Sparsity-based deconvolution of low-dose brain perfusion CT in subarachnoid hemorrhage patients. - Ruogu Fang; Tsuhan Chen; Pina C. Sanelli (ISBI, 2012)

Sparsity-Based Deconvolution of Low-Dose Perfusion CT Using Learned Dictionaries. - Ruogu Fang; Tsuhan Chen; Pina C. Sanelli (MICCAI (1), 2012)

Tissue-Specific Sparse Deconvolution for Low-Dose CT Perfusion. - Ruogu Fang; Tsuhan Chen; Pina C. Sanelli (MICCAI (1), 2013)

Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning. - Ruogu Fang; Tsuhan Chen; Pina C. Sanelli (Medical Image Anal., 2013)

Tensor Total-Variation Regularized Deconvolution for Efficient Low-Dose CT Perfusion. - Ruogu Fang; Pina C. Sanelli; Shaoting Zhang 001; Tsuhan Chen (MICCAI (1), 2014)

Improving low-dose blood-brain barrier permeability quantification using sparse high-dose induced prior for Patlak model. - Ruogu Fang; Kolbeinn Karlsson; Tsuhan Chen; Pina C. Sanelli (Medical Image Anal., 2014)

Sparsity techniques in medical imaging. - Ruogu Fang; Tsuhan Chen; Dimitris N. Metaxas; Pina C. Sanelli; Shaoting Zhang 001 (Comput. Medical Imaging Graph., 2015)

Robust Low-Dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization. - Ruogu Fang; Shaoting Zhang 001; Tsuhan Chen; Pina C. Sanelli (IEEE Trans. Medical Imaging, 2015)

Direct estimation of permeability maps for low-dose CT perfusion. - Ruogu Fang; Ajay Gupta; Pina C. Sanelli (ISBI, 2016)

STAR: Spatio-Temporal Architecture for Super-Resolution in Low-Dose CT Perfusion. - Yao Xiao; Ajay Gupta; Pina C. Sanelli; Ruogu Fang (MLMI@MICCAI, 2017)

TENDER: Tensor non-local deconvolution enabled radiation reduction in CT perfusion. - Ruogu Fang; Ajay Gupta; Junzhou Huang; Pina C. Sanelli (Neurocomputing, 2017)

Deep Spatial-Temporal Convolutional Neural Networks for Medical Image Restoration. - Yao Xiao; Skylar E. Stolte; Peng Liu 037; Yun Liang 012; Pina C. Sanelli; Ajay Gupta; Jana Ivanidze; Ruogu Fang (Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics, 2019)

Deep Evolutionary Networks with Expedited Genetic Algorithms for Medical Image Denoising. - Peng Liu 037; Mohammad D. El Basha; Yangjunyi Li; Yao Xiao; Pina C. Sanelli; Ruogu Fang (Medical Image Anal., 2019)

Quantification of Tomographic Patterns associated with COVID-19 from Chest CT. - Shikha Chaganti; Abishek Balachandran; Guillaume Chabin; Stuart Cohen; Thomas Flohr 001; Bogdan Georgescu; Philippe Grenier; Sasa Grbic; Siqi Liu 001; François Mellot; Nicolas Murray; Savvas Nicolaou; et al. (CoRR, 2020)

Machine Learning Automatically Detects COVID-19 using Chest CTs in a Large Multicenter Cohort. - Bogdan Georgescu; Shikha Chaganti; Gorka Bastarrika Aleman; Eduardo Jose Mortani Barbosa Jr.; Jordi Broncano Cabrero; Guillaume Chabin; Thomas Flohr 001; Philippe Grenier; Sasa Grbic; Nakul Gupta; François Mellot; Savvas Nicolaou; et al. (CoRR, 2020)

Brain midline shift detection and quantification by a cascaded deep network pipeline on non-contrast computed tomography scans. - Nguyen P. Nguyen; Youngjin Yoo; Andrei Chekkoury; Eva Eibenberger; Thomas J. Re; Jyotipriya Das; Abishek Balachandran; Yvonne W. Lui; Pina C. Sanelli; Thomas J. Schroeppel; Uttam Bodanapally; Savvas Nicolaou; et al. (ICCVW, 2021)

Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan. - Tran Anh Tuan 002; Tal Ze'evi; Stefan P. Haider; Gaby Abou Karam; Elisa R. Berson; Hishan Tharmaseelan; Adnan I. Qureshi; Pina C. Sanelli; David J. Werring; Ajay Malhotra; Nils H. Petersen; Adam DeHavenon; et al. (npj Digit. Medicine, 2024)

A Non-contrast Head CT Foundation Model for Comprehensive Neuro-Trauma Triage. - Youngjin Yoo; Bogdan Georgescu; Yanbo Zhang; Sasa Grbic; Han Liu; Gabriela D. Aldea; Thomas J. Re; Jyotipriya Das; Poikavila Ullaskrishnan; Eva Eibenberger; Andrei Chekkoury; Uttam Bodanapally; et al. (CoRR, 2025)

Physiology-Informed Generative Multi-Task Network for Contrast-Free CT Perfusion. - Wasif Khan; Kyle B. See; Simon Kato; Ziqian Huang; Amy Lazarte; Kyle Douglas; Xiangyang Lou; Teng J. Peng; Dhanashree A. Rajderkar; John H. Rees; Pina C. Sanelli; Amita Singh; et al. (CoRR, 2025)