Abstract

Introduction. Spinal cord injury is a debilitating traumatic event in central nervous system resulting in tissue destruction and severe neurological deficit development. Preclinical assessment of quantitative lesion area parameters (e. g. structure and volume) is critical for subsequent evaluation of neuroprotective and / or neuroregenerative therapy efficiency. Current methods for parameter calculation require manual limitation of the interested area (region of interest, RoI). This process is tedious and often not precise enough.Study objective is to develop and implement software for automated assessment of volume and structure of posttraumatic spinal cord lesion using extra-high-field MRI 7.0 Tesla and to compare methods preciseness with the current manual techniques.Study design. Ten rat models of acute severe spinal cord contusion injury were used including female Sprague–Dawley animals weighting 250–350 gr. MRI imaging was performed in 1 day postoperative and then 4 times with interval (1 week). Study was prospective open-label uncontrolled comparative.Materials and methods. Standard spinal cord contusion injury model was used. Anesthetized animals underwent laminectomy at level Th9–Th10 vertebrae followed by “weight drop” injury technique application: 10 g weight with 2 mm pin diameter dropped from 25 mm height. Software was developed using Microsoft Visual Studio 2017 environment and programming language C#. Statistical analysis was performed using IBM SPSS Statistics 21.0 software.Results. We developed and patented specialized software Spinal cavity Searcher realizing the algorithm of T2‑weighted images (T2‑WI) analysis based on image bynarization and Freeman chain code. This algorithm supports calculation of spinal cord posttraumatic lesion parameters in a half-automatic manner. Results of this algorithm application were comparable to results of manual calculation: no statistical difference were observed between two values.Conclusion. Current method of spinal cord injury volume and structure quantitative assessment simplifies the calculation procedure due to automatization of RoI limitation comparing to manual technique. The level of preciseness is comparable in both methods.Clinical relevance. The developed algorithm optimizes the process of non-invasive control of the performed treatment efficiency according to 7.0 Tesla MRI data.

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