The following publications are possibly variants of this publication:
- Tissue-specific sparse deconvolution for brain CT perfusionRuogu Fang, Haodi Jiang, JunZhou Huang. cmig, 46:64-72, 2015. [doi]
- Sparsity-Based Deconvolution of Low-Dose Perfusion CT Using Learned DictionariesRuogu Fang, Tsuhan Chen, Pina C. Sanelli. miccai 2012: 272-280 [doi]
- Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learningRuogu Fang, Tsuhan Chen, Pina C. Sanelli. mia, 17(4):417-428, 2013. [doi]
- Sparsity-based deconvolution of low-dose brain perfusion CT in subarachnoid hemorrhage patientsRuogu Fang, Tsuhan Chen, Pina C. Sanelli. isbi 2012: 872-875 [doi]
- Tensor Total-Variation Regularized Deconvolution for Efficient Low-Dose CT PerfusionRuogu Fang, Pina C. Sanelli, Shaoting Zhang, Tsuhan Chen. miccai 2014: 154-161 [doi]
- Robust Low-Dose CT Perfusion Deconvolution via Tensor Total-Variation RegularizationRuogu Fang, Shaoting Zhang, Tsuhan Chen, Pina C. Sanelli. tmi, 34(7):1533-1548, 2015. [doi]
- Efficient 4D Non-local Tensor Total-Variation for Low-Dose CT Perfusion DeconvolutionRuogu Fang, Ming Ni, JunZhou Huang, Qianmu Li, Tao Li. miccai 2016: 168-179 [doi]
- Direct estimation of permeability maps for low-dose CT perfusionRuogu Fang, Ajay Gupta, Pina C. Sanelli. isbi 2016: 739-742 [doi]
- STAR: Spatio-Temporal Architecture for Super-Resolution in Low-Dose CT PerfusionYao Xiao, Ajay Gupta, Pina C. Sanelli, Ruogu Fang. miccai 2017: 97-105 [doi]