Denoising Low-Dose CT Images using Multi-frame techniques
Date
2022-05-20
Authors
Journal Title
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ORCID
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Degree Level
Masters
Abstract
This study examines potential methods of achieving a reduction in X-ray radiation dose of Computer
Tomography (CT) using multi-frame low-dose CT images. Even though a single-frame low-dose CT image
is not very diagnostically useful due to excessive noise, we have found that by using multi-frame low-dose
CT images we can denoise these low-dose CT images quite significantly at lower radiation dose. We have
proposed two approaches leveraging these multi-frame low-dose CT denoising techniques.
In our first method, we proposed a blind source separation (BSS) based CT image method using a multiframe low-dose image sequence. By using BSS technique, we estimated the independent image component and
noise components from the image sequences. The extracted image component then is further donoised using
a nonlocal groupwise denoiser named BM3D that used the mean standard deviation of the noise components.
We have also proposed an extension of this method using a window splitting technique.
In our second method, we leveraged the power of deep learning to introduce a collaborative technique
to train multiple Noise2Noise generators simultaneously and learn the image representation from LDCT
images. We presented three models using this Collaborative Network (CN) principle employing two generators
(CN2G), three generators (CN3G), and hybrid three generators (HCN3G) consisting of BSS denoiser with
one of the CN generators. The CN3G model showed better performance than the CN2G model in terms of
denoised image quality at the expense of an additional LDCT image. The HCN3G model took the advantages
of both these models by managing to train three collaborative generators using only two LDCT images by
leveraging our first proposed method using blind source separation (BSS) and block matching 3-D (BM3D)
filter.
By using these multi-frame techniques, we can reduce the radiation dosage quite significantly without
losing significant image details, especially for low-contrast areas. Amongst our all methods, the HCN3G
model performs the best in terms of PSNR, SSIM, and material noise characteristics, while CN2G and CN3G
perform better in terms of contrast difference. In HCN3G model, we have combined two of our methods in a
single technique. In addition, we have introduced Collaborative Network (CN) and collaborative loss terms
in the L2 losses calculation in our second method which is a significant contribution of this research study.
Description
Keywords
Multiframe, Low-Dose CT,
Citation
Degree
Master of Science (M.Sc.)
Department
Biomedical Engineering
Program
Biomedical Engineering