Literature DB >> 22333988

Ray contribution masks for structure adaptive sinogram filtering.

Michael Balda1, Joachim Hornegger, Bjoern Heismann.   

Abstract

The patient dose in computed tomography (CT) imaging is linked to measurement noise. Various noise-reduction techniques have been developed that adapt structure preserving filters like anisotropic diffusion or bilateral filters to CT noise properties. We introduce a structure adaptive sinogram (SAS) filter that incorporates the specific properties of the CT measurement process. It uses a point-based forward projector to generate a local structure representation called ray contribution mask (RCM). The similarities between neighboring RCMs are used in an enhanced variant of the bilateral filtering concept, where the photometric similarity is replaced with the structural similarity. We evaluate the performance in four different scenarios: The robustness against reconstruction artifacts is demonstrated by a scan of a high-resolution-phantom. Without changing the modulation transfer function (MTF) nor introducing artifacts, the SAS filter reduces the noise level by 13.6%. The image sharpness and noise reduction capabilities are visually assessed on in vivo patient scans and quantitatively evaluated on a simulated phantom. Unlike a standard bilateral filter, the SAS filter preserves edge information and high-frequency components of organ textures well. It shows a homogeneous noise reduction behavior throughout the whole frequency range. The last scenario uses a simulated edge phantom to estimate the filter MTF for various contrasts: the noise reduction for the simple edge phantom exceeds 80%. For low contrasts at 55 Hounsfield units (HU), the mid-frequency range is slightly attenuated, at higher contrasts of approximately 100 HU and above, the MTF is fully preserved.

Entities:  

Mesh:

Year:  2012        PMID: 22333988     DOI: 10.1109/TMI.2012.2187213

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  12 in total

1.  Low-dose CT via convolutional neural network.

Authors:  Hu Chen; Yi Zhang; Weihua Zhang; Peixi Liao; Ke Li; Jiliu Zhou; Ge Wang
Journal:  Biomed Opt Express       Date:  2017-01-09       Impact factor: 3.732

2.  Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network.

Authors:  Hu Chen; Yi Zhang; Mannudeep K Kalra; Feng Lin; Yang Chen; Peixi Liao; Jiliu Zhou; Ge Wang
Journal:  IEEE Trans Med Imaging       Date:  2017-06-13       Impact factor: 10.048

3.  Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network.

Authors:  Xin Yi; Paul Babyn
Journal:  J Digit Imaging       Date:  2018-10       Impact factor: 4.056

4.  The synthesis of high-energy CT images from low-energy CT images using an improved cycle generative adversarial network.

Authors:  Haojie Zhou; Xinfeng Liu; Haiyan Wang; Qihang Chen; Rongpin Wang; Zhi-Feng Pang; Yong Zhang; Zhanli Hu
Journal:  Quant Imaging Med Surg       Date:  2022-01

5.  Low-dose computed tomography image reconstruction via a multistage convolutional neural network with autoencoder perceptual loss network.

Authors:  Qing Li; Saize Li; Runrui Li; Wei Wu; Yunyun Dong; Juanjuan Zhao; Yan Qiang; Rukhma Aftab
Journal:  Quant Imaging Med Surg       Date:  2022-03

6.  [Low-dose helical CT projection data restoration using noise estimation].

Authors:  F He; Y Wang; X Tao; M Zhu; Z Hong; Z Bian; J Ma
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2022-06-20

7.  A selective kernel-based cycle-consistent generative adversarial network for unpaired low-dose CT denoising.

Authors:  Chaoqun Tan; Mingming Yang; Zhisheng You; Hu Chen; Yi Zhang
Journal:  Precis Clin Med       Date:  2022-05-25

8.  Quadratic Autoencoder (Q-AE) for Low-Dose CT Denoising.

Authors:  Fenglei Fan; Hongming Shan; Mannudeep K Kalra; Ramandeep Singh; Guhan Qian; Matthew Getzin; Yueyang Teng; Juergen Hahn; Ge Wang
Journal:  IEEE Trans Med Imaging       Date:  2019-12-31       Impact factor: 10.048

9.  Stacked competitive networks for noise reduction in low-dose CT.

Authors:  Wenchao Du; Hu Chen; Zhihong Wu; Huaiqiang Sun; Peixi Liao; Yi Zhang
Journal:  PLoS One       Date:  2017-12-21       Impact factor: 3.240

10.  A Subband-Specific Deconvolution Model for MTF Improvement in CT.

Authors:  Seokmin Han; Kihwan Choi; Sang Wook Yoo
Journal:  J Healthc Eng       Date:  2017-10-25       Impact factor: 2.682

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.