CT-based thermometry with virtual monoenergetic images by dual-energy of fat, muscle and bone using FBP, iterative and deep learning–based reconstruction
| العنوان: | CT-based thermometry with virtual monoenergetic images by dual-energy of fat, muscle and bone using FBP, iterative and deep learning–based reconstruction |
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| المؤلفون: | U Teichgräber, A Heinrich, Sebastian Schenkl, David Buckreus, Felix V. Güttler |
| المصدر: | European Radiology |
| بيانات النشر: | Springer Science and Business Media LLC, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | medicine.medical_specialty, Thermometry, Iterative reconstruction, Signal-To-Noise Ratio, Imaging phantom, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Computed Tomography, Deep Learning, 0302 clinical medicine, Hounsfield scale, medicine, Humans, Radiology, Nuclear Medicine and imaging, Sensitivity (control systems), Tissue phantom, Retrospective Studies, Dual energy, business.industry, Muscles, Ultrasound, General Medicine, Spectral imaging, Dual-energy scanned projection, 030220 oncology & carcinogenesis, Radiographic Image Interpretation, Computer-Assisted, Radiology, Tomography, X-Ray Computed, business, Algorithms, Biomedical engineering |
| الوصف: | Objectives The 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. Methods A 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. Results The 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. Conclusions Dual-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. |
| تدمد: | 1432-1084 0938-7994 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::694d3f7b57c65956710a93a853ab1414Test https://doi.org/10.1007/s00330-021-08206-zTest |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....694d3f7b57c65956710a93a853ab1414 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14321084 09387994 |
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