Influence of the segmentation method – automatic vs manual – and the presence or not of extra myocardial activity in quantification of defect extent in SPECT myocardial studies

Authors

  • Marisa Machado Mestrado em Engenharia Biomédica. Escola Superior de Tecnologia da Saúde de Lisboa/Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa. Lisboa, Portugal.
  • Ricardo Jorge Hospital da Luz. Lisboa, Portugal.
  • Sérgio Figueiredo Departamento das Ciências e Tecnologias das Radiações e Biossinais da Saúde, Escola Superior de Tecnologia da Saúde de Lisboa, Instituto Politécnico de Lisboa. Lisboa, Portugal.
  • Elisabete Carolino Departamento das Ciências Naturais e Exatas, Escola Superior de Tecnologia da Saúde de Lisboa, Instituto Politécnico de Lisboa. Lisboa, Portugal.
  • Lina Vieira Departamento das Ciências e Tecnologias das Radiações e Biossinais da Saúde, Escola Superior de Tecnologia da Saúde de Lisboa, Instituto Politécnico de Lisboa. Lisboa, Portugal.

DOI:

https://doi.org/10.25758/set.1681

Keywords:

SPECT, Segmentation, Perfusion defect extent, Extra myocardial activity, Myocardial perfusion imaging

Abstract

Introduction – Myocardial Perfusion Imaging (MPI) by single-photon emission computed tomography (SPECT) is one of the most widely used Non-invasive imaging tests in the diagnosis of coronary artery disease that requires correct segmentation of the left ventricle (LV), to extract perfusion parameters. The aim of this study is to evaluate the influence of automatic (A) vs. manual (M) segmentation in the quantification of defect extent (DE) of myocardial perfusion, in studies with and without extra myocardial activity. Methodology – A retrospective study with a non-probabilistic sample was used, for convenience, of 63 stress studies, with the indication for MPI available in the Xeleris® workstation database in ESTeSL, that were divided into four groups: Group (G) I (GI): 26 studies by presenting a DE below 10% of the total surface area of the LV; Group II (GII): 5 studies with a DE equal or above 10%; Group III (GIII): 21 studies with a DE below 10%, with extra myocardial activity and Group IV (GIV): 11 studies with a DE, with extra myocardial activity. All studies were segmented, by one operator, using the A and the M quantification software Quantitative Perfusion SPECT (QPS®). For data analysis from the map polar with 20 segments were used t-Student, Wilcoxon, and U de Mann-Whitney tests considered α=0.05. Results – Concerning the perfusion DE evaluation it was verified that there were statistically significant differences (p>α) between the A vs. M segmentation, in segments 13-15 (GI); segments 13 and 16 (GIII), and segments 1 and 16 (GIV). Regarding the studies with and without extra myocardial activity, it was observed that no statistically significant variability exists (p>α). Conclusion – On the basis of the sample analyzed there are differences between an A vs M segmentation in peripheral segments of the polar map, in myocardial perfusion ED evaluation. There are no differences between myocardial perfusion DE in studies with and without extra myocardial activity.

Downloads

Download data is not yet available.

References

Ritt P, Vija H, Hornegger J, Kuwert T. Absolute quantification in SPECT. Eur J Nucl Med Mol Imaging. 2011;38 Suppl 1:S69-77.

Münch G, Neverve J, Matsunari I, Schröter G, Schwaiger M. Myocardial technetium 99m-tetrofosmin and technetium-99m-sestamibi kinetics in normal subjects and patients with coronary artery disease. J Nucl Med. 1997;38(3):428-32.

Akesson L, Svensson A, Edenbrandt L. Operator dependent variability in quantitative analysis of myocardial perfusion images. Clin Physiol Funct Imaging. 2004;24(6):374-9.

Pham DL, Xu C, Prince JL. Current methods in medical image segmentation. Annu Rev Biomed Eng. 2000;2:315-37.

Lin GS, Hines HH, Grant G, Taylor K, Ryals C. Automated quantification of myocardial ischemia and wall motion defects by use of cardiac SPECT polar mapping and 4-dimensional surface rendering. J Nucl Med Technol. 2006;34(1):3-17.

Figueiredo S, Fragoso P. Image processing and software. In: Ryder H, Testanera G, Jerónimo VV, Vidovic B, editors. Myocardial perfusion imaging: a technologist's guide. European Association of Nuclear Medicine; 2014. p. 77-108.

Zaret BL, Beller GA. Clinical nuclear cardiology: state of the art and future directions. 4th ed. St. Louis: Mosby; 2010. ISBN 9780323057967

Van der Veen BJ, Scholte AJ, Dibbets-Schneider P, Stokkel MP. The consequences of a new software package for the quantification of gated-SPECT myocardial perfusion studies. Eur J Nucl Med Mol Imaging. 2010;37(9):1736-44.

Germano G, Kavanagh PB, Slomka PJ, Van Kriekinge SD, Pollard G, Berman DS. Quantitation in gated perfusion SPECT imaging: the Cedars-Sinai approach. J Nucl Cardiol. 2007;14(4):433-54.

Boudraa AO, Zaidi H. Image segmentation techniques in nuclear medicine imaging. In: Zaidi H, editor. Quantitative analysis in nuclear medicine imaging. New York: Springer; 2006. p. 308-57. ISBN 9780387254449

Rogowska J. Overview and fundamentals of medical image segmentation. In: Bankman IN, editor. Handbook of medical imaging. San Diego: Academic Press; 2000. p. 73-90. ISBN 0120777908

Gulhane A, Paikrao PL, Chaudhari DS. A review of image data clustering techniques. Int J Soft Comput Eng. 2012;2(1):212-5.

Soneson H, Engblom H, Hedström E, Bouvier F, Sörensson P, Pernow J, et al. An automatic method for quantification of myocardium at risk from myocardial perfusion SPECT in patients with acute coronary occlusion. J Nucl Cardiol. 2010;17(5):831-40.

Germano G, Kiat H, Kavanagh PB, Moriel M, Mazzanti M, Su HT, et al. Automatic quantification of ejection fraction from gated myocardial perfusion SPECT. J Nucl Med. 1995;36(11):2138-47.

Wolak A, Slomka PJ, Fish MB, Lorenzo S, Acampa W, Berman DS, et al. Quantitative myocardial-perfusion SPECT: comparison of three state-of-the-art software packages. J Nucl Cardiol. 2008;15(1):27-34.

Kang D, Woo J, Slomka P, Key D, Germano G, Kuo CC. Heart chambers and whole heart segmentation techniques: review. J Electron Imaging. 2012;21(1):010901.

Higley B, Smith FW, Smith T, Gemmell HG, Das Gupta P, Gvozdanovic DV, et al. Technetium-99m-1,2-bis[bis(2-ethoxyethyl) phosphino]ethane: human biodistribution, dosimetry and safety of a new myocardial perfusion imaging agent. J Nucl Med. 1993;34(1):30-8.

Lim HS, Tahk SJ, Yoon MH, Woo SI, Choi WJ, Hwang JW, et al. A novel index of microcirculatory resistance for invasively assessing myocardial viability after primary angioplasty for treating acute myocardial infarction: comparison with FDG-PET imaging. Korean Circ J. 2007;37(7):318-26.

Marôco J. Análise estatística com o SPSS Statistics. 5ª ed. Pêro Pinheiro: ReportNumber; 2011. ISBN 9789899676329

Germano G, Berman DS. Clinical gated cardiac SPECT. 2nd ed. New York: Wiley-Blackwell; 2008. ISBN 9780470987308

Wackers FJ, Bruni W, Zaret B. Nuclear cardiology, the basics: how to set up and maintain a laboratory (contemporary cardiology). 2nd ed. New Jersey: Humana Press; 2007. ISBN 9781588299246

Xu Y, Kavanagh P, Fish M, Gerlach J, Ramesh A, Lemley M, et al. Automated quality control for segmentation of myocardial perfusion SPECT. J Nucl Med. 2009;50(9):1418-26.

Published

2022-08-04

Issue

Section

Artigos

How to Cite

Influence of the segmentation method – automatic vs manual – and the presence or not of extra myocardial activity in quantification of defect extent in SPECT myocardial studies. (2022). Saúde & Tecnologia, 17, 18-24. https://doi.org/10.25758/set.1681