Characterization of electron density of the real tissues for radiotherapy planning using dual energy algorithm and stoichiometric calibration method

Document Type: Conference Proceedings

Authors

1 Department of Medical Physics, Faculty of medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. Department of Oncology, Golestan Hospital, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.

2 Department of Medical Physics, Faculty of medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. Department of Radiology, School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.

3 Medical Imaging Research Center and Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran.

4 Ionizing and Non-Ionizing Radiation Protection Research Center, Shiraz University of Medical Sciences, Shiraz, Iran. Physics Unit, Department of Radiotherapy and Oncology, Shiraz University of Medical Sciences, Shiraz, Iran.

Abstract

Introduction: Characterization of the relative electron density (𝜌e) of the body tissues is routinely provided by scanning the commercial phantom like RMI 467 at 120 kilovoltage (kVp) in radiotherapy planning. Recent studies showed that the calibrating Hounsfield Unit–
𝜌e curve could be obtained linearly using dual energy computed tomography (DECT) algorithm in commercial phantom like RMI 467. The aim of this study was to produce a more accurate calibrating HU–𝜌e curve by constructing an in- house phantom and applying dual energy algorithm.
Materials and Methods: An in-house water filled phantom (33cm diameter) was made including ten water solutions plus composite cork as tissue substitute materials (TSM), and scanned at four kVps by multi-detector four slice CT. The dual energy algorithm was applied to two combination scans (80-140 and 100-140 kVp) and the linear HU–𝜌e curves were produced. The stoichiometric method reproduced the HUs of 31 real body tissues, that their compositions are available in ICRU-46 report. The HU–𝜌e curves for both kVp combination scans were produced. The t-test and compare means were performed between the mean and standard deviation of the relative and absolute differences (%) of the 𝜌e of 31 ICRU real tissues calculated for 120 kVp and both kVp combination scans in the current and previous studies, respectively
Results: Applying an energy subtraction algorithm mitigated the 𝜌e calculation error of real tissues. The mean and standard deviation of the relative difference between the 𝜌e of 31 ICRU tissues (–0.23±1.89) were statistically significant compared with the mean and standard deviation of the 30 ICRU tissues (0.80±1.58), which extracted from the RMI 467 phantom at
120 kVp in previous study (p<0.024). The mean and standard deviation of absolute differences of the calculated 𝜌e of the 31 ICRU tissues at 100-140 kVp combination scans (0.14±0.11) compared to previous study using 100-140kVp scan (0.30±0.40) in a second generation dual source CT, were statistically significant (P<0.035).
Conclusion: The stoichiometric calibration method and closeness of the 𝜌e of 11 TSMs can result in statistically significant smaller discrepancies in calculating the 𝜌e of real tissues at
120 kVp, compared to the previous studies with RMI 467. The stoichiometric fitting parameters were highly affected by beam hardening artifacts and image noise, especially at 80 kVp. Applying the energy subtraction algorithm can offer further error mitigation in 𝜌e calculation of real tissues by spectral separation and reduction of beam hardening artifacts and noise in two kVp combination scans compared to previous studies. Therefore, a dual energy algorithm in combination with stoichiometry can be used to decrease errors in calculation of the 𝜌e of real tissues, and could be used for radiotherapy planning and material differentiation in clinical practice.

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