BACKGROUND AND OBJECTIVE: X-ray computed tomography (CT) plays an important role in modern medical science. Human health problems caused by CT radiation have attracted the attention of the academic community widely. Reducing radiation dose results in a deterioration in image quality and further affects doctor's diagnosis. Therefore, this paper introduces a new denoise method for low dose CT (LDCT) images, called two stage residual convolutional neural network (TS-RCNN). METHODS: There are two important parts with respect to our network. 1) The first stage RCNN is proposed for texture denoising via the stationary wavelet transform (SWT) and the perceptual loss. Specifically, SWT is performed on each normal dose CT (NDCT) image and generated four wavelet images are considered as the labels. 2) The second stage RCNN is established for structure enhancement via the average NDCT model on the basis of the first network's result. Finally, the denoised CT image is obtained via inverse SWT. RESULTS: Our proposed TS-RCNN is trained on three groups of simulated LDCT images in 1123 images per group and evaluated on 129 simulated LDCT images for each group. Besides, to demonstrate the clinical application of TS-RCNN, we also test our method on the 2016 Low Dose CT Grand Challenge dataset. Quantitative results show that TS-RCNN almost achieves the best results in terms of MSE, SSIM and PSNR compared to other methods. CONCLUSIONS: The experimental results and comparisons demonstrate that TS-RCNN not only preserves more texture information, but also enhances structural information on LDCT images.
BACKGROUND AND OBJECTIVE: X-ray computed tomography (CT) plays an important role in modern medical science. Human health problems caused by CT radiation have attracted the attention of the academic community widely. Reducing radiation dose results in a deterioration in image quality and further affects doctor's diagnosis. Therefore, this paper introduces a new denoise method for low dose CT (LDCT) images, called two stage residual convolutional neural network (TS-RCNN). METHODS: There are two important parts with respect to our network. 1) The first stage RCNN is proposed for texture denoising via the stationary wavelet transform (SWT) and the perceptual loss. Specifically, SWT is performed on each normal dose CT (NDCT) image and generated four wavelet images are considered as the labels. 2) The second stage RCNN is established for structure enhancement via the average NDCT model on the basis of the first network's result. Finally, the denoised CT image is obtained via inverse SWT. RESULTS: Our proposed TS-RCNN is trained on three groups of simulated LDCT images in 1123 images per group and evaluated on 129 simulated LDCT images for each group. Besides, to demonstrate the clinical application of TS-RCNN, we also test our method on the 2016 Low Dose CT Grand Challenge dataset. Quantitative results show that TS-RCNN almost achieves the best results in terms of MSE, SSIM and PSNR compared to other methods. CONCLUSIONS: The experimental results and comparisons demonstrate that TS-RCNN not only preserves more texture information, but also enhances structural information on LDCT images.
Authors: K A Saneera Hemantha Kulathilake; Nor Aniza Abdullah; A M Randitha Ravimal Bandara; Khin Wee Lai Journal: J Healthc Eng Date: 2021-09-10 Impact factor: 2.682