the x-ray dose but can cause a loss of image sharpness. Since CT image
reconstruction from directional x-rays is a nonlinear process, it is
analytically difficult to correct the effect of dose reduction on image
quality. Recent and popular deep-learning approaches provide an intriguing
possibility of image enhancement for low-dose artifacts. Some recent works
propose combinations of multiple deep-learning and classical methods for this
purpose, which over-complicate the process. However, it is observed here that
the straight utilization of the well-known U-NET provides very successful
results for the correction of low-dose artifacts. Blind tests with actual
radiologists reveal that the U-NET enhanced quarter-dose CT images not only
provide an immense visual improvement over the low-dose versions, but also
become diagnostically preferable images, even when compared to their full-dose
CT versions.
