Computed tomography perfusion (CTP) is an important functional imaging modality in the evaluation of cerebrovascular diseases, particularly in acute stroke and vasospasm. However, the post-processed parametric maps of blood flow tend to be noisy, especially in low-dose CTP, due to the noisy contrast enhancement profile and the oscillatory nature of the results generated by the current computational methods. In this paper, we propose a robust sparse perfusion deconvolution method (SPD) to estimate cerebral blood flow in CTP performed at low radiation dose. We first build a dictionary from high-dose perfusion maps using online dictionary learning and then perform deconvolution-based hemodynamic parameters estimation on the low-dose CTP data. Our method is validated on clinical data of patients with normal and pathological CBF maps. The results show that we achieve superior performance than existing methods, and potentially improve the differentiation between normal and ischemic tissue in the brain.


  title = {Towards robust deconvolution of low-dose perfusion CT:Sparse perfusion deconvolution using online dictionary learning},
  author = {Fang, Ruogu and Chen, Tsuhan and Sanelli, Pina C},
  journal = {Medical image analysis},
  volume = {17},
  number = {4},
  pages = {417-428},
  year = {2013},
  publisher = {Elsevier}

Publications on Robust CT Perfusion

Ruogu Fang, Tsuhan Chen, Pina Sanelli: Towards Robust Deconvolution of Low-Dose Perfusion CT: Sparse Perfusion Deconvolution Using Online Dictionary Learning. Medical Image Analysis 17(4): 417-428 (2013) (Top 25 hottest articles in Medical Image Analysis in 2013 April to June) [Link] )

Ruogu Fang, Tsuhan Chen, Pina Sanelli: Tissue-Specific Sparse Deconvolution for Low-Dose CT Perfusion. International Conference on Medical Image Computing and Computer Assisted Intervention. 2013. Lecture Notes in Computer Science.

Ruogu Fang, Tsuhan Chen, Pina Sanelli: Sparsity-Based Deconvolution of Low-Dose Perfusion CT Using Learned Dictionaries. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. Lecture Notes in Computer Science Volume 7510, 2012, pp 272-280 (MICCAI)

Ruogu Fang, Tsuhan Chen, Pina Sanelli. Sparsity-Based Deconvolution Of Low-Dose Brain Perfusion CT In Subarachnoid Hemorrhage Patients. In Proceeding of the 9th International Symposium on Biomedical Imaging, pp. 872-875, 2012. (ISBI) (Oral presentation)

Ruogu Fang, Ashish Raj, Tsuhan Chen, Pina C. Sanelli. Radiation dose reduction in computed tomography perfusion using spatial-temporal Bayesian methods. In Proceedings of SPIE Medical Imaging, Volume 8313, Paper #831345, 2012. (SPIE)