Characterization and Correction of Beam-hardening Artifacts during Dynamic Volume CT Assessment of Myocardial Perfusion

Published Online:https://doi.org/10.1148/radiol.10091399

Application of a cardiac-specific beam-hardening correction software algorithm is helpful for improving accuracy of myocardial perfusion at dynamic volume CT.

Purpose

To fully characterize beam-hardening effects caused by iodinated contrast medium in the left ventricular (LV) cavity and aorta in the assessment of myocardial perfusion at computed tomography (CT) and to validate a beam-hardening artifact correction algorithm that considers fluid-filled vessels and chambers important sources of beam hardening.

Materials and Methods

The Johns Hopkins University animal care and use committee approved all procedures. An anatomically correct LV and myocardial phantom to characterize beam-hardening artifacts was designed. Following validation in the phantom, the beam-hardening correction (BHC) algorithm was applied to 256–detector row dynamic volume CT images in a canine ischemia model (n = 5) during adenosine stress, and the effect of beam hardening was determined by comparing regional dynamic volume CT perfusion metrics (myocardial upslope normalized by maximum LV blood pool attenuation) with microsphere-derived myocardial blood flow (MBF). A paired Student t test was used to compare continuous variables from the same subject but under different conditions, while linear regression analysis was performed to estimate the slope and statistical significance of the relationship between CT-derived perfusion metrics and microsphere-derived MBF.

Results

Beam-hardening artifacts were successfully reproduced in phantom studies and were eliminated with the BHC algorithm. The correlation coefficient of CT-derived perfusion metrics and microsphere-derived MBF improved from 0.60 to 0.74 (P > .05) following correction in the animal model.

Conclusion

Beam-hardening artifacts confound dynamic volume CT assessment of myocardial perfusion. Application of the BHC algorithm is helpful for improving accuracy of myocardial perfusion at dynamic volume CT.

© RSNA, 2010

Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.10091399/-/DC1

References

  • 1 Jongbloed MR, Dirksen MS, Bax JJ, et al.. Atrial fibrillation: multi-detector row CT of pulmonary vein anatomy prior to radiofrequency catheter ablation—initial experience. Radiology 2005;234(3):702–709. LinkGoogle Scholar
  • 2 Lardo AC, Cordeiro MA, Silva C, et al.. Contrast-enhanced multidetector computed tomography viability imaging after myocardial infarction: characterization of myocyte death, microvascular obstruction, and chronic scar. Circulation 2006;113(3):394–404. Crossref, MedlineGoogle Scholar
  • 3 Yamamuro M, Tadamura E, Kubo S, et al.. Cardiac functional analysis with multi-detector row CT and segmental reconstruction algorithm: comparison with echocardiography, SPECT, and MR imaging. Radiology 2005;234(2):381–390. LinkGoogle Scholar
  • 4 George RT, Silva C, Cordeiro MA, et al.. Multidetector computed tomography myocardial perfusion imaging during adenosine stress. J Am Coll Cardiol 2006;48(1):153–160. Crossref, MedlineGoogle Scholar
  • 5 Kurata A, Mochizuki T, Koyama Y, et al.. Myocardial perfusion imaging using adenosine triphosphate stress multi-slice spiral computed tomography: alternative to stress myocardial perfusion scintigraphy. Circ J 2005;69(5):550–557. Crossref, MedlineGoogle Scholar
  • 6 George RT, Arbab-Zadeh A, Miller JM, et al.. Adenosine stress 64- and 256-row detector computed tomography angiography and perfusion imaging: a pilot study evaluating the transmural extent of perfusion abnormalities to predict atherosclerosis causing myocardial ischemia. Circ Cardiovasc Imaging 2009;2(3):174–182. Crossref, MedlineGoogle Scholar
  • 7 Brooks RA, Di Chiro G. Beam hardening in x-ray reconstructive tomography. Phys Med Biol 1976;21(3):390–398. Crossref, MedlineGoogle Scholar
  • 8 Zatz LM, Alvarez RE. An inaccuracy in computed tomography: the energy dependence of CT values. Radiology 1977;124(1):91–97. LinkGoogle Scholar
  • 9 Olson EA, Han KS, Pisano DJ. CT reprojection polychromaticity correction for three attenuators. IEEE Trans Nucl Sci 1981;28(4):3628–3640. CrossrefGoogle Scholar
  • 10 George RT, Jerosch-Herold M, Silva C, et al.. Quantification of myocardial perfusion using dynamic 64-detector computed tomography. Invest Radiol 2007;42(12):815–822. Crossref, MedlineGoogle Scholar
  • 11 Rao PS, Alfidi RJ. The environmental density artifact: a beam-hardening effect in computed tomography. Radiology 1981;141(1):223–227. LinkGoogle Scholar
  • 12 Maki DD, Birnbaum BA, Chakraborty DP, Jacobs JE, Carvalho BM, Herman GT. Renal cyst pseudoenhancement: beam-hardening effects on CT numbers. Radiology 1999;213(2):468–472. LinkGoogle Scholar
  • 13 Young SW, Muller HH, Marshall WH. Computed tomography: beam hardening and environmental density artifact. Radiology 1983;148(1):279–283. LinkGoogle Scholar
  • 14 Kachelriess M, Sourbelle K, Kalender WA. Empirical cupping correction: a first-order raw data precorrection for cone-beam computed tomography. Med Phys 2006;33(5):1269–1274. Crossref, MedlineGoogle Scholar
  • 15 Joseph PM, Spital RD. A method for correcting bone induced artifacts in computed tomography scanners. J Comput Assist Tomogr 1978;2(1):100–108. Crossref, MedlineGoogle Scholar
  • 16 Hsieh J, Molthen RC, Dawson CA, Johnson RH. An iterative approach to the beam hardening correction in cone beam CT. Med Phys 2000;27(1):23–29. Crossref, MedlineGoogle Scholar

Article History

Received July 31, 2009; revision requested September 4; revision received December 24; accepted January 14, 2010; final version accepted January 20.
Published online: July 2010
Published in print: July 2010