Feasibility Study of CNN-based Super-Resolution Algorithm Applied to Low-Resolution CT Images

• 영문명: Feasibility Study of CNN-based Super-Resolution Algorithm Applied to Low-Resolution CT Images
• 발행기관: 한국인공지능학회
• 저자명: Doo Bin KIM Mi Jo LEE Joo Wan HONG
• 간행물 정보: 『인공지능연구』Vol.12 No. 1, 1~6쪽, 전체 6쪽
• 주제분류: 복합학 > 과학기술학
• 파일형태: PDF
• 발행일자: 2024.03.30

영문 초록

Recently, various techniques are being applied through the development of medical AI, and research has been conducted on the application of super-resolution AI models. In this study, evaluate the results of the application of the super-resolution AI model to brain CT as the basic data for future research. Acquiring CT images of the brain, algorithm for brain and bone windowing setting, and the resolution was downscaled to 5 types resolution image based on the original resolution image, and then upscaled to resolution to create an LR image and used for network input with the original imaging. The SRCNN model was applied to each of these images and analyzed using PSNR, SSIM, Loss. As a result of quantitative index analysis, the results were the best at 256×256, the brain and bone window setting PSNR were the same at 33.72, 35.2, and SSIM at 0.98 respectively, and the loss was 0.0004 and 0.0003, respectively, showing relatively excellent performance in the bone window setting CT image. The possibility of future studies aimed image quality and exposure dose is confirmed, and additional studies that need to be verified are also presented, which can be used as basic data for the above studies.

목차

1. Introduction

2. Related Work

3. Performance Evaluation and Results

4. Discussion

References

키워드

AI CNN Super-Resolution Windowing CT