Rationale and Objectives
This study aimed to evaluate the effects of iterative reconstruction (IR) algorithms on computer-assisted detection (CAD) software for lung nodules in ultra-low-dose computed tomography (ULD-CT) for lung cancer screening.
Materials and Methods
We selected 85 subjects who underwent both a low-dose CT (LD-CT) scan and an additional ULD-CT scan in our lung cancer screening program for high-risk populations. The LD-CT scans were reconstructed with filtered back projection (FBP; LD-FBP). The ULD-CT scans were reconstructed with FBP (ULD-FBP), adaptive iterative dose reduction 3D (AIDR 3D; ULD-AIDR 3D), and forward projected model-based IR solution (FIRST; ULD-FIRST). CAD software for lung nodules was applied to each image dataset, and the performance of the CAD software was compared among the different IR algorithms.
Results
The mean volume CT dose indexes were 3.02 mGy (LD-CT) and 0.30 mGy (ULD-CT). For overall nodules, the sensitivities of CAD software at 3.0 false positives per case were 78.7% (LD-FBP), 9.3% (ULD-FBP), 69.4% (ULD-AIDR 3D), and 77.8% (ULD-FIRST). Statistical analysis showed that the sensitivities of ULD-AIDR 3D and ULD-FIRST were significantly higher than that of ULD-FBP ( P < .001).
Conclusions
The performance of CAD software in ULD-CT was improved by using IR algorithms. In particular, the performance of CAD in ULD-FIRST was almost equivalent to that in LD-FBP.
Introduction
The results from the National Lung Screening Trial showed that lung cancer screening with low-dose computed tomography (LD-CT) compared to chest X-ray screening significantly reduced lung cancer mortality among heavy smokers . Recently, several guidelines have recommended annual LD-CT screening for lung cancer in high-risk populations .
If lung cancer screening using LD-CT is to become routinely available, it is desirable to further reduce the radiation dose. Several techniques have been proposed to reduce the radiation dose , and, recently, iterative reconstruction (IR) algorithms have become clinically applicable in decreasing the radiation dose while maintaining diagnostic image quality . There are two types of IR algorithm: hybrid IR and model-based IR. Hybrid IR algorithms involve a combination of IR and filtered back projection (FBP). In contrast, model-based IR algorithms are fully iterative and use models of the acquisition process, image statistics, and system geometry. Table 1 shows a list of the major commercially available IR algorithms. A number of studies have investigated the detectability of lung nodules in LD-CT or ultra-low-dose CT (ULD-CT) reconstructed with IR algorithms .
TABLE 1
Major Commercially Available IR Algorithms
Abbreviation Vendor Full Name Type ASIR GE Adaptive Statistical Iterative Reconstruction Hybrid Veo GE Veo Model-based iDose 4 Philips iDose 4 Hybrid IMR Philips Iterative Model Reconstruction Model-based SAFIRE Siemens Sinogram-Affirmed Iterative Reconstruction Hybrid AIDR 3D Toshiba Adaptive Iterative Dose Reduction 3D Hybrid FIRST Toshiba Forward projected model-based Iterative Reconstruction SoluTion Model-based Intelli IP Hitachi Intelli IP Hybrid
IR, iterative reconstruction.
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Materials and Methods
Subjects
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TABLE 2
Types and Numbers of Nodules
Nodule Type Number of Nodules Size (mm) Mean ± SD (Range) Overall 108 6.4 ± 3.0 (4–24) Solid 90 5.9 ± 2.3 (4–15) pGGN 12 7.5 ± 2.9 (4–14) PSN 6 10.3 ± 6.9 (5–24)
pGGN, pure ground-glass nodule; PSN, part-solid nodule; SD, standard deviation.
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CT Data Acquisition
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Image Reconstruction
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Radiation Dose Assessment
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CAD Software
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Data Analysis
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Results
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TABLE 3
Comparison of Radiation Doses for LD-CT and ULD-CT
LD-CT ULD-CT CTDI vol (mGy) 3.02 ± 0.10 0.30 ± 0.00 DLP (mGy⋅cm) 123.91 ± 8.48 12.37 ± 0.85
CTDI vol , volume CT dose index; DLP, dose-length product; LD-CT, low-dose CT; ULD-CT, ultra-low-dose CT.
Data are mean ± standard deviation.
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TABLE 4
Sensitivity Data at 3.0 FPs/Case for Each Nodule Type and Nodule Size
Sensitivity at 3.0 FPs/Case (%) Nodule Type Nodule Size (mm) Overall Solid Subsolid 4–5 6–24 (a) LD-FBP 78.7 83.3 55.6 82.7 75.0 (b) ULD-FBP 9.3 10.0 5.6 11.5 7.1 (c) ULD-AIDR 3D 69.4 75.6 38.9 75.0 64.3 (d) ULD-FIRST 77.8 83.3 50.0 82.7 73.2 Differences between each group ( P values) (a) vs. (b)<.001<.001.004<.001<.001 (a) vs. (c) .041 .143 .250<.001 .109 (a) vs. (d) 1.000 1.000 1.000 .344 1.000 (b) vs. (c)<.001<.001 .031<.001<.001 (b) vs. (d)<.001<.001.0078<.001<.001 (c) vs. (d) .011 .039 .500 .125 .125
FP, false positive; LD-FBP, LD-CT scans reconstructed with filtered back projection; pGGN, pure ground-glass nodule; PSN, part-solid nodule; ULD-AIDR 3D, ULD-CT scans reconstructed with adaptive iterative dose reduction 3D; ULD-FBP, ULD-CT scans reconstructed with filtered back projection; ULD-FIRST, ULD-CT scans reconstructed with forward projected model-based IR solution.
Subsolid includes both pGGN and PSN. A P value less than .0083 was considered as significant (bold).
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Discussion
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Acknowledgments
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References
1. Aberle D.R., Adams A.M., Berg C.D., et. al.: Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011; 365: pp. 395-409.
2. Goulart B.H., Ramsey S.D.: Moving beyond the national lung screening trial: discussing strategies for implementation of lung cancer screening programs. Oncologist 2013; 18: pp. 941-946.
3. Kubo T., Ohno Y., Kauczor H.U., et. al.: Radiation dose reduction in chest CT—review of available options. Eur J Radiol 2014; 83: pp. 1953-1961.
4. Willemink M.J., de Jong P.A., Leiner T., et. al.: Iterative reconstruction techniques for computed tomography part 1: technical principles. Eur Radiol 2013; 23: pp. 1623-1631.
5. Willemink M.J., Leiner T., de Jong P.A., et. al.: Iterative reconstruction techniques for computed tomography part 2: initial results in dose reduction and image quality. Eur Radiol 2013; 23: pp. 1632-1642.
6. Geyer L.L., Schoepf U.J., Meinel F.G., et. al.: State of the art: iterative CT reconstruction techniques. Radiology 2015; 276: pp. 339-357.
7. den Harder A.M., Willemink M.J., de Ruiter Q.M., et. al.: Achievable dose reduction using iterative reconstruction for chest computed tomography: a systematic review. Eur J Radiol 2015; 84: pp. 2307-2313.
8. Yamada Y., Jinzaki M., Tanami Y., et. al.: Model-based iterative reconstruction technique for ultralow-dose computed tomography of the lung: a pilot study. Invest Radiol 2012; 47: pp. 482-489.
9. Neroladaki A., Botsikas D., Boudabbous S., et. al.: Computed tomography of the chest with model-based iterative reconstruction using a radiation exposure similar to chest X-ray examination: preliminary observations. Eur Radiol 2013; 23: pp. 360-366.
10. Katsura M., Matsuda I., Akahane M., et. al.: Model-based iterative reconstruction technique for ultralow-dose chest CT: comparison of pulmonary nodule detectability with the adaptive statistical iterative reconstruction technique. Invest Radiol 2013; 48: pp. 206-212.
11. Hashemi S., Mehrez H., Cobbold R.S., et. al.: Optimal image reconstruction for detection and characterization of small pulmonary nodules during low-dose CT. Eur Radiol 2014; 24: pp. 1239-1250.
12. Yoon H.J., Chung M.J., Hwang H.S., et. al.: Adaptive statistical iterative reconstruction-applied ultra-low-dose CT with radiography-comparable radiation dose: usefulness for lung nodule detection. Korean J Radiol 2015; 16: pp. 1132-1141.
13. Martini K., Higashigaito K., Barth B.K., et. al.: Ultralow-dose CT with tin-filtration for detection of solid and sub-solid pulmonary nodules: a phantom study. Br J Radiol 2015; 88: pp. 20150389.
14. Aberle D.R., Henschke C.I., McLoud T.C., et. al.: Expert opinion: barriers to CT screening for lung cancer. J Thorac Imaging 2012; 27: pp. 208.
15. Giger M.L., Chan H.P., Boone J.: Anniversary paper: history and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. Med Phys 2008; 35: pp. 5799-5820.
16. Das M., Muhlenbruch G., Mahnken A.H., et. al.: Small pulmonary nodules: effect of two computer-aided detection systems on radiologist performance. Radiology 2006; 241: pp. 564-571.
17. Sahiner B., Chan H.P., Hadjiiski L.M., et. al.: Effect of CAD on radiologists’ detection of lung nodules on thoracic CT scans: analysis of an observer performance study by nodule size. Acad Radiol 2009; 16: pp. 1518-1530.
18. Zhao Y., de Bock G.H., Vliegenthart R., et. al.: Performance of computer-aided detection of pulmonary nodules in low-dose CT: comparison with double reading by nodule volume. Eur Radiol 2012; 22: pp. 2076-2084.
19. Godoy M.C., Kim T.J., White C.S., et. al.: Benefit of computer-aided detection analysis for the detection of subsolid and solid lung nodules on thin- and thick-section CT. AJR Am J Roentgenol 2013; 200: pp. 74-83.
20. Christe A., Leidolt L., Huber A., et. al.: Lung cancer screening with CT: evaluation of radiologists and different computer assisted detection software (CAD) as first and second readers for lung nodule detection at different dose levels. Eur J Radiol 2013; 82: pp. e873-e878.
21. Yanagawa M., Honda O., Kikuyama A., et. al.: Pulmonary nodules: effect of adaptive statistical iterative reconstruction (ASIR) technique on performance of a computer-aided detection (CAD) system-comparison of performance between different-dose CT scans. Eur J Radiol 2012; 81: pp. 2877-2886.
22. Wielpütz M.O., Wroblewski J., Lederlin M., et. al.: Computer-aided detection of artificial pulmonary nodules using an ex vivo lung phantom: influence of exposure parameters and iterative reconstruction. Eur J Radiol 2015; 84: pp. 1005-1011.
23. Den Harder A.M., Willemink M.J., van Hamersvelt R.W., et. al.: Effect of radiation dose reduction and iterative reconstruction on computer-aided detection of pulmonary nodules: intra-individual comparison. Eur J Radiol 2016; 85: pp. 346-351.
24. Huber A., Landau J., Ebner L., et. al.: Performance of ultralow-dose CT with iterative reconstruction in lung cancer screening: limiting radiation exposure to the equivalent of conventional chest X-ray imaging. Eur Radiol 2016; 26: pp. 3643-3652.
25. Ebner L., Roos J.E., Christensen J.D., et. al.: Maximum-intensity-projection and computer-aided detection algorithms as stand-alone reader devices in lung cancer screening using different dose levels and reconstruction kernels. AJR Am J Roentgenol 2016; 207: pp. 282-288.
26. National Comprehensive Cancer Network : NCCN guidelines for lung cancer screening. Available at: http://www.nccn.org/professionals/physician_gls/pdf/lung_screening.pdf Accessed November 11, 2016
27. Nomura Y., Nemoto M., Masutani Y., et. al.: Reduction of false positives at vessel bifurcations in computerized detection of lung nodules. J Biomed Graph Comput 2014; 4: pp. 36-46.
28. Dorai C., Jain A.K.: COSMOS—a representation scheme for 3D free-form objects. IEEE Trans Pattern Anal Mach Intell 1997; 19: pp. 1115-1130.
29. Nomura Y., Hayashi N., Masutani Y., et. al.: CIRCUS: an MDA platform for clinical image analysis in hospitals. Trans Mass Data Anal Images Signals 2010; 2: pp. 112-127.
30. Shi D.: Alternative noise map estimation methods for CT images.2013.pp. 866835.
31. Li Q., Li F., Doi K.: Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier. Acad Radiol 2008; 15: pp. 165-175.
32. Messay T., Hardie R.C., Rogers S.K.: A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med Image Anal 2010; 14: pp. 390-406.
33. Tan M., Deklerck R., Jansen B., et. al.: A novel computer-aided lung nodule detection system for CT images. Med Phys 2011; 38: pp. 5630-5645.
34. Teramoto A., Fujita H.: Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter. Int J Comput Assist Radiol Surg 2013; 8: pp. 193-205.
35. Wang B., Tian X., Wang Q., et. al.: Pulmonary nodule detection in CT images based on shape constraint CV model. Med Phys 2015; 42: pp. 1241-1254.
36. Li H., Giger M.L., Yuan Y., et. al.: Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset. Acad Radiol 2008; 15: pp. 1437-1545.
37. Nomura Y., Masutani Y., Miki S., et. al.: Training strategy for performance improvement in computer-assisted detection of lesions: based on multi-institutional study in teleradiology environment.First International Symposium on Computing and Networking (CANDAR 2013).2013.pp. 320-323.
38. Solomon J., Mileto A., Nelson R.C., et. al.: Quantitative features of liver lesions, lung nodules, and renal stones at multi-detector row CT examinations: dependency on radiation dose and reconstruction algorithm. Radiology 2016; 279: pp. 185-194.
39. Lee E.S., Kim S.H., Im J.P., et. al.: Effect of different reconstruction algorithms on computer-aided diagnosis (CAD) performance in ultra-low dose CT colonography. Eur J Radiol 2015; 84: pp. 547-554.
40. Lahiji K., Kligerman S., Jeudy J., et. al.: Improved accuracy of pulmonary embolism computer-aided detection using iterative reconstruction compared with filtered back projection. AJR Am J Roentgenol 2014; 203: pp. 763-771.
41. Wood D.E., Kazerooni E., Baum S.L., et. al.: Lung cancer screening, version 1.2015: featured updates to the NCCN guidelines. J Natl Compr Canc Netw 2015; 13: pp. 23-34.