ArticleIEEE transactions on radiation and plasma medical sciences2024
Systematic Review on Learning-based Spectral CT.
Article in IEEE transactions on radiation and plasma medical sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- A Dual-Energy CBCT With Reduced Scatter and Cone Beam Artifacts Using an X-Ray Source Array and Interlaced Spectral Filters.IEEE transactions on bio-medical engineering · 2026Article
- Application and Development of Spectral CT in Target Delineation for Lung Cancer Radiotherapy.Diagnostics (Basel, Switzerland) · 2026Review
- Accurate iodine quantification and residual error reduction with principal component analysis multimaterial decomposition using spectral CT.Medical physics · 2026Article
- ZS4D: Zero-Shot Self-Similarity-Steered Denoiser for Volumetric Photon-Counting CT.IEEE transactions on radiation and plasma medical sciences · 2026Article
- An interpretable cascaded residual iterative network for sparse-view spectral CT imaging.Quantitative imaging in medicine and surgery · 2026Article
- The Applications of Spectral CT in Interventional Radiology: Innovations in Diagnosis and Intra-procedural Guidance.Cardiovascular and interventional radiology · 2026Review
- Spectral CT in practice: insights from an International Atomic Energy Agency survey.Insights into imaging · 2025Article
- Enhancing synchrotron radiation micro-CT images using deep learning: an application of Noise2Inverse on bone imaging.Journal of synchrotron radiation · 2025Article
- Performance Evaluation of Image Segmentation Using Dual-Energy Spectral CT Images with Deep Learning Image Reconstruction: A Phantom Study.Tomography (Ann Arbor, Mich.) · 2025Article
- Spectral dual-layer detector CT-based radiomics-deep learning for predicting pathological aggressiveness of stage I lung adenocarcinoma: discrimination of precursor glandular lesions and invasive adenocarcinomas.Translational lung cancer research · 2025Article
- Better Cone-Beam CT Artifact Correction via Spatial and Channel Reconstruction Convolution Based on Unsupervised Adversarial Diffusion Models.Bioengineering (Basel, Switzerland) · 2025Article
- The diagnosis value of dual-energy computed tomography (DECT) multi-parameter imaging in lung adenocarcinoma and squamous cell carcinoma.BMC pulmonary medicine · 2024Article
- What to Expect (and What Not) from Dual-Energy CT Imaging Now and in the Future?Journal of imaging · 2024Review
- Derivative-Free Iterative One-Step Reconstruction for Multispectral CT.Journal of imaging · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.
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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.