Low-Dose CT Image Enhancement Using Deep Learning
A. Demir M. M. A. Shames O. N. Gerek S. Ergin M. Fidan M. Koc M. B. Gulmezoglu A. Barkana C. Calisir
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.