Rationale and Objectives
The aim of this study was to investigate variations in image noise and contrast using automatic exposure control (AEC) and different tube voltages on nonenhanced and iodine-enhanced hepatic computed tomography.
Materials and Methods
Nonenhanced and iodine-enhanced simulated liver phantoms and AEC were used. Tube current was automatically adjusted with the noise index. Two types of assessments were performed: at a fixed noise index of 10 Hounsfield units and at different noise indexes, keeping the same contrast-to-noise ratio at different tube voltages (100, 120, and 130 kV). Image noise was measured, and contrast between the computed tomographic number of the simulated liver and nodule was computed.
Results
At a fixed noise index, image noise on iodine-enhanced images was 10% to 13% higher than on nonenhanced images at the same tube voltage. At 130 and 100 kV, contrast was 33.86 and 46.90 Hounsfield units, respectively, and image noise was almost the same. Contrast-to-noise ratios at 100, 120, and 130 kV were 3.31, 3.22, and 3.37, respectively, and volume computed tomographic dose index fell from 22.94 to 12.49 mGy with decreasing tube voltage.
Conclusions
With AEC, image noise on iodine-enhanced images was higher than on nonenhanced images despite identical noise index settings. As tube voltage decreased, contrast on iodine-enhanced images increased. Considering noise index and contrast variations at different tube voltages, the optimal use of AEC on iodine-enhanced computed tomography facilitates a reduction in x-ray tube output while maintaining contrast-to-noise ratio.
Automatic exposure control (AEC) adjusts tube current according to the size and attenuation of the body region being scanned to obtain constant image quality with lower radiation dose . With AEC, computed tomographic (CT) projection radiographs (scanograms) obtained from either the anteroposterior or lateral direction are used to estimate patient-specific attenuations , and these values are then applied to calculate the necessary tube current for each projection to maintain image noise and decrease x-ray tube output. The noise index is determined from the nonenhanced projection radiograph for both iodine-enhanced and nonenhanced scans. However, x-ray absorption is different in scan areas that do or do not contain iodinated enhancing material . Therefore, image noise on iodine-enhanced CT images is higher than on nonenhanced images when the same x-ray tube output is applied. This may lead to the deterioration of image quality. In addition, contrast between the human liver and a nodule increases with decreasing tube voltage because of the effect of iodine attenuation . Because the mean photon energy moves closer to the k-absorption edge of iodine, the photoelectric effect is increased, and Compton scattering is decreased; consequently, the mean attenuation value of iodine is increased . Kalra et al stated that low-contrast areas on abdominal studies are severely affected by an increase in image noise. They also suggested that the effect of tube voltage on image quality is complex and affects both image noise and contrast. Therefore, it is important to properly regulate the setting of the noise index on AEC on iodine-enhanced or nonenhanced scanning while maintaining desired image quality. Yu et al documented variations in image quality under iodine-enhanced CT protocols with AEC. These were due to changes in phantom size or in tube voltage. However, they did not discuss attenuation in organ iodine enhancement. Not only the enhancement of nodules but also that of organs must be considered in simulation studies of iodine enhancement.
The purpose of our study was to quantitatively evaluate image quality in terms of image noise and contrast-to-noise ratio (CNR) and to provide iodine-enhanced CT protocols for optimizing x-ray tube output with AEC. We produced iodine-enhanced and nonenhanced liver phantoms that included a simulated nodule to mimic plain and arterial-phase hepatic CT scans and investigated variations in image noise and contrast. We applied AEC at different tube voltages on nonenhanced and iodine-enhanced hepatic computed tomography to identify optimal image noise settings.
Materials and methods
Phantom
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CT Scanning
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Quantitative Analysis
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|ROIn−ROIh|/SDh, |
ROI
n
−
ROI
h
|
/
SD
h
,
where ROI n and ROI h are the CT numbers of the ROIs placed in the simulated nodule and simulated liver, respectively, and SD h is the image noise in the simulated liver .
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Results
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Table 1
Mean Image Noise on Nonenhanced and Iodine-enhanced Images at Different Tube Voltages
Tube Voltage (kV) Image Noise (HU) Nonenhanced Iodine-enhanced 100 10.65 11.67 120 10.64 11.67 130 10.32 11.70
HU, hounsfield units.
The noise index for automatic exposure control was set at 10 HU.
Table 2
Mean CT Numbers and Contrast between the Simulated Liver and Nodule on Nonenhanced and Iodine-enhanced Images Acquired at a Fixed Noise Index of 10 HU
CT Number (HU) Tube Voltage (kV) Simulated Liver Simulated Nodule Contrast (HU) ∗ Nonenhanced 100 55.14 41.71 13.43 120 56.53 41.31 15.23 130 55.32 40.70 14.62 Iodine-enhanced 100 113.91 160.81 46.90 120 104.33 141.87 37.55 130 100.33 134.19 33.86
CT, computed tomographic; HU, hounsfield units.
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Table 3
Mean Image Noise, Contrast, and CNR for Iodine-enhanced Images at Different Noise Index Settings
Tube Voltage (kV) Noise Index (HU) Image Noise (HU) Contrast (HU) CNR 100 12.5 14.54 48.10 3.31 120 10.0 11.67 37.55 3.22 130 9.0 10.38 35.00 3.37
CNR, contrast-to-noise ratio; HU, hounsfield units.
Table 4
CTDI vol for Nonenhanced and Iodine-enhanced Scans Acquired at a Fixed Noise Index of 10 HU and Different Setting Values
Tube Voltage (kV) Noise Index (HU) CTDI vol (mGy) Nonenhanced 100 10.0 19.55 120 19.43 130 18.61 Iodine-enhanced 100 10.0 19.55 120 19.43 130 18.61 100 12.5 12.49 120 10.0 19.43 130 9.0 22.94
CTDI vol , volume computed tomographic dose index; HU, hounsfield units.
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Discussion
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Conclusions
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Acknowledgments
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Appendix
Relationship between Patient Size and Contrast
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References
1. Greess H., Wolf H., Baum U., et. al.: Dose reduction in computed tomography by attenuation-based on-line modulation of tube current: evaluation of six anatomical regions. Eur Radiol 2000; 10: pp. 391-394.
2. Kalender W.A., Wolf H., Suess C.: Dose reduction in CT by anatomically adapted tube current modulation. II. Phantom measurements. Med Phys 1999; 26: pp. 2248-2253.
3. Kalra M.K., Maher M.M., Toth T.L., et. al.: Techniques and applications of automatic tube current modulation for CT. Radiology 2004; 233: pp. 649-657.
4. McCollough C.H., Bruesewitz M.R., Kofler J.M.: CT dose reduction and dose management tools: overview of available options. Radiographics 2006; 26: pp. 503-512.
5. Mulkens T.H., Bellinck P., Baeyaert M., et. al.: Use of an automatic exposure control mechanism for dose optimization in multi-detector row CT examinations: clinical evaluation. Radiology 2005; 237: pp. 213-223.
6. Huda W., Scalzetti E.M., Levin G.: Technique factors and image quality as functions of patient weight at abdominal CT. Radiology 2000; 217: pp. 430-435.
7. Huda W.: Dose and image quality in CT. Pediatr Radiol 2002; 32: pp. 709-713.
8. Kalva S.P., Sahani D.V., Hahn P.F., et. al.: Using the K-edge to improve contrast conspicuity and to lower radiation dose with a 16-MDCT: a phantom and human study. J Comput Assist Tomogr 2006; 30: pp. 391-397.
9. Kalra M.K., Maher M.M., Toth T.L., et. al.: Strategies for CT radiation dose optimization. Radiology 2004; 230: pp. 619-628.
10. Yu L., Li H., Fletcher J.G., et. al.: Automatic selection of tube potential for radiation dose reduction in CT: a general strategy. Med Phys 2010; 37: pp. 234-243.
11. Kim T., Murakami T., Takahashi S., et. al.: Optimal phases of dynamic CT for detecting hepatocellular carcinoma: evaluation of unenhanced and triple-phase images. Abdom Imaging 1999; 24: pp. 473-480.
12. Nakayama Y., Awai K., Funama Y., et. al.: Abdominal CT with low tube voltage: preliminary observations about radiation dose, contrast enhancement, image quality, and noise. Radiology 2005; 237: pp. 945-951.
13. Awai K., Inoue M., Yagyu Y., et. al.: Moderate versus high concentration of contrast material for aortic and hepatic enhancement and tumor-to-liver contrast at multi-detector row CT. Radiology 2004; 233: pp. 682-688.
14. Awai K., Takada K., Onishi H., et. al.: Aortic and hepatic enhancement and tumor-to-liver contrast: analysis of the effect of different concentrations of contrast material at multi-detector row helical CT. Radiology 2002; 224: pp. 757-763.
15. Gupta A.K., Nelson R.C., Johnson G.A., et. al.: Optimization of eight-element multi-detector row helical CT technology for evaluation of the abdomen. Radiology 2003; 227: pp. 739-745.
16. Kalender W.A., Deak P., Kellermeier M., et. al.: Application- and patient size-dependent optimization of x-ray spectra for CT. Med Phys 2009; 36: pp. 993-1007.
17. Funama Y., Awai K., Nakayama Y., et. al.: Radiation dose reduction without degradation of low-contrast detectability at abdominal multisection CT with a low-tube voltage technique: phantom study. Radiology 2005; 237: pp. 905-910.
18. Marin D., Nelson R.C., Schindera S.T., et. al.: Low-tube-voltage, high-tube-current multidetector abdominal CT: improved image quality and decreased radiation dose with adaptive statistical iterative reconstruction algorithm—initial clinical experience. Radiology 2010; 254: pp. 145-153.
19. Schindera S.T., Nelson R.C., Mukundan S., et. al.: Hypervascular liver tumors: low tube voltage, high tube current multi-detector row CT for enhanced detection—phantom study. Radiology 2008; 246: pp. 125-132.
20. Marin D., Nelson R.C., Samei E., et. al.: Hypervascular liver tumors: low tube voltage, high tube current multidetector CT during late hepatic arterial phase for detection—initial clinical experience. Radiology 2009; 251: pp. 771-779.
21. Siegel M.J., Schmidt B., Bradley D., et. al.: Radiation dose and image quality in pediatric CT: effect of technical factors and phantom size and shape. Radiology 2004; 233: pp. 515-522.
22. Boone J.M., Geraghty E.M., Seibert J.A., et. al.: Dose reduction in pediatric CT: a rational approach. Radiology 2003; 228: pp. 352-360.