Published Online:https://doi.org/10.1148/radiol.11101450

CT radiation dose reduction down to 3.5 mGy is achievable for adaptive statistical iterative reconstructed chest CT while maintaining acceptable image noise and diagnostic confidence.

Purpose

To compare lesion detection and image quality of chest computed tomographic (CT) images acquired at various tube current–time products (40–150 mAs) and reconstructed with adaptive statistical iterative reconstruction (ASIR) or filtered back projection (FBP).

Materials and Methods

In this Institutional Review Board–approved HIPAA-compliant study, CT data from 23 patients (mean age, 63 years ± 7.3 [standard deviation]; 10 men, 13 women) were acquired at varying tube current–time products (40, 75, 110, and 150 mAs) on a 64-row multidetector CT scanner with 10-cm scan length. All patients gave informed consent. Data sets were reconstructed at 30%, 50%, and 70% ASIR-FBP blending. Two thoracic radiologists assessed image noise, visibility of small structures, lesion conspicuity, and diagnostic confidence. Objective noise and CT number were measured in the thoracic aorta. CT dose index volume, dose-length product, weight, and transverse diameter were recorded. Data were analyzed by using analysis of variance and the Wilcoxon signed rank test.

Results

FBP had unacceptable noise at 40 and 75 mAs in 17 and five patients, respectively, whereas ASIR had acceptable noise at 40–150 mAs. Objective noise with 30%, 50%, and 70% ASIR blending (11.8 ± 3.8, 9.6 ± 3.1, and 7.5 ± 2.6, respectively) was lower than that with FBP (15.8 ± 4.8) (P < .0001). No lesions were missed on FBP or ASIR images. Lesion conspicuity was graded as well seen on both FBP and ASIR images (P < .05). Mild pixilated blotchy texture was noticed with 70% blended ASIR images.

Conclusion

Acceptable image quality can be obtained for chest CT images acquired at 40 mAs by using ASIR without any substantial artifacts affecting diagnostic confidence.

© RSNA, 2011

Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11101450/-/DC1

References

  • 1 Berrington de González A, Mahesh M, Kim KP, et al.. Projected cancer risks from computed tomographic scans performed in the United States in 2007. Arch Intern Med 2009;169(22):2071–2077.
  • 2 Schauer DA, Linton OW. NCRP report no. 160: ionizing radiation exposure of the population of the United States, medical exposure—are we doing less with more, and is there a role for health physicists? Health Phys 2009;97(1):1–5.
  • 3 Kalra MK, Maher MM, Toth TL, et al.. Strategies for CT radiation dose optimization. Radiology 2004;230(3):619–628.
  • 4 Heyer CM, Mohr PS, Lemburg SP, Peters SA, Nicolas V. Image quality and radiation exposure at pulmonary CT angiography with 100- or 120-kVp protocol: prospective randomized study. Radiology 2007;245(2):577–583.
  • 5 Diel J, Perlmutter S, Venkataramanan N, Mueller R, Lane MJ, Katz DS. Unenhanced helical CT using increased pitch for suspected renal colic: an effective technique for radiation dose reduction? J Comput Assist Tomogr 2000;24(5):795–801.
  • 6 Rizzo SM, Kalra MK, Schmidt B, et al.. CT images of abdomen and pelvis: effect of nonlinear three-dimensional optimized reconstruction algorithm on image quality and lesion characteristics. Radiology 2005;237(1):309–315.
  • 7 Kalra MK, Maher MM, Blake MA, et al.. Detection and characterization of lesions on low-radiation-dose abdominal CT images postprocessed with noise reduction filters. Radiology 2004;232(3):791–797.
  • 8 Ziegler A, Köhler T, Proksa R. Noise and resolution in images reconstructed with FBP and OSC algorithms for CT. Med Phys 2007;34(2):585–598.
  • 9 De Man B, Nuyts J, Dupont P, et al.. Reduction of metal streak artifacts in x-ray computed tomography using a transmission maximum a posteriori algorithm. Nucl Sci IEEE Trans 2000;47(3):977–981.
  • 10 Liow JS, Strother SC. Practical tradeoffs between noise, quantitation, and number of iterations for maximum likelihood-based reconstructions. IEEE Trans Med Imaging 1991;10(4):563–571.
  • 11 Kalra MK, Maher MM, Kamath RS, et al.. Sixteen-detector row CT of abdomen and pelvis: study for optimization of z-axis modulation technique performed in 153 patients. Radiology 2004;233(1):241–249.
  • 12 Schindera ST, Nelson RC, Toth TL, et al.. Effect of patient size on radiation dose for abdominal MDCT with automatic tube current modulation: phantom study. AJR Am J Roentgenol 2008;190(2):W100–W105.
  • 13 EUR 16262. European guidelines on quality criteria for computed tomography. http://www.drs.dk/guidelines/ct/quality/download/eur16262.w51. Accessed January 10, 2010.
  • 14 Singh S, Kalra MK, Moore MA, et al.. Dose reduction and compliance with pediatric CT protocols adapted to patient size, clinical indication, and number of prior studies. Radiology 2009;252(1):200–208.
  • 15 Kalra MK, Rizzo S, Maher MM, et al.. Chest CT performed with z-axis modulation: scanning protocol and radiation dose. Radiology 2005;237(1):303–308.
  • 16 Sigal-Cinqualbre AB, Hennequin R, Abada HT, Chen X, Paul JF. Low-kilovoltage multi-detector row chest CT in adults: feasibility and effect on image quality and iodine dose. Radiology 2004;231(1):169–174.
  • 17 Kubo T, Nishino M, Kino A, et al.. 3-dimensional adaptive raw-data filter: evaluation in low dose chest multidetector-row computed tomography. J Comput Assist Tomogr 2006;30(6):933–938.
  • 18 Kim MJ, Park CH, Choi SJ, Hwang KH, Kim HS. Multidetector computed tomography chest examinations with low-kilovoltage protocols in adults: effect on image quality and radiation dose. J Comput Assist Tomogr 2009;33(3):416–421.
  • 19 Yi CA, Lee KS, Kim TS, Han D, Sung YM, Kim S. Multidetector CT of bronchiectasis: effect of radiation dose on image quality. AJR Am J Roentgenol 2003;181(2):501–505.
  • 20 Jung KJ, Lee KS, Kim SY, Kim TS, Pyeun YS, Lee JY. Low-dose, volumetric helical CT: image quality, radiation dose, and usefulness for evaluation of bronchiectasis. Invest Radiol 2000;35(9):557–563.
  • 21 Loeve M, Lequin MH, de Bruijne M, et al.. Cystic fibrosis: are volumetric ultra-low-dose expiratory CT scans sufficient for monitoring related lung disease? Radiology 2009;253(1):223–229.
  • 22 Li X, Samei E, DeLong DM, et al.. Pediatric MDCT: towards assessing the diagnostic influence of dose reduction on the detection of small lung nodules. Acad Radiol 2009;16(7):872–880.
  • 23 Singh S, Sharma A, Digumarthy SR, et al.. Two-dimensional image filters for reducing radiation dose for multidetector chest CT: a double blinded study [abstr]. In: Radiological Society of North America scientific assembly and annual meeting program. Oak Brook, Ill: Radiological Society of North America, 2008; 665.
  • 24 Hara AK, Paden RG, Silva AC, Kujak JL, Lawder HJ, Pavlicek W. Iterative reconstruction technique for reducing body radiation dose at CT: feasibility study. AJR Am J Roentgenol 2009;193(3):764–771.
  • 25 Prakash P, Kalra MK, Digumarthy SR, et al.. Radiation dose reduction with chest computed tomography using adaptive statistical iterative reconstruction technique: initial experience. J Comput Assist Tomogr 2010;34(1):40–45.
  • 26 Thibault JB, Sauer KD, Bouman CA, Hsieh J. A three-dimensional statistical approach to improved image quality for multislice helical CT. Med Phys 2007;34(11):4526–4544.
  • 27 Kole JS. Statistical image reconstruction for transmission tomography using relaxed ordered subset algorithms. Phys Med Biol 2005;50(7):1533–1545.
  • 28 Wang J, Li T, Lu H, Liang Z. Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose x-ray computed tomography. IEEE Trans Med Imaging 2006;25(10):1272–1283.
  • 29 Prakash P, Kalra MK, Ackman JB, et al.. Diffuse lung disease: CT of the chest with adaptive statistical iterative reconstruction technique. Radiology 2010;256(1):261–269.

Article History

Received July 20, 2010; revision requested October 11; revision received November 8; accepted November 19; final version accepted December 15.
Published online: May 2011
Published in print: May 2011