Peculiarities of dynamic evaluation of globular formation outlines of the lungs with multislice computed tomography

Background. Visualization of infiltration in lung tissue surrounding the globular formation of the lungs (GFL) determined by X-ray is one of the important points in the differential diagnosis of primary lung cancer, specific and non-specific inflammatory processes. At CT gauge body phantoms test facilities are widely used for evaluating the performance of scanners that allow the evaluation of scanner characteristics : noise, contrast sensitivity, positioning accuracy, stiffness of the radiation beam, the layer thickness, spatial resolution, etc.

Aim. To develop a methodology for assessing the GFL outlines of the dynamics of multislice computed tomography (MSCT) by selecting the optimal image processing algorithms.

Materials and methods. The visual analysis of two- component physical model images of the electronic window level (WL) and electronic window width (WW) was installed on the basis of the best conditions for studying a specific group of tissues. In the case of indistinct, poorly defined outlines of globular formations, visual assessment is operator-dependent and requires development and application of quantitative methods of analysis. For a quantitative description of the outlines of the image of the GFL model, a vector in a polar coordinate system coming from the center of the figure mass bounded by the outline was used. The following outline complexity measures were adopted: modified Shannon information entropy H(S(k)) for k harmonics of the normalized spectral power density S(k) of the length of oscillation of loop radius vector R(n); the number of local maxima L of signature radius vector R(n); the maximum value of the normalized power spectral density S(k); product (multiplicity) of the entropy H(S) and the number of local maxima L.

Results. “Multiplicity”, “the number of local maxima” of the outline depend on the GFL geometric dimensions and cannot be used for diagnosis without first normalizing for GFL outline length. The parameters, such as “entropy” and “maximum value of the normalized power spectral density” are invariant under GFL geometric sizes and can be used for differential diagnosis at any phase of the disease. 

1. Vlasov P.V. Luchevaja diagnostika zabolevanij organov grudnoj polosti [Radiological diagnosis of diseases of the chest cavity.] M.: Vidar Publ., 2006; 311 (in Russian).

2. Stebletsova T.V., Proskurin M.F., Yudin A.L., Yumatova E.A. Problemy differencial’noj diagnostiki dobrokachestvennyh i zlokachestvennyh konsolidacij na fone diffuznyh infil’trativnyh zabolevanij legkih [Problems of differential diagnosis of benign and malignant consolidations on a background of diffuse infiltrative lung diseases] // Medicinskaja vizualizacija – Medical Imaging. 2008; 6: 132–141 (in Russian).

3. Konovalov V.K., Shoikhet Y.N., Fedorov V.V., Lobanov M.N., Leonov S.L., Shayduk A.M., Kolmogorov V.G., Ostanin S.A., Kozlov D.Yu., Tseymah A.E. Sposob pricel’noj ob#emnoj densitometrii sharovidnyh obrazovanij legkih dlja ocenki ih vnutrennej struktury pri mul’tispiral’noj komp’juternoj tomografii [The method of sighting the volume of lung densitometry spherical formations to assess their internal structure with multislice computed tomography] // Problemy klinicheskoj mediciny – Problems of Clinical Medicine. 2012; 1–4 (26-29): 74–86 (in Russian).

4. Ivanichko T.E. Osobennosti differencial’noj diagnostiki perifericheskih obrazovanij legkih pri komp’juternoj tomografii: avtoref. dis. ... kand. med. nauk [Features of the differential diagnosis of peripheral lung formations computed tomography: аbstract. dis. ... cand. honey. Sciences: 14.00.14.] Tomsk, 2000: 24 (in Russian).

5. Chae H.D., Park C.M., Park S.J., Lee S.M., Kim K.G., Goo J.M. Computerized Texture Analysis of Persistent Part-Solid Ground-Glass Nodules: Differentiation of Preinvasive Lesions from Invasive Pulmonary Adenocarcinomas // Radiology. 2014; 273(1): 285–293. DOI: 10.1148/ radiol.14132187.

6. Samtsov E.N., Ivanichko T.E., Vasilyev N.V., Ermak E.E. Kompleksnaja luchevaja diagnostika vospalitel’nyh psevdoopuholej legkih [Complex X-ray diagnostics of inflammatory lung pseudotumour] // Medicinskaja vizualizacija – Medical Imaging. 2005; 6: 86–88 (in Russian).

7. Karpina N.L., Antipov A.V., Chekletsova L.I. Possazhennikova S.Y., Demikhova O.V. Slozhnyj sluchaj differencial’noj diagnostiki ochagovo-infil’trativnyh izmenenij legkih vo ftiziatricheskoj praktike [Complicated case the differential diagnosis of focal-infiltrative pulmonary TB changes in practice] // Vestnik rentgenologii i radiologii – Vestnik of Radiology. 2015; 5: 36–41 (in Russian).

8. Ischenko B.I., Bisenkov L.N., Tyurin I.E. Luchevaja diagnostika dlja torakal’nyh hirurgov [Radiological diagnosis for thoracic surgeons.] SPb.: Dean Publ., 2001; 133 (in Russian).

9. Dabagov A.R. Cifrovaja radiologija i diagnostika. Dostizhenija i perspektivy [Jelektronnyj resurs] [Digital radiology and diagnostics. Achievements and Prospects [Electronic resource] // Zhurnal radiojelektroniki – Radio Electronics Magazine. 2009; 5. Access: http://jre.cplire. ru/jre/jan12/9/text.pdf (in Russian).

10. Lobanov M.N. Differencial’naja diagnostika sharovidnyh obrazovanij legkih pri mul’tispiral’noj komp’juternoj tomografii na osnove mnogomernoj obrabotki densitometricheskih parametrov: avtoref. dis. ...kand. med. nauk: 14.01.25; 14.01.13 [Differential diagnosis of lung spherical formations with multislice computed tomography based on a multidimensional treatment densitometric parameters: abstract. dis. ... cand. honey. Sciences: 14.01.25; 14.01.13] Barnaul, 2013: 26 (in Russian).

11. Yudin A.L., Shatalov K.M., Sologubov G.F. Lipoidnaja pnevmonija [Lipoid pneumonia] // Medicinskaja vizualizacija – Medical Imaging. 2015; 6: 42–49 (in Russian).

12. Nudnov N.V., Sotnikov V.M., Ledenev V.V., Baryshnikov D.V. Vozmozhnosti kachestvennoj ocenki luchevyh povrezhdenij legkih metodom komp’juternoj tomografii [Features a qualitative assessment of radiation damage lung computerized tomography] // Medicinskaja vizualizacija – Medical Imaging. 2016; 1: 39–44 (in Russian).

13. Kim M., Lee J.M., Yoon J.H., Son H., Choi J.W., Han J.K., Choi B.I. Adaptive iterative dose reduction algorithm in CT: effect on image quality compared with filtered back projection in body phantoms of different sizes // Korean J. Radiol. 2014; 15(2): 195–204. DOI: 10.3348/ kjr.2014.15.2.195.

14. Higuchi K., Nagao M., Matsuo Y., Sunami S., Kamitani T., Jinnouchi M., Yonezawa M., Yamasaki Y., Yabuuchi H., Hatkenaka M., Honda H. Detection of ground-glass opacities by use of hybrid iterative reconstruction (iDose) and low-dose 256-section computed tomography: a phantom study // Radiol. Phys. Technol. 2013; 6: 299–304. DOI: 10.1007/s12194-013-0200-y.

15. Marenkova M.L., Zholudev S.E. Sposob vypolnenija gemoliticheskogo testirovanija konstrukcionnogo stomatologicheskogo materiala [A method for performing structural testing of hemolytic dental material]. RF patent for the invention – 2392619. 2008 (in Russian).

16. Konovalov V.K., Leonov S.L., Shayduk A.M., Lobanov M.N., Kolmogorov V.G., Fedoseyev M.A., Borisenko O.V., Modakalova J.S., Isakov A.E., Vorona O.I., Kazantsev M.V., Fedorov V.V., Shoikhet Y.N. Tkanevoj densitometricheskij analiz sharovidnyh obrazovanij legkih na osnove sistemy iskusstvennogo intellekta pri mul’tispiral’noj komp’juternoj tomografii [Tissue densitometric analysis of spherical lung formations based on artificial intelligence system with multislice computed tomography] // Rossijskij onkologicheskij zhurnal – Russian Journal of Oncology. 2015; 5: 8–12 (in Russian).

17. Prokop M., Galansky M. Spiral’naja i mnogoslojnaja komp’juternaja tomografija [Spiral computed tomography and multi-layer]. Moscow: MEDpress-inform Publ., 2007: 712 (in Russian).

18. Hofer M. Komp’juternaja tomografija. Bazovoe rukovodstvo [Computed tomography. A basic guide]. Moscow: Med. Litas Publ., 2008: 224 (in Russian).

19. Tyurin I.E. Computed tomography of the chest cavity. SPb., 2003: 371 (in Russian).

20. Ostanin S.A., Shayduk A.M. Kolichestvennaja ocenka slozhnosti kontura medicinskih izobrazhenij [Jelektronnyj resurs] [Quantitative assessment of the complexity of the circuit medical imaging [Electronic resource]] // Zhurnal radiojelektroniki – Radio Electronics Magazine. 2013; 2. Access: http://jre.cplire.ru/koi/feb13/index. html. (in Russian).

21. Ostanin S.A., Shayduk A.M., Kozlov D.Yu., Konovalov V.K., Leonov S.L., Fedorov V.V., Shoikhet Y.N., Kolmogorov V.G., Lobanov M.N. Jentropijnyj metod ocenki slozhnosti kontura medicinskih izobrazhenij [Entropy method for estimating the complexity of the circuit medical images] // Izvestija Altajskogo gosudarstvennogo universiteta – News of Altai State University. 2013; 1–2 (77): 177–180 (in Russian).

22. Ostanin S.A., Shayduk A.M. Utochnenie otnoshenija mezhdu fraktal’noj razmernost’ju i stepen’ju spektra moshhnosti signala [Clarification of the relationship between the fractal dimension and the extent of the power spectrum of the signal] // Zhurnal radiojelektroniki – Radio Electronics Magazine. 2012; 8: 2 (in Russian).