Contrast-Medium Anisotropy-Aware Tensor Total Variation Model for Robust Cerebral Perfusion CT Reconstruction With Low-Dose Scans

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Contrast-Medium Anisotropy-Aware Tensor Total Variation Model for Robust Cerebral Perfusion CT Reconstruction With Low-Dose Scans

Yuanke Zhang; Jiangjun Peng; Dong Zeng; Qi Xie; Sui Li; Zhaoying Bian; Yongbo Wang; Yong Zhang; Qian Zhao; Hao Zhang; Zhengrong Liang; Hongbing Lu; Deyu Meng; Jianhua Ma

Perfusion computed tomography (PCT) is critical in detecting cerebral ischemic lesions. PCT examination with lowdose scans can effectively reduce radiation exposure to patients at the cost of degraded images with severe noise, and artifacts. Tensor total variation (TTV) models are powerful tools that can encode the regional continuous structures underlying a PCT object. In a TTV model, the sparsity structures of the contrast-medium concentration (CMC) across PCT frames are assumed to be isotropic with identical, and independent distribution. However, this assumption is inconsistent with practical PCT tasks wherein the sparsity has evident variations, and correlations. Such modeling deviation hampers the performance of TTV-based PCT reconstructions. To address this issue, we developed a novel contrast-medium anisotropyaware tensor total variation (CMAA-TTV) model to describe the intrinsic anisotropy sparsity of the CMC in PCT imaging tasks. Instead of directly on the difference matrices, the CMAA-TTV model characterizes sparsity on a low-rank subspace of the difference matrices which are calculated from the input data adaptively, thus naturally encoding the intrinsic variant, and correlated anisotropy sparsity structures of the CMC. We further proposed a robust... 

SPS on Twitter

  • NEW SPS WEBINAR: On Tuesday, 13 December, join Dr. Qian Huang for "Deep Learning for All-in-Focus Imaging" - regist…
  • Join the SPS Membership Drive on Monday, 12 December, when SPS members, potential members, and the greater signal p…
  • The fundraising deadline to meet our 30 unique donations of US$10 or more is tonight — increase your impact for sig…
  • Happy ! Celebrate this global day of generosity and community action with the IEEE Foundation and…
  • The SPS Biomedical Imaging and Signal Processing Technical Committee Webinar Series continues on Tuesday, 6 Decembe…

SPS Videos

Signal Processing in Home Assistants


Multimedia Forensics

Careers in Signal Processing             


Under the Radar