Coronary Artery Diseases (CAD) are a major cause of heart attack events which may lead to disabilities and death. Recent advances in CAD diagnosis include computed aided and physical 3D printed models. Both methods are computer coronary reconstruction dependent.
This work enlists a comprehensive method for computer coronary reconstruction and physical model 3D printed, which is generated after a segmentation process from the region of interest (ROI).
Initial step of the presented method includes a precise strategy for image acquisition through Computed Tomography (CT), where two points must be observed: the first point is to synchronize the time of image acquisition at CT with heart beat, in order to obtain a static image, this process is achieved by using electrocardiogram signal to adjust the time of the CT image acquisition. Second point is to correctly plan the contrast application to make sure that, during image acquisition, contrast will be at coronaries. This initial step is highly dependent on professional abilities.
Contrast differentiation in the coronary lumen is used as a guideline for segmentation process, where this lumen is separated from all other structures. There are several softwares that can be used for CT image segmentation at our laboratory, segmentation was performed with an open source software InVesalius (CEMPRA, Campinas, Brazil).
Two methods were previously developed validating segmentation process: one was analysis of mean absorbed energy in the coronary lumen determining vessel size and other was a metrological analysis comparing the measurements from segmented model with measurements from other exams as reference.
Extension which may be used for computer aided analysis or 3D printing. A case study from patient with coronary arthrosclerosis was performed using this methodology. In this case, two options of treatment could be applied: surgical procedure or pharmacological care. Each treatment is focused on the coronary stenosis stage The computer reconstructed model from this method was used in a fluid dynamic simulation, pointing that patient disease had small chance to evolve in a short period, causing a heart attack and, in this case; just a pharmacological care preventing stenosis growth was the best option.
In other cardiovascular area, a few initiatives of reconstructed “patient specific” models were found. This is due to the difficulties faced in segmenting different tissues, as muscles, arteries and vessels.
This methodology presents a comprehensive process to segment coronaries arteries, which starts with the knowledge of the CT team in order to strategically focus on how to guide the segmentation by the correct contrast diffusion.
In our institution, this reconstruction has been helping physicians to take more secure decisions on difficult cases by complementing them through new methods of visualization with 3D printed models.