Name
Abstract | ||
|---|---|---|
Minimally invasive cardiac procedures requiring a trans-septal puncture such as atrial ablation and MitraClip r mitral valve repair are becoming increasingly common. These procedures are performed on the beating heart, and require clinicians to rely on image-guided techniques. For cases of complex or diseased anatomy, in which fluoroscopic and echocardiography images can be difficult to interpret, clinicians may benefit from patient-specific atrial models that can be used for training, surgical planning, and the validation of new devices and guidance techniques. Computed tomography (CT) images of a patient's heart were segmented and used to generate geometric models to create a patient-specific atrial phantom. Using rapid prototyping, the geometric models were converted into physical representations and used to build a mold. The atria were then molded using tissue-mimicking materials and imaged using CT. The resulting images were segmented and used to generate a point cloud data set that could be registered to the original patient data. The absolute distance of the two point clouds was compared and evaluated to determine the model's accuracy. The result when comparing the molded model point cloud to the original data set, resulted in a maximum Euclidean distance error of 4.5 mm, an average error of 0.5 mm and a standard deviation of 0.6 mm. Using our workflow for creating atrial models, potential complications, particularly for complex repairs, may be accounted for in pre-operative planning. The information gained by clinicians involved in planning and performing the procedure should lead to shorter procedural times and better outcomes for patients. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1117/12.2249693 | Proceedings of SPIE |
Keywords | Field | DocType |
Patient-Specific,3D printing,Molding,Atria,Surgical Planning,Minimally Invasive,Cardiac | Computer vision,Surgical planning,Euclidean distance,Imaging phantom,MitraClip,Atrial Ablation,Mitral valve repair,Artificial intelligence,Point cloud,Pre-Procedure,Physics | Conference |
Volume | ISSN | Citations |
10135 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Justin Laing | 1 | 0 | 0.34 |
| John Moore | 2 | 166 | 25.41 |
| Daniel Bainbridge | 3 | 119 | 16.28 |
| Maria Drangova | 4 | 0 | 0.34 |
| Terry M. Peters | 5 | 1335 | 181.71 |