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Related Concept Videos

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Updated: Dec 18, 2025

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TENDER: Tensor non-local deconvolution enabled radiation reduction in CT perfusion.

Ruogu Fang1, Ajay Gupta2, Junzhou Huang3

  • 1School of Computing and Information Sciences, Florida International University, USA.

Neurocomputing
|June 12, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces TENDER, a new method for low-dose CT perfusion imaging in stroke diagnosis. It improves accuracy and reduces radiation exposure, enhancing patient safety and diagnostic reliability for cerebrovascular diseases.

Keywords:
CT perfusionLow radiation doseStrokeTensor non-local deconvolutionTotal variation

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Area of Science:

  • Medical Imaging
  • Radiology
  • Biomedical Engineering

Background:

  • Stroke is a leading cause of death and disability globally.
  • Computed Tomography (CT) is crucial for acute stroke diagnosis.
  • CT perfusion imaging raises concerns due to radiation exposure.

Purpose of the Study:

  • To develop an efficient framework for low-radiation dose CT perfusion.
  • To maintain accuracy of hemodynamic parameters and diagnostic reliability.
  • To address limitations of existing Tensor Total Variation (TTV) methods.

Main Methods:

  • Proposed TENDER: a non-local tensor deconvolution framework.
  • Extended TTV using non-local spatio-temporal cubics for regularization.
  • Developed an efficient algorithm for reduced time complexity and speedy similarity computation.

Main Results:

  • TENDER effectively reduces radiation dose in CT perfusion.
  • Maintained accuracy of hemodynamic parameters and diagnostic reliability.
  • Demonstrated advantages over local TTV methods using clinical data.

Conclusions:

  • Non-local tensor deconvolution (TENDER) is advantageous for low-dose CT perfusion.
  • The method enhances patient safety by reducing radiation exposure.
  • TENDER improves diagnostic reliability for cerebrovascular disease assessment.