ArticleIEEE transactions on medical imaging2015
Robust Low-Dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization.
Article in IEEE transactions on medical imaging, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Cross-Domain TransNet for sparse-view CT reconstruction.Frontiers in nuclear medicine · 2026Article
- A Novel Self-Supervised Learning-Based Method for Dynamic CT Brain Perfusion Imaging.Journal of imaging informatics in medicine · 2025Article
- Article
- DuDoUFNet: Dual-Domain Under-to-Fully-Complete Progressive Restoration Network for Simultaneous Metal Artifact Reduction and Low-Dose CT Reconstruction.IEEE transactions on medical imaging · 2022Article
- [Nonlocal low-rank and sparse matrix decomposition for low-dose cerebral perfusion CT image restoration].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2022Article
- Contrast-Medium Anisotropy-Aware Tensor Total Variation Model for Robust Cerebral Perfusion CT Reconstruction with Low-Dose Scans.IEEE transactions on computational imaging · 2020Article
- Article
- Comparison between three-dimensional CT and conventional radiography in proximal tibia morphology.Medicine · 2018Article
- Low-Dose Dynamic Cerebral Perfusion Computed Tomography Reconstruction via Kronecker-Basis-Representation Tensor Sparsity Regularization.IEEE transactions on medical imaging · 2017Article
- Low-Dose Volume-Perfusion CT of the Brain: Effects of Radiation Dose Reduction on Performance of Perfusion CT Algorithms.Clinical neuroradiology · 2017Article
- Iterative reconstruction for dual energy CT with an average image-induced nonlocal means regularization.Physics in medicine and biology · 2017Article
- TENDER: Tensor non-local deconvolution enabled radiation reduction in CT perfusion.Neurocomputing · 2017Article
- Robust dynamic myocardial perfusion CT deconvolution for accurate residue function estimation via adaptive-weighted tensor total variation regularization: a preclinical study.Physics in medicine and biology · 2016Article
- Low-dose cerebral perfusion computed tomography image restoration via low-rank and total variation regularizations.Neurocomputing · 2016Article
- Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2016Article
- [Sinogram restoration for low-dose cerebral perfusion CT images].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2016Article
- Denoising Magnetic Resonance Images Using Collaborative Non-Local Means.Neurocomputing · 2016Article
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Authors and funding
4 authors.
Funding
Abstract
Acute brain diseases such as acute strokes and transit ischemic attacks are the leading causes of mortality and morbidity worldwide, responsible for 9% of total death every year. "Time is brain" is a widely accepted concept in acute cerebrovascular disease treatment. Efficient and accurate computational framework for hemodynamic parameters estimation can save critical time for thrombolytic therapy. Meanwhile the high level of accumulated radiation dosage due to continuous image acquisition in CT perfusion (CTP) raised concerns on patient safety and public health. However, low-radiation leads to increased noise and artifacts which require more sophisticated and time-consuming algorithms for robust estimation. In this paper, we focus on developing a robust and efficient framework to accurately estimate the perfusion parameters at low radiation dosage. Specifically, we present a tensor total-variation (TTV) technique which fuses the spatial correlation of the vascular structure and the temporal continuation of the blood signal flow. An efficient algorithm is proposed to find the solution with fast convergence and reduced computational complexity. Extensive evaluations are carried out in terms of sensitivity to noise levels, estimation accuracy, contrast preservation, and performed on digital perfusion phantom estimation, as well as in vivo clinical subjects. Our framework reduces the necessary radiation dose to only 8% of the original level and outperforms the state-of-art algorithms with peak signal-to-noise ratio improved by 32%. It reduces the oscillation in the residue functions, corrects over-estimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT), and maintains the distinction between the deficit and normal regions.
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Registered trials
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.