Abstract

Acute brain diseases such as acute strokes and transit ischemic attacks are the leading causes of mortality and morbidity worldwide, responsible for 9% of total death every year. ‘Time is brain’ is a widely accepted concept in acute cerebrovascular disease treatment. Efficient and accurate com- putational framework for hemodynamic parameters estimation can save critical time for thrombolytic therapy. Meanwhile the high level of accumulated radiation dosage due to continuous image acquisition in CT perfusion (CTP) raised concerns on patient safety and public health. However, low-radiation leads to increased noise and artifacts which require more sophisticated and time-consuming algorithms for robust estimation. In this paper, we focus on developing a robust and efficient framework to accurately estimate the perfusion parameters at low radiation dosage. Specifically, we present a tensor total-variation (TTV) technique which fuses the spatial correlation of the vascular structure and the temporal continuation of the blood signal flow. An efficient algorithm is proposed to find the solution with fast convergence and reduced computational complexity. Extensive evaluations are carried out in terms of sensitivity to noise levels, estimation accuracy, contrast preservation, and performed on digital perfusion phantom estimation, as well as in-vivo clinical subjects. Our framework reduces the necessary radiation dose to only 8% of the original level and outperforms the state-of-art algorithms with peak signal-to-noise ratio improved by 32%. It reduces the oscillation in the residue functions, corrects over- estimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT), and maintains the distinction between the deficit and normal regions.

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BibTeX

@article{fang2015robust, 
author={Fang, R. and Zhang, S. and Chen, T. and Sanelli, P.}, 
journal={IEEE Transactions on Medical Imaging}, 
title={Robust Low-dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization},  
year={2015}, 
month={July}, 
volume={34}, 
number={7}, 
pages={1533-1548}, 
keywords={Computed tomography;Deconvolution;Estimation;Image reconstruction;Noise;Phantoms;Tensile stress;Computed tomography perfusion;deconvolution;low-dose;radiation dose safety;regularization;tensor total variation}, 
doi={10.1109/TMI.2015.2405015}, 
ISSN={0278-0062},}

@incollection{fang2014tensor,
  title={Tensor total-variation regularized deconvolution for efficient low-dose ct perfusion},
  author={Fang, Ruogu and Sanelli, Pina C and Zhang, Shaoting and Chen, Tsuhan},
  booktitle={Medical Image Computing and Computer-Assisted Intervention--MICCAI 2014},
  pages={154--161},
  year={2014},
  publisher={Springer}
}

Publications on Tensor Total Variation

Robust Low-dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization [online] [PDF]
Ruogu Fang, Shaoting Zhang, Tsuhan Chen, Pina C. Sanelli.
TMI, IEEE Transaction on Medical Imaging, vol.34, no.7, pp.1533-1548, July 2015.

Tensor Total-Variation Regularized Deconvolution for Efficient Low-Dose CT Perfusion
[PDF] [Bibtex] MICCAI Student Travel Award
Ruogu Fang, Pina Sanelli, Shaoting Zhang, Tsuhan Chen.
MICCAI'14, The 17th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, Boston, USA, September, 2014.

Anisotropic Tensor Total Variation Regularization For Low Dose Low CT Perfusion Deconvolution [PDF] [Online] [Proceeding] [Bibtex]
Ruogu Fang, Tsuhan Chen, Pina C. Sanelli.
MICCAI'14, The 17th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, Workshop on Sparsity Techniques in Medical Imaging, Boston, USA, September, 2014.