ArticleMedical image analysis2013
Towards robust deconvolution of low-dose perfusion CT: sparse perfusion deconvolution using online dictionary learning.
Article in Medical image analysis, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 55 citations in OpenAlex.
- Photon-counting CT for dynamic lung perfusion: validation of a low-dose protocol in a porcine lung transplantation model.European radiology experimental · 2026Article
- A Novel Self-Supervised Learning-Based Method for Dynamic CT Brain Perfusion Imaging.Journal of imaging informatics in medicine · 2025Article
- Artificial Intelligence as A Complementary Tool for Clincal Decision-Making in Stroke and Epilepsy.Brain sciences · 2024Review
- A Machine Learning Approach for Classifying Ischemic Stroke Onset Time From Imaging.IEEE transactions on medical imaging · 2019Article
- Predicting ischemic stroke tissue fate using a deep convolutional neural network on source magnetic resonance perfusion images.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Article
- Low-Dose Dynamic Cerebral Perfusion Computed Tomography Reconstruction via Kronecker-Basis-Representation Tensor Sparsity Regularization.IEEE transactions on medical imaging · 2017Article
- TENDER: Tensor non-local deconvolution enabled radiation reduction in CT perfusion.Neurocomputing · 2017Article
- Classifying Acute Ischemic Stroke Onset Time using Deep Imaging Features.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- [Sinogram restoration for low-dose cerebral perfusion CT images].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2016Article
- Article
- Segmentation of Hyperacute Cerebral Infarcts Based on Sparse Representation of Diffusion Weighted Imaging.Computational and mathematical methods in medicine · 2016Article
- Robust Low-Dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization.IEEE transactions on medical imaging · 2015Article
- Improving low-dose blood-brain barrier permeability quantification using sparse high-dose induced prior for Patlak model.Medical image analysis · 2014Article
- Multi-modal registration for correlative microscopy using image analogies.Medical image analysis · 2014Article
- Tissue-specific sparse deconvolution for low-dose CT perfusion.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2013Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.