Implantation of cardiovascular devices, such as stents and aortic valves, is often a costly procedure with little room for error. There are also patient-specific issues in ensuring that the device is appropriate for individual anatomies and pathologies, where the "universal" approach has its limitations. A new solution adopted by medical device researchers is the use of 3D image processing to accurately depict the complex anatomy and performance of the device.

Silico tests and silico-clinical tests can help evaluate healthcare facilities and better understand physiological interactions as a cheaper and effective adjunct to table, animal or clinical tests. Computational modeling and simulations allow researchers, engineers, and physicians to study specific aspects, such as the heart and cardiovascular system, and to make various design modifications. still about the best placement and performance prediction in different circumstances. 

Tackling thrombosis formation in malapposed coronary stents

Researchers at MIT working with Harvard Medical School are exploring new ways to investigate stent thrombosis, such as blood clotting. With more than 500,000 coronary stents implanted in the United States each year in response to coronary artery disease, stent thrombosis is a complication that produces thrombotic material that can lead to occlusion after intervention.

Tabletop flow tests have traditionally been used to study how clots form under hemodynamic conditions, but their ability to replicate in vivo is limited, while clinical trials are not possible due to unexpected thrombotic behavior. An in vitro flow loop setup was used at MIT to simulate blood flow conditions similar to those in the human coronary artery. Stents are distributed in the airways under various conditions during expansion.

The next phase of the project involves the creation of in vitro flow loops with metal stents and blood to model thrombosis formation for different types of misplacement in the physical model. The stent malapposition describes a gap between the stent strut surface and the arterial wall that is greater than the actual strut thickness.

Samples were scanned using microcomputer tomography (micro-CT) to create DICOM files for 3D image processing using Synopsys Simpleware software. Predicted threshold levels from previous experimental studies were used to segment stent fluid volume and clot formation, while smoothing filters were used to obtain continuous structures as part of the 3D visualization workflow. The Simpleware API was later used to obtain pixel values for each mask for each micro-CT section, which were then plotted as an indicator of clot formation. Finally, the standard MATLAB program was used to reposition the strut and calculate the wall distances of the mask pixel values at each location.

Stent clot wall and malapposition metrics as a function of stent length were obtained as 1-D signals and studied by frequency domain analysis and quadrate size correlations. Stent and clot patterns were observed and malaposition locations were identified, including strut correlation and clot locations. Additional knowledge can be obtained by micro-CT imaging and signal analysis to complete the number of table thromboses, including stent position and blood response.

The 3D computational model also allows flow simulations to better understand local and regional stent organization, flow, and anatomy. Looking to the future, this approach promises to generate important data on how you can define different thrombotic mechanisms and reduce the risk of stent placement in patients.

finally, continuous improvements in the use of 3D imaging, artificial intelligence and computing to improve traditional desktop testing and expand the options available to cardiovascular equipment manufacturers.

Silico's studies and tests support traditional testing methods and regulatory submissions, allowing new devices to get to market faster with a greater understanding of their expected performance, honesty and safety. Clinicians can use the results of these studies to support clinical decision-making and provide patients with safer and more optimized diagnostic and surgical solutions, especially in cases where small changes may have a significant difference in outcomes.