ArticleIEEE transactions on medical imaging2017
Comparison Between Pre-Log and Post-Log Statistical Models in Ultra-Low-Dose CT Reconstruction.
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 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The application of metal artifact reduction methods on computed tomography scans for radiotherapy applications: A literature review.Journal of applied clinical medical physics · 2021Pooled it
- Comparative Analysis of Bilateral-Based Noise Reduction Algorithms in Low-Dose Pediatric Computed Tomographic Images.Diagnostics (Basel, Switzerland) · 2026Article
- Gradient Descent Provably Solves Nonlinear Tomographic Reconstruction.IEEE transactions on information theory · 2026Article
- Comparison of iterative reconstruction implementations for multislice helical CT.Zeitschrift fur medizinische Physik · 2025Article
- Patient-specific hyperparameter learning for optimization-based CT image reconstruction.Physics in medicine and biology · 2021Article
- LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction.Scientific data · 2021Article
- Article
- Statistical characterization of the linear attenuation coefficient in polychromatic CT scans.Medical physics · 2020Article
- Characterization of tissue-specific pre-log Bayesian CT reconstruction by texture-dose relationship.Medical physics · 2020Article
- Constructing a tissue-specific texture prior by machine learning from previous full-dose scan for Bayesian reconstruction of current ultralow-dose CT images.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- SPULTRA: Low-Dose CT Image Reconstruction With Joint Statistical and Learned Image Models.IEEE transactions on medical imaging · 2020Article
- Regularization strategies in statistical image reconstruction of low-dose x-ray CT: A review.Medical physics · 2018Review
- Structurally-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising.IEEE access : practical innovations, open solutions · 2018Article
- Hybrid Pre-Log and Post-Log Image Reconstruction for Computed Tomography.IEEE transactions on medical imaging · 2017Article
- Robust Low-Dose CT Sinogram Preprocessing via Exploiting Noise-Generating Mechanism.IEEE transactions on medical imaging · 2017Article
- Accuracy comparison of 4D computed tomography (4DCT) and 4D cone beam computed tomography (4DCBCT).International journal of medical physics, clinical engineering and radiation oncology · 2017Article
- Recent advances in cardiac computed tomography dose reduction strategies: a review of scientific evidence and technical developments.Journal of medical imaging (Bellingham, Wash.) · 2017Review
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9 authors.
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Abstract
X-ray detectors in clinical computed tomography (CT) usually operate in current-integrating mode. Their complicated signal statistics often lead to intractable likelihood functions for practical use in model-based image reconstruction (MBIR). It is therefore desirable to design simplified statistical models without losing the essential factors. Depending on whether the CT transmission data are logarithmically transformed, pre-log and post-log models are two major categories of choices in CT MBIR. Both being approximations, it remains an open question whether one model can notably improve image quality over the other on real scanners. In this study, we develop and compare several pre-log and post-log MBIR algorithms under a unified framework. Their reconstruction accuracy based on simulation and clinical datasets are evaluated. The results show that pre-log MBIR can achieve notably better quantitative accuracy than post-log MBIR in ultra-low-dose CT, although in less extreme cases, post-log MBIR with handcrafted pre-processing remains a competitive alternative. Pre-log MBIR could play a growing role in emerging ultra-low-dose CT applications.
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