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.
Highlights
Materials and methodsRadiodensity depends on the temperature of the tissue, since the density decreases if the temperature of the tissue increases
With recent technical advances in computed tomography (CT) (Fig. 1), such as dual-energy and iterative reconstruction, e.g. adaptive statistical iterative reconstruction-Volume (ASIR-V) [14, 15], or deep learning image reconstruction (DLIR) [16], the question arises whether these advances have improved the quality of CT-based thermometry
DLIR represents the latest stage of development in CT image reconstruction and promises significant improvements in image quality and radiation dose reduction compared to ASIR-V and filtered back projection (FBP) [16,17,18]
Summary
Materials and methodsRadiodensity depends on the temperature of the tissue, since the density decreases if the temperature of the tissue increases. Possible applications of CT-based thermometry would be the monitoring of tissue temperatures during high-frequency treatments or microwave ablation of tumors in the liver and kidneys [2, 3, 6,7,8,9] Despite these clear relationships, the method is rarely used in clinical practice so far [10], because the repeatability of quantitative CT numerical measurements has not been guaranteed and a higher required ionizing radiation, e.g. for thermometry due to more frequent scans [11]. A dual-energy CT with a gemstone spectral imaging (GSI) detector measures the raw data of an object during a continuous energy change between low (80 kVp) and high (140 kVp) voltage [19] This generates two data records with different attenuation values based on the corresponding energy levels, which allow the reconstruction of material decomposition images and virtual monoenergetic images (VMI). VMI demonstrated significantly better signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and subjective score compared to conventional 120-kVp polychromatic images [20]
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