Minimally invasive mitral valve repair procedures including MitraClip® are becoming increasingly common. For cases of complex or diseased anatomy, clinicians may benefit from using a patient-specific cardiac phantom for training, surgical planning, and the validation of devices or techniques. An imaging compatible cardiac phantom was developed to simulate a MitraClip® procedure. The phantom contained a patient-specific cardiac model manufactured using tissue mimicking materials. To evaluate accuracy, the patient-specific model was imaged using computed tomography (CT), segmented, and the resulting point cloud dataset was compared using absolute distance to the original patient data. The result, when comparing the molded model point cloud to the original dataset, resulted in a maximum Euclidean distance error of 7.7 mm, an average error of 0.98 mm, and a standard deviation of 0.91 mm. The phantom was validated using a MitraClip® device to ensure anatomical features and tools are identifiable under image guidance. Patient-specific cardiac phantoms may allow for surgical complications to be accounted for preoperative planning. The information gained by clinicians involved in planning and performing the procedure should lead to shorter procedural times and better outcomes for patients.
Minimally invasive cardiac procedures requiring a trans-septal puncture such as atrial ablation and MitraClip® 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.
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