Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep neural networks-based denoising models for CT imaging and their
  efficacy   Most of the Deep Neural Networks (DNNs) based CT image denoising literature
shows that DNNs outperform traditional iterative methods in terms of metrics
such as the RMSE, the PSNR and the SSIM. In many instances, using the same
metrics, the DNN results from low-dose inputs are also shown to be comparable
to their high-dose counterparts. However, these metrics do not reveal if the
DNN results preserve the visibility of subtle lesions or if they alter the CT
image properties such as the noise texture. Accordingly, in this work, we seek
to examine the image quality of the DNN results from a holistic viewpoint for
low-dose CT image denoising. First, we build a library of advanced DNN
denoising architectures. This library is comprised of denoising architectures
such as the DnCNN, U-Net, Red-Net, GAN, etc. Next, each network is modeled, as
well as trained, such that it yields its best performance in terms of the PSNR
and SSIM. As such, data inputs (e.g. training patch-size, reconstruction
kernel) and numeric-optimizer inputs (e.g. minibatch size, learning rate, loss
function) are accordingly tuned. Finally, outputs from thus trained networks
are further subjected to a series of CT bench testing metrics such as the
contrast-dependent MTF, the NPS and the HU accuracy. These metrics are employed
to perform a more nuanced study of the resolution of the DNN outputs'
low-contrast features, their noise textures, and their CT number accuracy to
better understand the impact each DNN algorithm has on these underlying
attributes of image quality.

12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th