Assessment of paravertebral soft tissues using computed tomography

Artem Skidanov, Aleksandr Avrunin, Maksym Tymkovych, Yuriy Zmiyenko, Liliya Levitskaya, Lyubov Mischenko, Volodymyr Radchenko


Limited opportunities for studying of paravertebral soft tissue in living individuals are pushing to find new ways to enhance knowledge in this area. Computed tomography (CT) allows to determine radiological density of soft tissues expressing in Hausfild units.

Objective: to create a method of paravertebral muscles examination based on determinination of their radio­logical density using computed tomography. The material of the study consists of 663 axial slices of CT scans of the lumbar spine performed in 129 adult patients and in 93 children under 21 year. For the analysis we selected 3 978 samples of muscle tissue, 658 connective tissue and 663 fat one. Statistical analysis was carried out by «data mining» (Data Mining) using so-called decision tree (or classification tree, decision (classification) trees).

Results: we obtained some data characterizing the highlighted regions of tissues in Hausfild units (minimum and maximum value, standard deviation, mean and peak values). It was found that by using only one indicator one can not distinguish between muscle, connec­tive and fatt tissue. However if we assess tissues considering all the parameters of their radiological density separation becomes possible with precision up to 87.85 %. On the basis of the recogni­tion algorithm there was created a computer program allowing to define in a muscle which was highlighted on CT axial sections of the lumbar spine an area of its cross-section, and the percentage in it muscle, connective and fatt tissues.

Conclusion: A computer program created significantly expands possibilities for the study of paravertebral muscles in various pathological conditions of the spine.


paravertebral muscles; disorders; lumbar spine; computed tomography


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Copyright (c) 2015 Artem Skidanov, Aleksandr Avrunin, Michael Tymkovych, Yuriy Zmiyenko, Liliya Levitskaya, Lyubov Mischenko, Volodymyr Radchenko

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