Skip to main content
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.
The full text of this article is not currently available.
1.S. Richard, G. Yadava, S. Murphy, D. Husarik, and E. Samei, “Towards task-based assessment of CT performance: System and object MTF across different reconstruction algorithms,” Med. Phys. 39, 41154122 (2012).
2.D. Marin, R. C. Nelson, S. T. Schindera, S. Richard, R. S. Youngblood, T. T. Yoshizumi, and E. Samei, “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 254, 145153 (2010).
3.P. Prakash, M. K. Kalra, A. K. Kambadakone, H. Pien, J. Hsieh, M. A. Blake, and D. V. Sahani, “Reducing abdominal CT radiation dose with adaptive statistical iterative reconstruction technique,” Invest. Radiol. 45, 202210 (2010).
4.J. B. Thibault, K. D. Sauer, C. A. Bouman, and J. Hsieh, “A three-dimensional statistical approach to improved image quality for multislice helical CT,” Med. Phys. 34, 45264544 (2007).
5.Z. Yu, J. B. Thibault, C. A. Bouman, K. D. Sauer, and J. A. Hsieh, “Fast model-based x-ray CT reconstruction using spatially nonhomogeneous ICD optimization,” IEEE Trans. Image Process. 20, 161175 (2011).
6.International Commission on Radiation Units and Measurements, “Medical imaging—The assessment of image quality,” ICRU Report No.54 (ICRU Publications, Bethesda, MD, 1996).
7.S. Richard and J. H. Siewerdsen, “Comparison of model and human observer performance for detection and discrimination tasks using dual-energy x-ray images,” Med. Phys. 35, 50435053 (2008).
8.S. Richard and E. Samei, “Quantitative breast tomosynthesis: From detectability to estimability,” Med. Phys. 37, 61576165 (2010).
9.S. Richard and E. Samei, “Quantitative imaging in breast tomosynthesis and CT: Comparison of detection and estimation task performance,” Med. Phys. 37, 26272637 (2010).
10.E. Samei, M. J. Flynn, and D. A. Reimann, “A method for measuring the presampled MTF of digital radiographic systems using an edge test device,” Med. Phys. 25, 102113 (1998).
11.H. Fujita, D. Y. Tsai, T. Itoh, K. Doi, J. Morishita, K. Ueda, and A. Ohtsuka, “A simple method for determining the modulation transfer function in digital radiography,” IEEE Trans. Med. Imaging 11, 3439 (1992).
12.X. Li, E. Samei, W. P. Segars, G. M. Sturgeon, J. G. Colsher, and D. P. Frush, “Patient-specific radiation dose and cancer risk for pediatric chest CT,” Radiology 259, 862874 (2011).
13.F. A. Miéville, F. Gudinchet, F. Brunelle, F. O. Bochud, and F. R. Verdun, “Iterative reconstruction methods in two different MDCT scanners: Physical metrics and 4-alternative forced-choice detectability experiments—A phantom approach,” Phys. Med. 29, 99110 (2013).
14.S. L. Brady, B. S. Yee, and R. A. Kaufman, “Characterization of adaptive statistical iterative reconstruction algorithm for dose reduction in CT: A pediatric oncology perspective,” Med. Phys. 39, 55205531 (2012).
15.B. Chen, O. Christianson, J. Wilson, and E. Samei, “Assessment of volumetric noise and resolution performance for linear and nonlinear CT reconstruction methods,” Med. Phys. 41, 071909 (12pp.) (2014).
16.E. Samei, S. Richard, and L. Lurwitz, “Model-based CT performance assessment and optimization for iodinated and non-iodinated imaging tasks as a function of kVp and body size,” Med. Phys. 41, 081910 (8pp.) (2014).
17.K. Li, J. Tang, and G.-H. Chen, “Statistical model based iterative reconstruction (MBIR) in clinical CT systems: Experimental assessment ofnoise performance,” Med. Phys. 41, 041906 (15pp.) (2014).
18.K. Li, J. Garrett, Y. Ge, and G.-H. Chen, “Statistical model based iterative reconstruction (MBIR) in clinical CT systems. Part II. Experimental assessment of spatial resolution performance,” Med. Phys. 41, 071911 (12pp.) (2014).
19.S. Richard and J. H. Siewerdsen, “Comparison of model and human observer performance for detection and discrimination tasks using dual-energyx-ray images,” Med. Phys. 35, 50435053 (2008).
20.G. J. Gang, D. J. Tward, J. Lee, and J. H. Siewerdsen, “Anatomical background and generalized detectability in tomosynthesis and cone-beam CT,” Med. Phys. 37, 19481965 (2010).
21.G. J. Gang, J. Lee, J. W. Stayman, D. J. Tward, W. Zbijewski, J. L. Prince, and J. H. Siewerdsen, “Analysis of Fourier-domain task-based detectability index in tomosynthesis and cone-beam CT in relation to human observer performance,” Med. Phys. 38, 17541768 (2011).
22.O. Christianson, J. Chen, Z. Yang, G. Saiprasad, A. Dima, J. Filliben, A. Peskin, C. Trimble, E. Siegel, and E. Samei, “An improved index of image quality for task-based performance of CT iterative reconstruction across three commercial implementations,” Radiology (2015) (in press).
23.J. Solomon, A. Mileto, J. C. Ramirez Giraldo, and E. Samei, “Diagnostic performance of an advanced modeled iterative reconstruction algorithm for low-contrast detectability on a third-generation dual-source MDCT scanner: Potential for radiation dose reduction in a multireader study,” Radiology (2015) (in press).
24.J. Solomon and E. Samei, “Quantum noise properties of CT images with anatomical textured backgrounds across reconstruction algorithms: FBP and SAFIRE,” Med. Phys. 41, 091908 (12pp.) (2014).

Data & Media loading...


Article metrics loading...



Different computed tomography (CT) reconstruction techniques offer different image quality attributes of resolution and noise, challenging the ability to compare their dose reduction potential against each other. The purpose of this study was to evaluate and compare the task-based imaging performance of CT systems to enable the assessment of the dose performance of a model-based iterative reconstruction (MBIR) to that of an adaptive statistical iterative reconstruction (ASIR) and a filtered back projection (FBP) technique.

The ACR CT phantom (model 464) was imaged across a wide range of mA setting on a 64-slice CT scanner (GE Discovery CT750 HD, Waukesha, WI). Based on previous work, the resolution was evaluated in terms of a task-based modulation transfer function (MTF) using a circular-edge technique and images from the contrast inserts located in the ACR phantom. Noise performance was assessed in terms of the noise-power spectrum (NPS) measured from the uniform section of the phantom. The task-based MTF and NPS were combined with a task function to yield a task-based estimate of imaging performance, the detectability index (′). The detectability index was computed as a function of dose for two imaging tasks corresponding to the detection of a relatively small and a relatively large feature (1.5 and 25 mm, respectively). The performance of MBIR in terms of the ′ was compared with that of ASIR and FBP to assess its dose reduction potential.

Results indicated that MBIR exhibits a variability spatial resolution with respect to object contrast and noise while significantly reducing image noise. The NPS measurements for MBIR indicated a noise texture with a low-pass quality compared to the typical midpass noise found in FBP-based CT images. At comparable dose, the ′ for MBIR was higher than those of FBP and ASIR by at least 61% and 19% for the small feature and the large feature tasks, respectively. Compared to FBP and ASIR, MBIR indicated a 46%–84% dose reduction potential, depending on task, without compromising the modeled detection performance.

The presented methodology based on ACR phantom measurements extends current possibilities for the assessment of CT image quality under the complex resolution and noise characteristics exhibited with statistical and iterative reconstruction algorithms. The findings further suggest that MBIR can potentially make better use of the projections data to reduce CT dose by approximately a factor of 2. Alternatively, if the dose held unchanged, it can improve image quality by different levels for different tasks.


Full text loading...


Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd