title: Sparsity-based deconvolution of low-dose perfusion CT using learned dictionaries.
abstract: Computational tomography perfusion (CTP) is an important functional imaging modality in the evaluation of cerebrovascular diseases, such as 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 novel sparsity-base deconvolution method to estimate cerebral blood flow in CTP performed at low-dose. We first built an overcomplete dictionary from high-dose perfusion maps and then performed deconvolution-based hemodynamic parameters estimation on the low-dose CTP data. Our method is validated on a clinical dataset of ischemic patients. The results show that we achieve superior performance than existing methods, and potentially improve the differentiation between normal and ischemic tissue in the brain.
authors.affiliation: Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA.
authors.forename: Ruogu
authors.initials: R
authors.lastname: Fang
authors.forename: Tsuhan
authors.initials: T
authors.lastname: Chen
authors.forename: Pina C
authors.initials: PC
authors.lastname: Sanelli
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D002560:Cerebrovascular Circulation; D003952:Diagnostic Imaging / Q000379:methods; D006801:Humans; D007511:Ischemia / Q000473:pathology; D009991:Oscillometry / Q000379:methods; D010477:Perfusion; D011871:Radiology / Q000379:methods; D012039:Regional Blood Flow / Q000502:physiology; D012984:Software; D020521:Stroke / Q000473:pathology; D014057:Tomography, X-Ray Computed / Q000379:methods*