Effectiveness of Noise Reduction in LDCT Images Based on SRCNN

• 발행기관: 한국인공지능학회
• 저자명: Doo Bin KIM Hyun Mee PARK Sang Hoon JOON Joo Wan HONG
• 간행물 정보: 『인공지능연구』Vol.12 No. 4, 21~26쪽, 전체 6쪽
• 주제분류: 복합학 > 과학기술학
• 파일형태: PDF
• 발행일자: 2024.12.31

국문 초록

This study aims to evaluate the performance of noise reduction in LDCT images using an SRCNN based AI model. Using the Lungman phantom, images of effective mAs 72 recommended by AAPM and effective mAs 709 without using the AEC function were acquired. SRCNN model input image used a GT, label image and GT image was used as a low-resolution image. Image evaluation was conducted in the lung apex, middle level lung, and carina of trachea regions, and PSNR, SSIM, SSIM error map, SNR, MSE, and RMSE were used as evaluation indices are based on label image. Lung apex results showed increase of 19.52, 29.69 and 23.8%, and decreased of 71.37, 46.43% respectively. Middle level lung results showed increase of 20.99, 20.0 and 26.26%, and decreased of 72.67, 47.72% respectively. Carina of trachea results showed increase of 22.05, 32.31 and 28.18%, and decreased of 73.93, 48.93% respectively. Image evaluation results were improvement in image quality due to noise reduction was confirmed using the SRCNN based AI model. Therefore, confirmed that applying the SRCNN to LDCT images can improve image quality by reducing noise, and it is considered that AI based post processing will be useful for CT images without AI.

목차

1. Introduction

2. Research Methods and Materials

3. Results

4. Discussion

5. Conclusions

References

키워드

LDCT SRCNN Noise Denoising PSNR