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Direct estimation of permeability maps for low-dose CT perfusion., , and . ISBI, page 739-742. IEEE, (2016)Improving low-dose blood-brain barrier permeability quantification using sparse high-dose induced prior for Patlak model., , , and . Medical Image Analysis, 18 (6): 866-880 (2014)Sparsity-based deconvolution of low-dose brain perfusion CT in subarachnoid hemorrhage patients., , and . ISBI, page 872-875. IEEE, (2012)Deep Spatial-Temporal Convolutional Neural Networks for Medical Image Restoration., , , , , , , and . Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics, Springer, (2019)Sparsity techniques in medical imaging., , , , and . Comp. Med. Imag. and Graph., (2015)Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning., , and . Medical Image Analysis, 17 (4): 417-428 (2013)Sparsity-Based Deconvolution of Low-Dose Perfusion CT Using Learned Dictionaries., , and . MICCAI (1), volume 7510 of Lecture Notes in Computer Science, page 272-280. Springer, (2012)STAR: Spatio-Temporal Architecture for Super-Resolution in Low-Dose CT Perfusion., , , and . MLMI@MICCAI, volume 10541 of Lecture Notes in Computer Science, page 97-105. Springer, (2017)Robust Low-Dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization., , , and . IEEE Trans. Med. Imaging, 34 (7): 1533-1548 (2015)Deep Evolutionary Networks with Expedited Genetic Algorithms for Medical Image Denoising., , , , , and . Medical Image Analysis, (2019)