ArticleIEEE transactions on medical imaging2017
Cone Beam X-ray Luminescence Computed Tomography Based on Bayesian Method.
Article in IEEE transactions on medical imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 53 citations in OpenAlex.
- Dual and Multi-Target Cone-Beam X-ray Luminescence Computed Tomography Based on the DeepCB-XLCT Network.Bioengineering (Basel, Switzerland) · 2024Article
- Automated Restarting Fast Proximal Gradient Descent Method for Single-View Cone-Beam X-ray Luminescence Computed Tomography Based on Depth Compensation.Bioengineering (Basel, Switzerland) · 2024Article
- A Joint-Parameter Estimation and Bayesian Reconstruction Approach to Low-Dose CT.Sensors (Basel, Switzerland) · 2023Article
- Superfast Scan of Focused X-Ray Luminescence Computed Tomography Imaging.IEEE access : practical innovations, open solutions · 2023Article
- High-speed X-ray-induced luminescence computed tomography.Journal of biophotonics · 2020Article
- Restarted primal-dual Newton conjugate gradient method for enhanced spatial resolution of reconstructed cone-beam x-ray luminescence computed tomography images.Physics in medicine and biology · 2020Article
- Adaptive shrinking reconstruction framework for cone-beam X-ray luminescence computed tomography.Biomedical optics express · 2020Article
- Method for improving the spatial resolution of narrow x-ray beam-based x-ray luminescence computed tomography imaging.Journal of biomedical optics · 2019Article
- Focused x-ray luminescence computed tomography: experimental studies.Proceedings of SPIE--the International Society for Optical Engineering · 2019Article
- Regularized reconstruction based on joint LBiomedical optics express · 2019Article
- Recent methodology advances in fluorescence molecular tomography.Visual computing for industry, biomedicine, and art · 2018Review
- Gamma rays excited radioluminescence tomographic imaging.Biomedical engineering online · 2018Article
- Sensitivity evaluation and selective plane imaging geometry for x-ray-induced luminescence imaging.Medical physics · 2017Article
- Resolving adjacent nanophosphors of different concentrations by excitation-based cone-beam X-ray luminescence tomography.Biomedical optics express · 2017Article
- Sensitivity study of x-ray luminescence computed tomography.Applied optics · 2017Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
X-ray luminescence computed tomography (XLCT), which aims to achieve molecular and functional imaging by X-rays, has recently been proposed as a new imaging modality. Combining the principles of X-ray excitation of luminescence-based probes and optical signal detection, XLCT naturally fuses functional and anatomical images and provides complementary information for a wide range of applications in biomedical research. In order to improve the data acquisition efficiency of previously developed narrow-beam XLCT, a cone beam XLCT (CB-XLCT) mode is adopted here to take advantage of the useful geometric features of cone beam excitation. Practically, a major hurdle in using cone beam X-ray for XLCT is that the inverse problem here is seriously ill-conditioned, hindering us to achieve good image quality. In this paper, we propose a novel Bayesian method to tackle the bottleneck in CB-XLCT reconstruction. The method utilizes a local regularization strategy based on Gaussian Markov random field to mitigate the ill-conditioness of CB-XLCT. An alternating optimization scheme is then used to automatically calculate all the unknown hyperparameters while an iterative coordinate descent algorithm is adopted to reconstruct the image with a voxel-based closed-form solution. Results of numerical simulations and mouse experiments show that the self-adaptive Bayesian method significantly improves the CB-XLCT image quality as compared with conventional methods.
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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.