ArticleMedical physics2018
Quantitative accuracy of CT numbers: Theoretical analyses and experimental studies.
Article in Medical physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
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- Physics-informed model-based generative neural network for synthesizing scanner- and algorithm-specific low-dose CT exams.Medical physics · 2025Article
- Spectral performance for iodine quantification of a dual-source, dual-kV photon counting detector CT.Medical physics · 2025Article
- Simulating scanner-and algorithm-specific 3D CT noise texture using physics-informed 2D and 2.5D generative neural network models.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- A Review on the Use of Imaging Biomarkers in Oncology Clinical Trials: Quality Assurance Strategies for Technical Validation.Tomography (Ann Arbor, Mich.) · 2023Review
- Correcting statistical CT number biases without access to raw detector counts: Applications to high spatial resolution photon counting CT imaging.Medical physics · 2023Article
- Impact of Noise Level on the Accuracy of Automated Measurement of CT Number Linearity on ACR CT and Computational Phantoms.Journal of biomedical physics & engineering · 2023Article
- Reconstruction of three-dimensional tomographic patient models for radiation dose modulation in CT from two scout views using deep learning.Medical physics · 2022Article
- Pore-throat structure characterization of carbon fiber reinforced resin matrix composites: Employing Micro-CT and Avizo technique.PloS one · 2021Article
- Deep-learning-based direct inversion for material decomposition.Medical physics · 2020Article
- Leveraging non-contrast head CT to improve the image quality of cerebral CT perfusion maps.Journal of medical imaging (Bellingham, Wash.) · 2020Article
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6 authors.
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Abstract
purposeThe CT number accuracy, that is, CT number bias, plays an important role in clinical diagnosis. When strategies to reduce radiation dose are discussed, it is important to make sure that the CT number bias is controlled within an acceptable range. The purpose of this paper was to investigate the dependence of CT number bias on radiation dose level and on image contrast (i.e., the difference in CT number between the ROI and the background) in Computed Tomography (CT).
methodsA lesion-background model was introduced to theoretically study how the CT number bias changes with radiation exposure level and with CT number contrast when a simple linear reconstruction algorithm such as filtered backprojection (FBP) is used. The theoretical results were validated with experimental studies using a benchtop CT system equipped with a photon-counting detector (XC-HYDRA FX50, XCounter AB, Sweden) and a clinical diagnostic MDCT scanner (Discovery CT750 HD, GE Healthcare, Waukesha, WI, USA) equipped with an energy-integrating detector. The Catphan phantom (Catphan 600, the Phantom Laboratory, Salem, NY, USA) was scanned at different mAs levels and 50 scans were performed for each mAs. The bias of CT number was evaluated for each combination of mAs and ROIs with different contrast levels. An anthropomorphic phantom (ATOM 10-year-old phantom, Model 706, CIRS Inc. Norfolk, VA, USA) with much more heterogeneous object content was used to test the applicability of the theory to the more general image object cases.
resultsBoth theoretical and experimental studies showed that the CT number bias is inversely proportional to the radiation exposure level yet linearly dependent on the CT number contrast between the lesion and the background, that is,
conclusionsThe quantitative accuracy of CT numbers can be problematic and thus needs some extra attention when radiation dose is reduced. In this work, we showed that the bias of the FBP reconstruction increases as mAs is reduced; both positive and negative bias can be observed depending on the contrast difference between a targeted ROI and its surrounding background tissues.
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