banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
A quality assurance framework for the fully automated and objective evaluation of image quality in cone-beam computed tomography
Rent this article for
Access full text Article
1. L. A. Feldkamp, L. C. Davis, and J. W. Kress, “Practical cone-beam algorithm,” J. Opt. Soc. Am. A 1, 612619 (1984).
2. R. Fahrig, A. J. Fox, S. Lownie, and D. W. Holdsworth, “Use of a C-arm system to generate true three-dimensional computed rotational angiograms: Preliminary in vitro and in vivo results,” Am. J. Neuroradiol. 18, 15071514 (1997).
3. L. E. Antonuk, K.-W. Jee, Y. El-Mohri, M. Maolinbay, S. Nassif, X. Rong, Q. Zhao, J. H. Siewerdsen, R. A. Street, and K. S. Shah, “Strategies to improve the signal and noise performance of active matrix, flat-panel imagers for diagnostic x-ray applications,” Med. Phys. 27, 289306 (2000).
4. D. A. Jaffray and J. H. Siewerdsen, “Cone-beam computed tomography with a flat-panel imager: Initial performance characterization,” Med. Phys. 27, 13111323 (2000).
5. W. A. Kalender and Y. Kyriakou, “Flat-detector computed tomography (FD-CT),” Eur. Radiol. 17, 27672779 (2007).
6. W. A. Kalender, Computed Tomography: Fundamentals, System Technology, Image Quality, Applications, 3rd ed. (Publicis, Erlangen, 2011).
7. A. Suomalainen, T. Kiljunen, Y. Käser, J. Peltola, and M. Kortesniemi, “Dosimetry and image quality of four dental cone beam computed tomography scanners compared with multislice computed tomography scanners,” Dentomaxillofac. Radiol. 38, 367378 (2009).
8. Y. Kyriakou, G. Richter, A. Dörfler, and W. A. Kalender, “Neuroradiologic applications with routine C-arm flat panel detector CT: Evaluation of patient dose measurements,” Am. J. Neuroradiol. 29, 19301936 (2008).
9. J. B. Ludlow and M. Ivanovic, “Comparative dosimetry of dental CBCT devices and 64-slice CT for oral and maxillofacial radiology,” Oral Surg. Oral Med. Oral Pathol. Oral Radiol. Endod. 106, 106114 (2008).
10. J. H. Siewerdsen, D. J. Moseley, S. Burch, S. K. Bisland, A. Bogaards, B. C. Wilson, and D. A. Jaffray, “Volume CT with a flat-panel detector on a mobile, isocentric C-arm: Pre-clinical investigation in guidance of minimally invasive surgery,” Med. Phys. 32, 241254 (2005).
11. G. Eggers, J. Klein, T. Welzel, and J. Mühling, “Geometric accuracy of digital volume tomography and conventional computed tomography,” Br. J. Oral Maxillofac. Surg. 46, 639644 (2008).
12. Y. Arai, E. Tammisalo, K. Iwai, K. Hashimoto, and K. Shinoda, “Development of a compact computed tomographic apparatus for dental use,” Dentomaxillofac. Radiol. 28, 245248 (1999).
13. A. Doerfler, I. Wanke, T. Egelhof, U. Dietrich, S. Asgari, D. Stolke, and M. Forsting, “Aneurysmal rupture during embolization with Guglielmi detachable coils: Causes, management, and outcome,” Am. J. Neuroradiol. 22, 18251832 (2001).
14. N. S. Heran, J. K. Song, K. Namba, W. Smith, Y. Niimi, and A. Berenstein, “The utility of DynaCT in neuroendovascular procedures,” Am. J. Neuroradiol. 27, 330332 (2006).
15. D. A. Jaffray, J. H. Siewerdsen, J. W. Wong, and A. A. Martinez, “Flat-panel cone-beam computed tomography for image-guided radiation therapy,” Int. J. Radiat. Oncol., Biol., Phys. 53, 13371349 (2002).
16. C. Holberg, S. Steinhäuser, P. Geis, and I. Rudzki-Janson, “Cone-beam computed tomography in orthodontics: Benefits and limitations,” J. Orofac. Orthop. 66, 434444 (2005).
17. J. M. Boone, T. R. Nelson, K. K. Lindfors, and J. A. Seibert, “Dedicated breast CT: Radiation dose and image quality evaluation,” Radiology 221, 657667 (2001).
18. A. O’Connell, D. L. Conover, Y. Zhang, P. Seifert, W. Logan-Young, C. F. L. Lin, L. Sahler, and R. Ning, “Cone-beam CT for breast imaging: Radiation dose, breast coverage, and image quality,” Am. J. Roentgenol. 195, 496509 (2010).
19. W. A. Kalender, M. Beister, J. M. Boone, D. Kolditz, S. V. Vollmar, and M. C. C. Weigel, “High-resolution spiral CT of the breast at very low dose: Concept and feasibility considerations,” Eur. Radiol. 22, 18 (2012).
20. J. B. Pialat, N. Vilayphiou, S. Boutroy, P. J. Gouttenoire, E. Sornay-Rendu, R. Chapurlat, and F. Peyrin, “Local topological analysis at the distal radius by HR-pQCT: Application to in vivo bone microarchitecture and fracture assessment in the OFELY study,” Bone 51, 362368 (2012).
21. D. Töpfer, S. Finzel, O. Museyko, G. Schett, and K. Engelke, “Segmentation and quantification of bone erosions in high-resolution peripheral quantitative computed tomography datasets of the metacarpophalangeal joints of patients with rheumatoid arthritis,” Rheumatology 53, 6571 (2014).
22. W. Ross, D. D. Cody, and J. D. Hazle, “Design and performance characteristics of a digital flat-panel computed tomography system,” Med. Phys. 33, 18881901 (2006).
23. R. Gupta, M. Grasruck, C. Suess, S. H. Bartling, B. Schmidt, K. Stierstorfer, S. Popescu, T. Brady, and T. Flohr, “Ultra-high resolution flat-panel volume CT: Fundamental principles, design architecture, and system characterization,” Eur. Radiol. 16, 11911205 (2006).
24.Health Protection Agency, HPA-RPD-065: Recommendations for the Design of X-ray Facilities and Quality Assurance of Dental Cone Beam CT (Computed Tomography) systems (Health Protection Agency, Chilton, 2010).
25.Health Protection Agency, HPA-CRCE-010: Guidance on the Safe Use of Dental Cone Beam CT (Computed Tomography) Equipment (Health Protection Agency, Chilton, 2010).
26. R. Schulze, S. Hassfeld, D. Schulze, O. Ahlers, W. Freesmeyer, K. Ackermann, E. Frank, H. Terheyden, and U. Hirschfelder, “S1-Empfehlung: Dentale Volumentomographie (DVT),” Dtsch. Zahnärztl. Z. 64, 490496 (2009).
27. Deutsches Institut für Normung (DIN), “Sicherung der Bildqualität in röntgendiagnostischen Betrieben - Teil 161: Abnahmeprüfung nach RöV an zahnmedizinischen Röntgeneinrichtungen zur digitalen Volumentomographie,” DIN Report Nr. 6868-161 (DIN, Berlin, 2013).
28.SEDENTEXCT Guideline Development Panel, “Radiation protection: Cone beam CT for dental and maxillofacial radiology. Evidence based guidelines,” A report prepared by the SEDENTEXCT project (2011) (available URL: http://www.sedentexct.eu).
29. R. Fahrig, R. Dixon, T. Payne, R. L. Morin, A. Ganguly, and N. Strobel, “Dose and image quality for a cone-beam C-arm CT system,” Med. Phys. 33, 45414550 (2006).
30. S. Yoo, G.-Y. Kim, R. Hammoud, E. Elder, T. Pawlicki, H. Guan, T. Fox, G. Luxton, F.-F. Yin, and P. Munro, “A quality assurance program for the on-board imager,” Med. Phys. 33, 44314447 (2006).
31. J. P. Bissonnette, D. J. Moseley, and D. A. Jaffray, “A quality assurance program for image quality of cone-beam CT guidance in radiation therapy,” Med. Phys. 35, 18071815 (2008).
32. J.-P. Bissonnette, P. A. Balter, L. Dong, K. M. Langen, D. M. Lovelock, M. Miften, D. J. Moseley, J. Pouliot, J.-J. Sonke, and S. Yoo, “Quality assurance for image-guided radiation therapy utilizing CT-based technologies: A report of the AAPM TG-179,” Med. Phys. 39, 19461963 (2012).
33. J. Vassileva and D. Stoyanov, “Quality control and patient dosimetry in dental cone beam CT,” Radiat. Prot. Dosimetry 139, 310312 (2010).
34. R. Pauwels, H. Stamatakis, G. Manousaridis, A. Walker, K. Michielsen, H. Bosmans, R. Bogaerts, R. Jacobs, K. Horner, and K. Tsiklakis, “Development and applicability of a quality control phantom for dental cone-beam CT,” J. Appl. Clin. Med. Phys. 12, 245260 (2011).
35. C. H. McCollough, M. R. Bruesewitz, M. F. McNitt-Gray, K. Bush, T. Ruckdeschel, J. T. Payne, J. A. Brink, and R. K. Zeman, “The phantom portion of the American College of Radiology (ACR) computed tomography (CT) accreditation program: Practical tips, artifact examples, and pitfalls to avoid,” Med. Phys. 31, 24232442 (2004).
36. International Electrotechnical Commission (IEC), “Amendment 1 – Medical electrical equipment – Part 2-44: Particular requirements for the basic safety and essential performance of X-ray equipment for computed tomography,” IEC Report No. 60601-2-44-A1 (IEC, Geneva, 2012).
37. International Electrotechnical Commission (IEC), “Evaluation and routine testing in medical imaging departments – Part 3-5: Acceptance tests – Imaging performance of computed tomography X-ray equipment,” IEC Report No. 61223-3-5 (IEC, Geneva, 2004).
38. International Electrotechnical Commission (IEC), “Evaluation and routine testing in medical imaging departments – Part 2-6: Constancy tests – Imaging performance of computed tomography X-ray equipment,” IEC Report No. 61223-2-6 (IEC, Geneva, 2006).
39. J. Baek and N. J. Pelc, “Local and global 3D noise power spectrum in cone-beam CT system with FDK reconstruction,” Med. Phys. 38, 21222131 (2011).
40. M. M. Thornton and M. J. Flynn, “Measurement of the spatial resolution of a clinical volumetric computed tomography scanner using a sphere phantom,” Proc. SPIE 6142, 61421Z (2006).
41. American Association of Physicists in Medicine (AAPM), “Specification and acceptance testing of computed tomography scanners,” AAPM Report No. 39 (AAPM, New York, 1993).
42. Y. Kyriakou, D. Kolditz, O. Langner, J. Krause, and W. A. Kalender, “Digital volume tomography (DVT) and multislice spiral CT (MSCT): An objective examination of dose and image quality,” Fortschr. Röntgenstr. 183, 144153 (2011).
43. P. Mah, T. E. Reeves, and W. D. McDavid, “Deriving Hounsfield units using grey levels in cone beam computed tomography,” Dentomaxillofac. Radiol. 39, 323335 (2010).
44. P. Sprawls, “AAPM tutorial. CT image detail and noise,” Radiographics 12, 10411046 (1992).
45. D. A. Chesler, S. J. Riederer, and N. J. Pelc, “Noise due to photon counting statistics in computed X-ray tomography,” J. Comput. Assist. Tomogr. 1, 6474 (1977).
46. K. E. Bennett and R. L. Byer, “Fan-beam-tomography noise theory,” J. Opt. Soc. Am. A 3, 624633 (1986).
47. Y. Zhang and R. Ning, “Investigation of image noise in cone-beam CT imaging due to photon counting statistics with the Feldkamp algorithm by computer simulations,” J. X-Ray Sci. Technol. 16, 143158 (2008).
48. M. Slaney and A. Kak, Principles of Computerized Tomographic Imaging, 1st ed. (IEEE, New York, NY, 1988).
49. S. J. Riederer, N. J. Pelc, and D. A. Chesler, “The noise power spectrum in computed x-ray tomography,” Phys. Med. Biol. 23, 446454 (1978).
50. R. F. Wagner, D. G. Brown, and M. S. Pastel, “Application of information theory to the assessment of computed tomography,” Med. Phys. 6, 8394 (1979).
51. J. H. Siewerdsen, I. A. Cunningham, and D. A. Jaffray, “A framework for noise-power spectrum analysis of multidimensional images,” Med. Phys. 29, 26552671 (2002).
52. D. J. Tward and J. H. Siewerdsen, “Cascaded systems analysis of the 3D noise transfer characteristics of flat-panel cone-beam CT,” Med. Phys. 35, 55105529 (2008).
53. M. F. Kijewski and P. F. Judy, “The noise power spectrum of CT images,” Phys. Med. Biol. 32, 565575 (1987).
54. D. J. Tward and J. H. Siewerdsen, “Noise aliasing and the 3D NEQ of flat-panel cone-beam CT: Effect of 2D/3D apertures and sampling,” Med. Phys. 36, 38303843 (2009).
55. P. Welch, “The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms,” IEEE Trans. Audio Electroac. 15, 7073 (1967).
56. C. E. Metz and K. Doi, “Transfer function analysis of radiographic imaging systems,” Phys. Med. Biol. 24, 10791106 (1979).
57. I. A. Cunningham and A. Fenster, “A method for modulation transfer function determination from edge profiles with correction for finite-element differentiation,” Med. Phys. 14, 533537 (1987).
58. J. M. Boone, “Determination of the presampled MTF in computed tomography,” Med. Phys. 28, 356360 (2001).
59. A. L. C. Kwan, J. M. Boone, K. Yang, and S. Y. Huang, “Evaluation of the spatial resolution characteristics of a cone-beam breast CT scanner,” Med. Phys. 34, 275281 (2007).
60. A. Katsumata, A. Hirukawa, M. Noujeim, S. Okumura, M. Naitoh, M. Fujishita, E. Ariji, and R. P. Langlais, “Image artifact in dental cone-beam CT,” Oral Surg. Oral Med. Oral Pathol. Oral Radiol. Endod. 101, 652657 (2006).
61. F. G. Draenert, E. Coppenrath, P. Herzog, S. Müller, and U. G. Mueller-Lisse, “Beam hardening artefacts occur in dental implant scans with the NewTom® cone beam CT but not with the dental 4-row multidetector CT,” Dentomaxillofac. Radiol. 36, 198203 (2007).
62. R. K. W. Schulze, D. Berndt, and B. d’Hoedt, “On cone-beam computed tomography artifacts induced by titanium implants,” Clin. Oral Impl. Res. 21, 100107 (2010).
63. J. F. Barrett and N. Keat, “Artifacts in CT: Recognition and avoidance,” Radiographics 24, 16791691 (2004).
64. G. H. Golub and C. Reinsch, “Singular value decomposition and least squares solutions,” Numer. Math. 14, 403420 (1970).
65. C. Suess, W. A. Kalender, and J. M. Coman, “New low-contrast resolution phantoms for computed tomography,” Med. Phys. 26, 296302 (1999).
66. C. Blendl, M. Fiebich, J. M. Voigt, M. Selbach, and C. Uphoff, “Investigation on the 3 D geometric accuracy and on the image quality (MTF, SNR and NPS) of volume tomography units (CT, CBCT and DVT),” Fortschr. Röntgenstr. 184, 2431 (2012).
67. K. M. Hanson, “Detectability in computed tomographic images,” Med. Phys. 6, 441451 (1979).
68. T. Nowak, M. Hupfer, F. Althoff, R. Brauweiler, F. Eisa, C. Steiding, and W. A. Kalender, “Time-delayed summation as a means of improving resolution on fast rotating computed tomography systems,” Med. Phys. 39, 22492260 (2012).
69. R. T. Droege and R. L. Morin, “A practical method to measure the MTF of CT scanners,” Med. Phys. 9, 758760 (1982).
70. J. Chindasombatjaroen, N. Kakimoto, S. Murakami, Y. Maeda, and S. Furukawa, “Quantitative analysis of metallic artifacts caused by dental metals: Comparison of cone-beam and multi-detector row CT scanners,” Oral Radiol. 27, 114120 (2011).
71. R. Pauwels, H. Stamatakis, H. Bosmans, R. Bogaerts, R. Jacobs, K. Horner and K. Tsiklakis, “Quantification of metal artifacts on cone beam computed tomography images,” Clin. Oral Implants Res. 24, 9499 (2013).


Image of FIG. 1.

Click to view

FIG. 1.

Design of the proposed QA phantom. A 3D rendered sketch of the modular test object (a) and the optional extension ring (b). (c)–(h) Ideal transverse and coronal views of section 1–5 ( = 0 a.u., = 1000 a.u.).

Image of FIG. 2.

Click to view

FIG. 2.

Scheme of the data ensemble extraction process in the noise-only images to estimate the global (a) and local (b) 2D NPS. The white-striped areas represent one of nine half-overlapped subregions (white rectangle) of size covered by the entire ROI (black dashed rectangle). For local spatially dependent 2D NPS synthesis (b), one central and eight peripheral ROIs positioned on a circular trajectory with radius were selected.

Image of FIG. 3.

Click to view

FIG. 3.

Schematic representation of the ESF profiles in a spherical VOI (outer spherical grid) for 3D MTF calculations with a sphere phantom (inner spherical grid). (a) Collection of data sequences to determine the representative response functions in - (right rear-facing profiles), - (left rear-facing profiles), and -direction (upward-facing profiles). (b) Ensemble of transverse ESF profiles to compute .

Image of FIG. 4.

Click to view

FIG. 4.

Schematic workflow of the detection method for the modular IQ phantom. The overall description of test object in 3D spatial domain was realized by calculating the associated local -, -, and -orientation ( , , and ) and the position offset .

Image of FIG. 5.

Click to view

FIG. 5.

Fourier-based assessment of the global noise behavior in the homogeneous phantom section 3. (a) and (b) Estimated 2D NPS by performing the measurements without (w|o) and with (w) the optional extension ring. Both spectra in the νν-plane are shown up to the Nyquist sampling frequency and have an identical display window level and width [0, 1200]. (c) 1D NPS curves as determined by averaging radial profiles of the corresponding 2D spectra.

Image of FIG. 6.

Click to view

FIG. 6.

Fourier-based assessment of the spatially dependent noise behavior in the homogeneous phantom section 3 without using the optional extension ring. (a)–(i) Estimated local 2D NPS data ( = 1, …, 9) by evaluating nine different regions as depicted in Fig. 2(b) . The spectra in the νν-plane are shown up to the Nyquist sampling frequency and have an identical display window level [0, 400].

Image of FIG. 7.

Click to view

FIG. 7.

Comparison of the two approaches to assess high-contrast spatial resolution. (a) and (b) Visual evaluation of the spatially selective PMMA-air resolution pattern for the dental CBCT system. The limiting spatial resolution in local - (a) and -direction (b) is indicated by an arrow. Transverse and coronal views of phantom section 4 are identically windowed ( = 0 a.u., = 1000 a.u.). (c) Representative MTFs in local - and -direction of the dental CBCT system are presented.

Image of FIG. 8.

Click to view

FIG. 8.

Artifacts caused by using two metallic inserts in phantom section 5. The window center and width of the transverse image from the dental CBCT system are 0 and 1000 a.u., respectively.

Image of FIG. 9.

Click to view

FIG. 9.

Results of the fully automated performance evaluation of the clinical dental CBCT system. Essential IQ parameters were assessed objectively in terms of uniformity (a), CT value linearity (b), image noise (c), CNR for the HA100 insert (d), 3D spatial resolution (e), and artifact behavior (f). In each subfigure, the horizontal axis denotes the period of measurement from August 2012 till January 2013.


Generic image for table

Click to view


Differences in the mean voxel values and the corresponding uniformity indices for one central ( ) and four peripheral ( , with = 1, …, 4) circular ROIs positioned in the homogeneous phantom section 3. The volume data set was acquired on the dental CBCT system .

Generic image for table

Click to view


Mean voxel values of the inserts in phantom section 1 using cylindrical VOIs to evaluate CT value linearity. The experiment was performed for a tube voltage of 120 kV on the dental CBCT system.

Generic image for table

Click to view


Image noise in the homogeneous phantom section 3 employing both approaches the standard deviation and the square root of the 0D NPS. The measurements were executed without (w|o) and with (w) the optional extension ring on the dental CBCT system.

Generic image for table

Click to view


Percentage ratio of the 3D spatial resolution at the phantom periphery to the center.

Generic image for table

Click to view


Standard deviation of the measured data as a percentage of the corresponding averaged IQ parameter values. For the dental CBCT system, the QA examinations were repeated 50 times without any displacement of the phantom.


Article metrics loading...



Thousands of cone-beam computed tomography (CBCT) scanners for vascular, maxillofacial, neurological, and body imaging are in clinical use today, but there is no consensus on uniform acceptance and constancy testing for image quality (IQ) and dose yet. The authors developed a quality assurance (QA) framework for fully automated and time-efficient performance evaluation of these systems. In addition, the dependence of objective Fourier-based IQ metrics on direction and position in 3D volumes was investigated for CBCT.

The authors designed a dedicated QA phantom 10 cm in length consisting of five compartments, each with a diameter of 10 cm, and an optional extension ring 16 cm in diameter. A homogeneous section of water-equivalent material allows measuring CT value accuracy, image noise and uniformity, and multidimensional global and local noise power spectra (NPS). For the quantitative determination of 3D high-contrast spatial resolution, the modulation transfer function (MTF) of centrally and peripherally positioned aluminum spheres was computed from edge profiles. Additional in-plane and axial resolution patterns were used to assess resolution qualitatively. The characterization of low-contrast detectability as well as CT value linearity and artifact behavior was tested by utilizing sections with soft-tissue-equivalent and metallic inserts. For an automated QA procedure, a phantom detection algorithm was implemented. All tests used in the dedicated QA program were initially verified in simulation studies and experimentally confirmed on a clinical dental CBCT system.

The automated IQ evaluation of volume data sets of the dental CBCT system was achieved with the proposed phantom requiring only one scan for the determination of all desired parameters. Typically, less than 5 min were needed for phantom set-up, scanning, and data analysis. Quantitative evaluation of system performance over time by comparison to previous examinations was also verified. The maximum percentage interscan variation of repeated measurements was less than 4% and 1.7% on average for all investigated quality criteria. The NPS-based image noise differed by less than 5% from the conventional standard deviation approach and spatially selective 10% MTF values were well comparable to subjective results obtained with 3D resolution pattern. Determining only transverse spatial resolution and global noise behavior in the central field of measurement turned out to be insufficient.

The proposed framework transfers QA routines employed in conventional CT in an advanced version to CBCT for fully automated and time-efficient evaluation of technical equipment. With the modular phantom design, a routine as well as an expert version for assessing IQ is provided. The QA program can be used for arbitrary CT units to evaluate 3D imaging characteristics automatically.


Full text loading...

This is a required field
Please enter a valid email address
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: A quality assurance framework for the fully automated and objective evaluation of image quality in cone-beam computed tomography