Low-Dose CT Image Enhancement Using Deep Learning

• URL: http://arxiv.org/abs/2310.20265v1
• Date: Tue, 31 Oct 2023 08:34:33 GMT
• Title: Low-Dose CT Image Enhancement Using Deep Learning
• Authors: A.Demir, M.M.A.Shames, O.N.Gerek, S.Ergin, M.Fidan, M.Koc, M.B.Gulmezoglu, A.Barkana, C.Calisir
• Abstract summary: It is preferable to use as low a dose of ionizing radiation as possible, particularly in computed tomography (CT) imaging systems. A popular method for radiation dose reduction in CT imaging is known as the quarter-dose technique. Recent and popular deep-learning approaches provide an intriguing possibility of image enhancement for low-dose artifacts.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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