ArticlePhysics in medicine and biology2016
Spectral prior image constrained compressed sensing (spectral PICCS) for photon-counting computed tomography.
Article in Physics in medicine and biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
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- An interpretable cascaded residual iterative network for sparse-view spectral CT imaging.Quantitative imaging in medicine and surgery · 2026Article
- Super Time-Resolved Tomography.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Systematic Review on Learning-based Spectral CT.ArXiv · 2024Article
- Systematic Review on Learning-based Spectral CT.IEEE transactions on radiation and plasma medical sciences · 2024Article
- A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images.Tomography (Ann Arbor, Mich.) · 2023Article
- Low-dose spectral reconstruction with global, local, and nonlocal priors based on subspace decomposition.Quantitative imaging in medicine and surgery · 2023Article
- Image-spectral decomposition extended-learning assisted by sparsity for multi-energy computed tomography reconstruction.Quantitative imaging in medicine and surgery · 2023Article
- Measurement of enhanced vasa vasorum density in a porcine carotid model using photon counting detector CT.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Deep learning-based virtual noncalcium imaging in multiple myeloma using dual-energy CT.Medical physics · 2022Article
- One-step iterative reconstruction approach based on eigentissue decomposition for spectral photon-counting computed tomography.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- One half-scan dual-energy CT imaging using the Dual-domain Dual-way Estimated Network (DoDa-Net) model.Quantitative imaging in medicine and surgery · 2022Article
- Ultra-Low-Dose Spectral CT Based on a Multi-level Wavelet Convolutional Neural Network.Journal of digital imaging · 2021Article
- Spectral Photon Counting CT: Imaging Algorithms and Performance Assessment.IEEE transactions on radiation and plasma medical sciences · 2021Article
- Image-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning.IEEE transactions on radiation and plasma medical sciences · 2021Article
- [Four-dimensional cone-beam CT reconstruction based on motion-compensated robust principal component analysis].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2021Article
- Next-Generation Hardware Advances in CT: Cardiac Applications.Radiology · 2021Review
- Noise reduction in CT image using prior knowledge aware iterative denoising.Physics in medicine and biology · 2020Article
- Dual Energy Differential Phase Contrast CT (DE-DPC-CT) Imaging.IEEE transactions on medical imaging · 2020Article
- Multi-energy CT reconstruction using tensor nonlocal similarity and spatial sparsity regularization.Quantitative imaging in medicine and surgery · 2020Article
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4 authors.
Funding
Abstract
Photon-counting computed tomography (PCCT) is an emerging imaging technique that enables multi-energy imaging with only a single scan acquisition. To enable multi-energy imaging, the detected photons corresponding to the full x-ray spectrum are divided into several subgroups of bin data that correspond to narrower energy windows. Consequently, noise in each energy bin increases compared to the full-spectrum data. This work proposes an iterative reconstruction algorithm for noise suppression in the narrower energy bins used in PCCT imaging. The algorithm is based on the framework of prior image constrained compressed sensing (PICCS) and is called spectral PICCS; it uses the full-spectrum image reconstructed using conventional filtered back-projection as the prior image. The spectral PICCS algorithm is implemented using a constrained optimization scheme with adaptive iterative step sizes such that only two tuning parameters are required in most cases. The algorithm was first evaluated using computer simulations, and then validated by both physical phantoms and in vivo swine studies using a research PCCT system. Results from both computer-simulation and experimental studies showed substantial image noise reduction in narrow energy bins (43-73%) without sacrificing CT number accuracy or spatial resolution.
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