Aim. To evaluate the results of surgical intervention planning using three-dimensional models based on magnetic resonance imaging in patients with postinfarction left ventricular aneurysms.Material and Methods. Two groups of patients with postinfarction left ventricular aneurysm (PLVA) were included in the study, totaling 41 patients. The first (experimental) group included 17 patients diagnosed with PLVA by magnetic resonance imaging (MRI), and surgical intervention planning was performed using a 3D model of the heart. The control group comprised 24 patients in whom PLVA was diagnosed by echocardiography (TTE) or ventriculography, and surgical intervention planning was performed using traditional two-dimensional slice images.Results. Comparison of full perfusion under cardiopulmonary bypass (CPB) showed statistically significant differences between the groups: this parameter was 60 [56; 68] min in group 1 vs. 71 [61; 84] min in group 2, which was significantly higher (p = 0.043). There were no significant differences in total operation time (280 [265; 320] min in group 1 vs. 263 [248; 283] min in group 2, p = 0.055), overall CPB time (93 [86; 109] min in group 1 vs. 104 [83; 109] min in group 2, p = 0.653), and partial CPB time (31 [26; 39] min in group 1 vs. 27 [21; 32] min in group 2, p = 0.127).Conclusion. The use of 3D models to support surgeons for PLVA correction makes it possible to determine the type of reconstructive surgery, practice the main stages of the upcoming intervention, and reduce the time of full perfusion under CPB during its implementation.
Computed tomography is now widely used in cardiac surgery as a method of non-destructive study of internal structure of objects, including specific tasks, such as mathematical modeling of physiological processes, surgical interventions in augmented reality, 3D printing, and radiomics. One of the key steps in creating a 3D model from computed tomography data is segmentation the process of selecting objects in the image. Currently, there are several approaches to automating the segmentation process, including image processing methods, texture analysis and machine learning algorithms (in particular, clustering). Image processing methods are the simplest of the presented approaches and are found in various applications for segmentation of tomographic data. This paper reviews the advantages and disadvantages of various image processing methods (threshold, region growing, contour detection, and morphological watersheds) as tools for automated cardiac segmentation from computed tomography data. It was revealed that computed tomography images have characteristic features affecting the segmentation process (presence of noise, partial volume effect, etc.). The choice of the segmentation method is based on the brightness characteristics of the area of interest and also requires knowledge of the subject area, so it should be performed by a specialist with competence in anatomy and digital image processing. As independent methods of automated segmentation, the listed methods are applicable only in relatively simple cases (selection of homogeneous or high-contrast areas), otherwise, a combination of these methods, the use of machine learning algorithms or manual correction of the results is required.
Aim of study. To evaluate the geometric deviations associated with creation of physical objects from computed tomography data using computer-aided design and additive manufacturing. Materials and methods. The source object was created using the FreeCAD application; Blender and Meshmixer software was used for polygon meshes correction and transformation. 3D printing was carried out on an Ender-3 printer with copper-impregnated polylactide plastic BFCopper. Scanning was performed using a 128-slice tomograph Philips Ingenuity CT. A series of tomographic images were processed in 3DSlicer software, used to create virtual models by semiautomatic segmentation with threshold values of 500 HU, 0 HU, -500 HU, -750 HU and manual segmentation. Reproduced and reference polygon meshes were compared using Iterative Closest Point algorithm in CloudCompare software. Results. Reproduced models volume exceeded the volume of respective reference models by 1-27%. The average point cloud linear deviation values of reproduced models from the reference ones were 0.03-0.41 mm. A significant correlation between integral sums of linear deviations and changes in the volume of reproduced models was shown using Spearman's rank correlation coefficient ( = 0.83; temp = 5.27, significance level p = 0.05). Conclusion. The geometry of the reproduced object changes inevitably, while the linear deviations depend more on the chosen segmentation method rather than on the overall size of the model or its structures. Manual segmentation method can lead to greater linear deviations, though it allows to save all the necessary structures.
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