Back
 OJRad  Vol.4 No.1 , March 2014
Effect of Slice Thickness Reconstruction on Quantitative Vascular Parameters in Perfusion CT
Abstract: Objective: The purpose of this study was to determine how the slice thickness reconstruction influences quantitative perfusion CT parameters. Materials and Methods: Eighteen patients with cancer (15 non-small-cell lung cancer, 2 rectal cancer and 1 renal cancer) were examined prospectively with multidetector row CT. A 90-second perfusion study was performed after intravenous bolus injection of contrast material. Blood flow, blood volume, mean transit time and permeability-surface area product were determined at three different slice thickness reconstruction (1.25, 2.5 and 5 mms) both in tumors and in paraspinal muscle. Mean values, limits of agreement between measurements and within-subject coefficient of correlation were obtained for these thicknesses. Results: Mean ± standard deviation BF, BV, MTT and PS in lesions were 118.7 ± 117.9 mL/min/100g tissue, 8.2 ± 8.2 mL/100g tissue, 7.5 ± 5.4 seconds and 10.3 ± 7.2 mL/min/100g tissue respectively at1.25 mmslice thickness; 116.1 ± 115.7 mL/min/100g tissue, 7.8 ± 8.7 mL/100g tissue, 7 ± 4.5 seconds and 10.4 ± 7.5 mL/min/100g tissue at 2.5 mms; and 119.6 ± 115.7 mL/min/100g tissue, 7.8 ± 8.8 mL/100g tissue, 5.4 ± 3.4 seconds and 9.6 ± 7.5 mL/min/100g tissue at 5 mms. Differences between means for different slice thickness where relatively small in all parameters (<15%) except in MTT where difference was up to 37%. 95% limits of agreement were worse when comparing more different slice thicknesses (e.g. 1.25 vs 5 mms) than when comparing more close slice thicknesses (1.25 vs 2.5 mms or 2.5 vs 5 mms). Conclusions: There is a significant variability in perfusion parameter measurements at different slice thickness reconstruction, particularly in MTT. The more close together the slice thicknesses were, the smaller was the variability.
Cite this paper: Martínez-de-Alegría, A. , García-Figueiras, R. , Baleato-González, S. , Rodriguez-Alvarez, M. , Martínez-Souto, I. and Villalba-Martín, C. (2014) Effect of Slice Thickness Reconstruction on Quantitative Vascular Parameters in Perfusion CT. Open Journal of Radiology, 4, 53-59. doi: 10.4236/ojrad.2014.41007.
References

[1]   Kambadakone, A.R. and Sahani, D.V. (2009) Body Perfusion CT: Technique, Clinical Applications, and Advances. Radiologic Clinics of North America, 47, 161-178.
http://dx.doi.org/10.1016/j.rcl.2008.11.003

[2]   Axel, L. (1980) Cerebral Blood Flow Determination by Rapid-Sequence Computed Tomography: Theoretical Analysis. Radiology, 137, 679-686.

[3]   García-Figueiras, R., Goh, V.J., Padhani, A.R., Baleato-Gonzalez, S., Garrido, M., León, L. and Gómez-Caamano, A. (2013) CT Perfusion in Oncologic Imaging: A Useful Tool? AJR, 1, 8-19.
http://dx.doi.org/10.2214/AJR.11.8476

[4]   Ng, Q.S. and Goh, V. (2010) Angiogenesis in Non-Small Cell Lung Cancer: Imaging with Perfusion Computed Tomography. Journal of Thoracic Imaging, 25, 142-150.
http://dx.doi.org/10.1097/RTI.0b013e3181d29ccf

[5]   Petralia, G., Bonello, L., Viotti, S., Preda, L., d’Andrea G. and Bellomi, M. (2010) CT Perfusion in Oncology: How to Do It. Cancer Imaging, 10, 8-19. http://dx.doi.org/10.1102/1470-7330.2010.0001

[6]   Bland, J.M. and Altman, D.G. (1986) Statistical Methods for Assessing Agreement between Two Methods of Clinical Measurement. Lancet, 1, 307-310.
http://dx.doi.org/10.1016/S0140-6736(86)90837-8

[7]   Quan, H. and Shih, W.J. (1996) Assessing Reproducibility by the Within-Subject Coefficient of Variation with Random Effects Models. Biometrics, 52, 1195-1203.
http://dx.doi.org/10.2307/2532835

[8]   R Core Team (2013) R: A Language and Environment for Statistical Computing. http://www.R-project.org/

[9]   Carstensen, B., Gurrin, L. and Ekstrom, C. (2012) MethComp: Functions for Analysis of Method Comparison Studies.
http://bendixcarstensen.com/MethComp/

[10]   Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. and the R Development Core Team (2013) nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-109.

[11]   Goh, V., Dattani, M., Farwell, J., Shekhdar, J., Tam, E., Patel, S., Juttla, J., Simcock, I., Stirling, J., Mandeville, H., Aird, E. and Hoskin, P. (2011) Radiation Dose from Volumetric Helical Perfusion CT of the Thorax, Abdomen or Pelvis. European Radiology, 21, 974-981. http://dx.doi.org/10.1007/s00330-010-1997-y

[12]   Goh, V., Halligan, S., Hugill, J.-A. and Bartram, C.I. (2006) Quantitative Assessment of Tissue Perfusion Using MDCT Comparison of Colorectal Cancer and Skeletal Muscle Measurement Reproducibility. AJR, 187, pp.164-169.
http://dx.doi.org/10.2214/AJR.05.0050

[13]   Ng, C.S., Chandler, A.G., Wei, W., Herron, D.H., Anderson, E.F., Kurzrock, R. and Charnsangavej, C. (2011) Reproducibility of CT Perfusion Parameters in Liver Tumors and Normal Liver. Radiology, 260, 762-770.
http://dx.doi.org/10.1148/radiol.11110331

[14]   Goh, V., Halligan, S. and Bartram, C.I. (2007) Quantitative Tumor Perfusion Assessment with Multidetector CT: Are Measurements from Two Commercial Software Packages Interchangeable? Radiology, 242, 777-782.
http://dx.doi.org/10.1148/radiol.2423060279

 
 
Top