Hostname: page-component-6898cfcb55-zsp5v Total loading time: 0 Render date: 2026-11-11T09:50:17.589Z Has data issue: false hasContentIssue false
  • English
  • Français

The effect of CT reconstruction filter selection on Hounsfield units in radiotherapy treatment planning

Published online by Cambridge University Press:  19 June 2023

Oussama NhilaOpen the ORCID record for Oussama Nhila[Opens in a new window],
Mohammed Talbi,
M’hamed El Mansouri,
Moulay Ali Youssoufi,
Morad Erraoudi,
El Mahjoub Chakir and
Mohamed Azougagh
Oussama Nhila*
Affiliation:
Ibn Tofail University, Faculty of Sciences, Department of Physics, Laboratory of Materials and Subatomic Physics, Kenitra, Morocco
Mohammed Talbi
Affiliation:
Moulay Ismail University, Faculty of Sciences, Physical Sciences and Engineering, Meknes, Morocco
M’hamed El Mansouri
Affiliation:
Ibn Tofail University, Faculty of Sciences, Department of Physics, Laboratory of Materials and Subatomic Physics, Kenitra, Morocco
Moulay Ali Youssoufi
Affiliation:
National Institute of Oncology, University Hospital Center, Rabat, Morocco
Morad Erraoudi
Affiliation:
Mohammed V University, Faculty of Sciences, Department of Physics, Rabat, Morocco
El Mahjoub Chakir
Affiliation:
Ibn Tofail University, Faculty of Sciences, Department of Physics, Laboratory of Materials and Subatomic Physics, Kenitra, Morocco
Mohamed Azougagh
Affiliation:
Mohammed V University, National Graduate School of Arts and Crafts, Rabat, Morocco
*
Corresponding author: Oussama Nhila; Email: nhila.oussama@gmail.com
Rights & Permissions [Opens in a new window]

Abstract

Introduction:

This work aims to evaluate the effect of Hitachi 16-slice scanner reconstruction filters on Hounsfield unit (HU) variations. In the literature, there is a lack of information from a wide variety of scanners in this regard. In addition, not all studies have investigated the effect of reconstruction filters on HU in an exhaustive way.

Methods:

The computerised imaging reference system electron density phantom (model 062M) was scanned with different substitute materials of different density from Hitachi 16-slice computed tomography. The raw images were obtained with four tube voltage settings: 80 kVp, 100 kVp, 120 kVp and 140 kVp. The raw images for each energy level were then reconstructed using different reconstruction filters.

Results:

The HU values of dense bone were significantly different when changing the reconstruction filters without beam hardening correction (BHC). Nevertheless, when selecting the BHC, this variation decreases heavily for 80 kVp and decreases slightly for 140 kVp, but it remains outside the tolerance of ±50 HU. However, for 100 kVp and 120 kVp, the differences in HU values become within the tolerances indicated for dense bone.

Conclusions:

Changing image reconstruction filters during a dosimetric scan had a significant effect on HU in dense bone. Therefore, it is recommended to evaluate this effect during the commissioning phase. As a result, this study provides a methodology to comprehensively investigate the effect of reconstruction filters on HU.

Keywords

computed tomographyCT-RED calibration curveHounsfield unitradiotherapyreconstruction filters

Information

Type
Original Article
Information
Journal of Radiotherapy in Practice , Volume 22 , 2023, e102
Check for updates
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Introduction

Computed tomography (CT) is the principal imaging modality in radiation therapy. Reference van Elmpt and Landry1 It provides the necessary information required for the treatment planning system (TPS), such as patient CT images for target volumes and organs at risk delineation, and Hounsfield unit (HU) that are converted into physical densities or relative electronic densities from a CT number-relative electron density (CT-RED) calibration curve for dose calculation. Reference van der Heyden, Öllers, Ritter, Verhaegen and van Elmpt2,Reference Vergalasova, McKenna, Yue and Reyhan3 Thus, the quality and fidelity of the treatment plans depend on both image quality and calculation accuracy of HU. Reference Davis, Palmer, Pani and Nisbet4,Reference Chen, Noid and Tai5 Consequently, to provide high image quality, CT image acquisition protocols should be varied according to patient morphology and region of interest (ROI). Reference Li, Yu and Anastasio6–Reference El Mansouri, Choukri, Talbi and Hakam9 However, these variations can affect the HU values. Reference Davis, Palmer, Pani and Nisbet4,Reference Nhila, Talbi, El, Katib and El Chakir10,Reference Zurl, Tiefling, Winkler, Kindl and Kapp11 In this context, Zurl et al. concluded that the use of different CT protocols leads to variations of up to 20% in HU values. Reference Zurl, Tiefling, Winkler, Kindl and Kapp11 A number of studies have quantified the dose change in the radiation therapy planning process due to HU variation. Reference Saini, Pandey, Kumar, Singh and Pasricha12–Reference Cozzi, Fogliata, Buffa and Bieri14 One of these reports to the International Atomic Energy Agency (IAEA) has found that a variation of ± 60 HU can result in ± 1% deviation of the calculated dose for the 6 MV photon beam crossing 5 cm of bone equivalent material. Therefore, a number of recommended tolerances of HU values used in the CT-RED calibration curve are available in the literature. Reference Davis, Palmer and Nisbet15–Reference Bissonnette, Balter and Dong18 For example, two of the most recent references, one is from the Institute of Physics and Engineering in Medicine (IPEM), which quotes a change of ± 30 HU in soft tissue to limit the dose change to ± 1%, and with a dose change of ± 2% in lung and bone corresponded to a HU change of ± 50 and ± 150 HU, respectively. Reference Patel, Steven and Antony17 The second which is the most widely adopted in recent studies concluded that a dose change of about 1% corresponded to a change of ± 20 HU in soft tissue and ± 50 HU in bone, as well as in air. Reference Davis, Palmer and Nisbet15

For this reason, several studies have investigated the effect of acquisition parameters, Reference Nhila, Talbi, El, Katib and El Chakir10,Reference Fang, Mazur, Mutic and Khan13,Reference Das, Cheng, Cao and Johnstone19,Reference Ebert, Lambert and Greer20 type of scanner Reference Chung, Mossahebi and Gopal21,Reference Cropp, Seslija, Tso and Thakur22 and type of phantom Reference Koniarova23,Reference Hasani, Farhood and Ghorbani24 on the HU variation. The most influential parameters are the voltage tube Reference Saini, Pandey, Kumar, Singh and Pasricha12,Reference Zheng, Al-Hayek and Cummins25,Reference Afifi, Abdelrazek, Deiab, Abd El-Hafez and El-Farrash26 and the reconstruction filters, whereas, for this last, the HU change depends on the brand of scanner. Reference Vergalasova, McKenna, Yue and Reyhan3,Reference Zurl, Tiefling, Winkler, Kindl and Kapp11,Reference Davis, Muscat and Palmer27 For this purpose, the setting of reconstruction filters is recommended during the commissioning phase. Reference Lillicrap28 In the literature, some studies have provided methodologies to evaluate the effect of reconstruction filters on the number of HU. Reference Vergalasova, McKenna, Yue and Reyhan3,Reference Davis, Palmer, Pani and Nisbet4,Reference Zurl, Tiefling, Winkler, Kindl and Kapp11,Reference Davis, Muscat and Palmer27 Most of these studies are performed with the most commonly used tube voltage in clinical routine, which is 120 kVp. Reference van der Heyden, Öllers, Ritter, Verhaegen and van Elmpt2 Furthermore, these studies are conducted with various scanner brands such as Toshiba, Siemens, GE and Philips. However, none of these studies have evaluated the Hitachi Supria scanner algorithms.

In our previous study, the effect of CT acquisition protocols on HUs for this brand of scanner has been investigated. Reference Nhila, Talbi, El, Katib and El Chakir10 It was concluded that changing the reconstruction filter from body to head and using beam hardening correction (BHC) significantly affects HU in dense bone. Moreover, this study was conducted with a single energy (120 kVp). However, in clinical workflows, dosimetric scans may be performed with other tube voltage settings. Nevertheless, there is a lack of knowledge regarding the effect of reconstruction filters on the HU when selecting voltages higher or lower than 120 kVp.

The purpose of this work is to evaluate the effect of Hitachi Supria scanner reconstruction filters on HU and to see how this effect varies as a function of kVp. This can help the clinicians to quantify any possible change in HU when varying both reconstruction filters and tube voltage.

Materials and Methods

The measurements were carried out on a Hitachi Supria scanner 16-slice. The gantry opening was 75 cm in diameter. The tube voltage settings were 80 kVp, 100 kVp, 120 kVp, and 140 kVp. The tube current could be adjusted from 10 to 400 mA. This scanner has an automatic exposure control and modulates tube current called Intelli EC and a dose reduction function that applies an iterative reconstruction technology called Intelli IP. It achieves both low dose and high image quality by reducing image noise and artefacts.

The scans of a computerised imaging reference system M062 phantom were acquired. The distribution of the tissue equivalent inserts in the phantom as well as their relative electron densities (REDs) is shown in Figure 1.

Figure 1.

The distribution of tissue equivalent inserts in the phantom and their REDs (a) CIRS M062 and (b) CT image of CIRS M062.

As a first step, the acquisitions were done using the four tube voltage settings while keeping the other acquisition parameters constant (300 mAs, 500 FOV, 2·5mm slices, 1·065 × 0·625 collimation). As a second step, the raw data were reconstructed using different reconstruction algorithms. The reconstruction filters studied are listed in Table 1. The mean HUs of each insert were obtained through the central slice of the phantom using a ROI of 10 mm diameter; the ROI must be spaced far from the extremities of the inserts to avoid statistical fluctuations.

Table 1.

Main reconstruction filters of Hitachi Supria 16-slice scanner

Results

Figures 2, 3, 4 and 5 show the HU variations of the CT-RED calibration curves when comparing the reconstructions filter of body (F32) against head (F12) with tube voltage settings of 80 kVp, 100 kVp, 120 kVp and 140 kVp, respectively. All these figures show that the HU values coincide for all materials except for the RED equal to 1·51, which explains that there is no significant difference between the reconstruction filters, except for the dense bone. In addition, Figures 2 and 5 show a significant difference in the HU values of dense bone when changing the reconstruction filters without BHC: up to 159 HU for 80 kVp (Figure 2a) and 92 HU for140 kVp (Figure 5a). Nevertheless, when selecting the BHC, this variation decreases strongly for 80 kVp to become 51 HU (fig2B) and decreases slightly for 140 kVp to become 74 HU (Figure 5b), but it is still out of tolerance which is ±50 HU.

Figure 2.

CT-RED calibration curves of F12 versus F32 at 80 kVp (a) without BHC and (b) with BHC.

Figure 3.

CT-RED calibration curves of F12 versus F32 at 140 kVp (a) without BHC and (b) with BHC.

Figure 4.

CT-RED calibration curves of F12 versus F32 at 100 kVp (a) without BHC and (b) with BHC.

Figure 5.

CT-RED calibration curves of F12 versus F32 at 120 kVp (a) without BHC and (b) with BHC.

In contrast, Figures 3 and 4 show the effect of the reconstruction filters without and with BHC on the HU variation for voltage setting of 100 kVp and 120 kVp, respectively. The results still show that changing the filters has a significant impact on the HU values in the dense bone, 121 HU and 98 HU differences between the filters without BHC for tube voltage setting of 100 kVp (Figure 3a) and 120 kVp (Figure 4a), respectively. However, in this case, when selecting the BHC the impact of the filters decreases and the HU differences become within the tolerances indicated for the dense bone (Figures 3b and 4b).

Figure 6 shows the difference between the F12 and F32 reconstruction filters without BHC; according to the tube voltage setting, the difference was greater for the materials at the extremes of the HU scale (the lowest and highest densities). However, it was still within the tolerances for lung. Hence, for dense bone, the variation in HU values was increased when the tube voltage was decreased from 140 kVp to 80 kVp.

Figure 6.

HU variation between F32 and F12 without BHC according to different kVp settings.

Figure 7 shows the variation of HU of substitute materials between F12 and F32 with BHC according to the tube voltage settings. In this case, the greatest variation appeared in the tube voltage extremes, which are 140 kVp and 80 kVp.

Figure 7.

HU variation between F32 and F12 with BHC according to different kVp settings.

Discussion

In the present study, we aimed to evaluate the effect of reconstruction filters on HU variation and investigate the differences based on kVp settings.

According to the results, we concluded that the reconstruction filters have no significant effect on the HU change except in high-density materials; the degree of this variation depends on the selected tube voltage. Thus, for dense bone, the difference in HU between F12 and F32 increased as energy decreased from 140 kVp to 80 kVp. This result is consistent with the study of Sarah et al. Reference Kirwin, Langmack and Nightingale29 On a Siemens Emotion Duo scanner, a range of head and body reconstruction algorithms were tested with varying energies of 80 kV, 110 kV and 130kV. They concluded that the maximum difference between reconstructions filters increases when decreasing energy.

However, this no longer applies when selecting the BHC. In addition, the selection of BHC reduces the effect of the reconstruction filters on the HU, and only the differences between the filters when selecting the extreme energies (80 kVp and 140 kVp) become out of tolerance (±50 HU) for the dense bone. For other materials, we had a minimal effect of about 2–3 HU, as reported in Figure 7.

This can be explained by the change in energy spectrum resulting from the BHC algorithm, which aims to transform polychromatic attenuation data into the monochromatic equivalent before image reconstruction. Reference Ketcham and Hanna30

Therefore, changes in HU depend on the BHC algorithm of the scanner brand. Reference Zheng, Al-Hayek and Cummins25 In the literature, the effect of these algorithms is not well described, although the extent of the variation is not clearly indicated. For instance, on a Toshiba Aquilion scanner, Zurl et al. Reference Zurl, Tiefling, Winkler, Kindl and Kapp11 concluded that the use of a beam-hardening filter resulted in a dose difference of 0·6% in response to a density change of about 5%. Nevertheless, this study does not precisely indicate the HU changes of each substitute material.

The common point between the effects of the reconstruction filters as a function of all tube voltage settings is that only HU values of dense bone were significantly affected. Similarly, a tolerable effect was observed for the lung and negligible for the soft tissue. In this context, several studies have investigated the effect of reconstruction filters on HU using different types of scanners. One of them was conducted by Anne et al. Reference Davis, Muscat and Palmer27 on three brands of scanner: GE (Chicago, USA), Toshiba (now Canon, Tochigi Prefecture, Japan) and Siemens (Erlangen, Germany). They found that the degree of HU change depended strongly on the selected reconstruction filter, with some resulting in little or no change. The largest HU changes were observed for reconstruction filters on GE (Chicago, USA) and Toshiba (now Canon, Tochigi Prefecture, Japan) CT scanners, even for soft tissue. In contrast, Siemens showed insignificant variations in HU.

Typically, radiotherapy centres insert the calibration curves in TPS taking into account the effect of kVp change, but few centres take into account the effect of reconstruction filters on HU. It seems that this may be caused by the lack of studies in this context.

However, a significant number of studies have recently been published, which evaluate the effect of reconstruction filters from different scanner brands on HU variations. Reference van der Heyden, Öllers, Ritter, Verhaegen and van Elmpt2,Reference Vergalasova, McKenna, Yue and Reyhan3,Reference Ebert, Lambert and Greer20,Reference Davis, Muscat and Palmer27 These studies can help physicists to understand the performance of their scanner in HU calculation. Thus, a change in kVp during a dosimetric scan can be associated with a change in the acquisition parameters, including the reconstruction filter. However, these earlier studies only examined the effect of filters for a single energy. Therefore, it was important to investigate the effect of reconstruction filters on HU values according to the selected energy, which was the objective of this work.

The decision to implement a new calibration curve or not is based on the effect of HU variations on the dose calculation. For example, third generation algorithms such as Monte Carlo take into account small variations in the electron density of the medium and include them in dose calculations. Reference Saini, Pandey, Kumar, Singh and Pasricha12

Therefore, with the advent of new techniques like intensity-modulated radiation therapy, the tolerances of variations in dose calculation have become more stringent. Consequently, each centre must establish its own HU tolerances according to its technical platform (scanner, phantom, TPS, accelerator, etc.) and treatment techniques.

Conclusion

The purpose of this work is to evaluate the effect of Hitachi Supria scanner reconstruction filters on HU and to see how this effect varies as a function of kVp. This can help the clinicians quantify any possible change in HU when varying both reconstruction filters and tube voltage.

References

van Elmpt, W, Landry, G. Quantitative computed tomography in radiation therapy: a mature technology with a bright future. Phys Imaging Radiat Oncol 2018; 6: 12–13.CrossRefGoogle ScholarPubMed
van der Heyden, B, Öllers, M, Ritter, A, Verhaegen, F, van Elmpt, W. Clinical evaluation of a novel CT image reconstruction algorithm for direct dose calculations. Phys Imaging Radiat Oncol 2017; 2: 11–16.CrossRefGoogle Scholar
Vergalasova, I, McKenna, M, Yue, N J, Reyhan, M. Impact of computed tomography (CT) reconstruction kernels on radiotherapy dose calculation. J Appl Clin Med Phys 2020; 21: 178–186.CrossRefGoogle ScholarPubMed
Davis, A T, Palmer, A L, Pani, S, Nisbet, A. Assessment of the variation in CT scanner performance (image quality and Hounsfield units) with scan parameters, for image optimisation in radiotherapy treatment planning. Phys Medica 2018; 45: 198–204.CrossRefGoogle ScholarPubMed
Chen, GP, Noid, G, Tai, A et al. Improving CT quality with optimized image parameters for radiation treatment planning and delivery guidance. Phys Imaging Radiat Oncol 2017; 4: 6–11.CrossRefGoogle Scholar
Li, H, Yu, L, Anastasio, MA et al. Automatic CT simulation optimization for radiation therapy: A general strategy. Med Phys 2014; 41: 031913.CrossRefGoogle ScholarPubMed
Li, H, Dolly, S, Chen, HC et al. A comparative study based on image quality and clinical task performance for CT reconstruction algorithms in radiotherapy. J Appl Clin Med Phys 2016; 17: 377–390.CrossRefGoogle ScholarPubMed
El Mansouri, M, Choukri, A, Talbi, M, Khallouki, O. Radiation dose and image quality in abdominal CT: Phantom Study. (IJRE) Intern J Res Ethics (ISSN 2665-7481), 2022 5(1). https://doi.org/10.51766/ijre.v5i1.151.CrossRefGoogle Scholar
El Mansouri, M, Choukri, A, Talbi, M, Hakam, O K. Impact of tube voltage on radiation dose (CTDI) and image quality at chest CT examination. Atom Indones 2021; 47: 105.CrossRefGoogle Scholar
Nhila, O, Talbi, M, El, M, Katib, M, El Chakir, E M. Evaluation of CT acquisition protocols effect on Hounsfield units and optimization of CT-RED calibration curve selection in radiotherapy treatment planning systems. Moscow Univ Phys Bull 2022; 77: 661–671.CrossRefGoogle Scholar
Zurl, B, Tiefling, R, Winkler, P, Kindl, P, Kapp, K S. Hounsfield units variations: impact on CT-density based conversion tables and their effects on dose distribution. Strahlentherapie und Onkol 2014; 190: 88–93.CrossRefGoogle ScholarPubMed
Saini, A, Pandey, V P, Kumar, P, Singh, A, Pasricha, R. Investigation of tube voltage dependence on CT number and its effect on dose calculation algorithms using thorax phantom in Monaco treatment planning system for external beam radiation therapy. 2021; 46: 315–323. doi: 10.4103/jmp.JMP CrossRefGoogle Scholar
Fang, R, Mazur, T, Mutic, S, Khan, R. The impact of mass density variations on an electron Monte Carlo algorithm for radiotherapy dose calculations. Phys Imaging Radiat Oncol 2018; 8: 1–7.CrossRefGoogle Scholar
Cozzi, L, Fogliata, A, Buffa, F, Bieri, S. Dosimetric impact of computed tomography calibration on a commercial treatment planning system for external radiation therapy. Radiother Oncol 1998; 48: 335–338.CrossRefGoogle ScholarPubMed
Davis, A T, Palmer, A L, Nisbet, A. Can CT scan protocols used for radiotherapy treatment planning be adjusted to optimize image quality and patient dose? A systematic review. Br J Radiol 2017; 90: 20160406.CrossRefGoogle Scholar
Assurance, Q, Calculations, D. Commissioning of Radiotherapy Treatment Planning Systems: Testing for Typical External Beam Treatment Techniques. At Energy. Vienna, Austria: IAEA, 2008.Google Scholar
Patel, I, Steven, W, Antony, P. Physics Aspects of Quality Control in Radiotherapy (IPEM Report 81, 2nd edition. York: Institution of Physics & Engineering in Medicine & Biology, 2018: 550.Google Scholar
Bissonnette, J P, Balter, PA, Dong, L et al. Quality assurance for image-guided radiation therapy utilizing CT-based technologies: a report of the AAPM TG-179. Med Phys 2012; 39: 1946–1963.CrossRefGoogle Scholar
Das, I J, Cheng, C W, Cao, M, Johnstone, P A S. Computed tomography imaging parameters for inhomogeneity correction in radiation treatment planning. J Med Phys 2016; 41: 3–11.Google ScholarPubMed
Ebert, M A, Lambert, J, Greer, P B. CT-ED conversion on a GE Lightspeed-RT scanner: Influence of scanner settings. Australas Phys Eng Sci Med 2008; 31: 154–159.CrossRefGoogle Scholar
Chung, H, Mossahebi, S, Gopal, A et al. Evaluation of computed tomography scanners for feasibility of using averaged Hounsfield unit–to–stopping power ratio calibration curve. Int J Part Ther 2018; 5: 28.Google ScholarPubMed
Cropp, R J, Seslija, P, Tso, D, Thakur, Y. Scanner and kVp dependence of measured CT numbers in the ACR CT phantom. J Appl Clin Med Phys 2013; 14: 338–349.Google ScholarPubMed
Koniarova, I. Inter-comparison of phantoms for CT numbers to relative electron density (RED)/physical density calibration and influence to dose calculation in TPS. J Phys Conf Ser 2019; 1248: 012046.CrossRefGoogle Scholar
Hasani, M, Farhood, B, Ghorbani, M et al. Effect of computed tomography number-relative electron density conversion curve on the calculation of radiotherapy dose and evaluation of Monaco radiotherapy treatment planning system. Australas Phys Eng Sci Med 2019; 0: 0.Google Scholar
Zheng, X, Al-Hayek, Y, Cummins, C et al. Body size and tube voltage dependent corrections for Hounsfield Unit in medical X - ray computed tomography : theory and experiments. Sci Rep 2020; 10: 1–10. doi: 10.1038/s41598-020-72707-y CrossRefGoogle Scholar
Afifi, M B, Abdelrazek, A, Deiab, N A, Abd El-Hafez, A I, El-Farrash, A H. The effects of CT x-ray tube voltage and current variations on the relative electron density (RED) and CT number conversion curves. J Radiat Res Appl Sci 2020; 13: 1–11.Google Scholar
Davis, A T, Muscat, S, Palmer, AL et al. Radiation dosimetry changes in radiotherapy treatment plans for adult patients arising from the selection of the CT image reconstruction kernel. BJR|Open 2019; 1: 20190023.CrossRefGoogle ScholarPubMed
Lillicrap, S C. Physics aspects of quality control in radiotherapy (Report no. 81). Phys Med Biol 2000; 45: 815.CrossRefGoogle Scholar
Kirwin, S, Langmack, K, Nightingale, A. Effect of CT reconstruction kernel and post-processing filter on Hounsfield number constancy in radiotherapy treatment planning. Inst Phys Eng Med SCOPE 2004. https://ctug.org.uk/meet04-10-14/recon_paramters_ct_number.pdf Google Scholar
Ketcham, R A, Hanna, R D. Beam hardening correction for X-ray computed tomography of heterogeneous natural materials. Comput Geosci 2014; 67: 49–61.CrossRefGoogle Scholar
Figure 0

Figure 1.The distribution of tissue equivalent inserts in the phantom and their REDs (a) CIRS M062 and (b) CT image of CIRS M062.

Figure 1

Table 1.Main reconstruction filters of Hitachi Supria 16-slice scanner

Figure 2

Figure 2.CT-RED calibration curves of F12 versus F32 at 80 kVp (a) without BHC and (b) with BHC.

Figure 3

Figure 3.CT-RED calibration curves of F12 versus F32 at 140 kVp (a) without BHC and (b) with BHC.

Figure 4

Figure 4.CT-RED calibration curves of F12 versus F32 at 100 kVp (a) without BHC and (b) with BHC.

Figure 5

Figure 5.CT-RED calibration curves of F12 versus F32 at 120 kVp (a) without BHC and (b) with BHC.

Figure 6

Figure 6.HU variation between F32 and F12 without BHC according to different kVp settings.

Figure 7

Figure 7.HU variation between F32 and F12 with BHC according to different kVp settings.