ArticleEuropean radiology2022
CT-based thermometry with virtual monoenergetic images by dual-energy of fat, muscle and bone using FBP, iterative and deep learning-based reconstruction.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Subtraction-based Stepwise computed tomography post-processing with probabilistically adjusted thresholding for fat-ice demarcation: an in situ study.Radiological physics and technology · 2026Article
- Automatic personal identification using a single CT image.European radiology · 2025Article
- Exploring Morphological and Molecular Properties of Different Adipose Cell Models: Monolayer, Spheroids, Gellan Gum-Based Hydrogels, and Explants.Macromolecular bioscience · 2025Article
- Impact of Deep Learning-Based Image Reconstruction on Tumor Visibility and Diagnostic Confidence in Computed Tomography.Bioengineering (Basel, Switzerland) · 2024Article
- Impact of multi-parameter images obtained from dual-energy CT on radiomics to predict pathological grading of bladder urothelial carcinoma.Abdominal radiology (New York) · 2024Article
- Systematic Review on Learning-based Spectral CT.ArXiv · 2024Article
- What to Expect (and What Not) from Dual-Energy CT Imaging Now and in the Future?Journal of imaging · 2024Review
- Dual-Energy CT in Oncologic Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
- Accelerating computer vision-based human identification through the integration of deep learning-based age estimation from 2 to 89 years.Scientific reports · 2024Article
- Systematic Review on Learning-based Spectral CT.IEEE transactions on radiation and plasma medical sciences · 2024Article
- A Deep Learning Image Reconstruction Algorithm for Improving Image Quality and Hepatic Lesion Detectability in Abdominal Dual-Energy Computed Tomography: Preliminary Results.Journal of digital imaging · 2023Article
- Computed tomography-based thermography (CTT) in microwave ablation: prediction of the heat ablation zone in the porcine liver.Insights into imaging · 2023Article
- Evaluation of Different Registration Algorithms to Reduce Motion Artifacts in CT-Thermography (CTT).Diagnostics (Basel, Switzerland) · 2023Article
- Photon-counting computed tomography thermometry via material decomposition and machine learning.Visual computing for industry, biomedicine, and art · 2023Article
- Article
- Quality measures for fully automatic CT histogram-based fat estimation on a corpse sample.Scientific reports · 2022Article
- Bioprinting of 3D Adipose Tissue Models Using a GelMA-Bioink with Human Mature Adipocytes or Human Adipose-Derived Stem Cells.Gels (Basel, Switzerland) · 2022Article
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Abstract
objectivesThe aim of this study was to evaluate the sensitivity of CT-based thermometry for clinical applications regarding a three-component tissue phantom of fat, muscle and bone. Virtual monoenergetic images (VMI) by dual-energy measurements and conventional polychromatic 120-kVp images with modern reconstruction algorithms adaptive statistical iterative reconstruction-Volume (ASIR-V) and deep learning image reconstruction (DLIR) were compared.
methodsA temperature-regulating water circuit system was developed for the systematic evaluation of the correlation between temperature and Hounsfield units (HU). The measurements were performed on a Revolution CT with gemstone spectral imaging technology (GSI). Complementary measurements were performed without GSI (voltage 120 kVp, current 130-545 mA). The measured object was a tissue equivalent phantom in a temperature range of 18 to 50°C. The evaluation was carried out for VMI at 40 to 140 keV and polychromatic 120-kVp images.
resultsThe regression analysis showed a significant inverse linear dependency between temperature and average HU regardless of ASIR-V and DLIR. VMI show a higher temperature sensitivity compared to polychromatic images. The temperature sensitivities were 1.25 HU/°C (120 kVp) and 1.35 HU/°C (VMI at 140 keV) for fat, 0.38 HU/°C (120 kVp) and 0.47 HU/°C (VMI at 40 keV) for muscle and 1.15 HU/°C (120 kVp) and 3.58 HU/°C (VMI at 50 keV) for bone.
conclusionsDual-energy with VMI enables a higher temperature sensitivity for fat, muscle and bone. The reconstruction with ASIR-V and DLIR has no significant influence on CT-based thermometry, which opens up the potential of drastic dose reductions. KEY POINTS: • Virtual monoenergetic images (VMI) enable a higher temperature sensitivity for fat (8%), muscle (24%) and bone (211%) compared to conventional polychromatic 120-kVp images. • With VMI, there are parameters, e.g. monoenergy and reconstruction kernel, to modulate the temperature sensitivity. In contrast, there are no parameters to influence the temperature sensitivity for conventional polychromatic 120-kVp images. • The application of adaptive statistical iterative reconstruction-Volume (ASIR-V) and deep learning-based image reconstruction (DLIR) has no effect on CT-based thermometry, opening up the potential of drastic dose reductions in clinical applications.
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