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Patient-Specific Atrium Models for Training and Pre-Procedure Surgical Planning

机译:患者特定的培训中的中庭模型进行培训和预先进行手术规划

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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.
机译:微创心脏手术需要逆间穿刺,如心房消融和Mitraclip?二尖瓣修复变得越来越普遍。这些程序是对击败心脏进行的,并要求临床医生依赖于图像引导技术。对于复杂或患病解剖学的病例,其中荧光镜和超声心动图来说是难以解释的,临床医生可以从患者特异性心房模型中受益,可用于培训,手术规划和新设备的验证和指导技术的验证。患者心脏的计算机断层扫描(CT)图像被分段并用于产生几何模型以产生特定于患者的心房幻影。使用快速原型设计,几何模型被转换为物理表示并用于构建模具。然后使用组织模拟材料模塑ATRIA并使用CT成像。将结果图像分段并用于生成可以注册到原始患者数据的点云数据集。比较两点云的绝对距离和评估以确定模型的准确性。结果将模型模型点云与原始数据集进行比较,导致最大欧几里德距离误差为4.5毫米,平均误差为0.5毫米,标准偏差为0.6毫米。使用我们的工作流程来创建心房模型,可能会在术前规划中考虑潜在的复杂性,特别是复杂的修复。参与计划和执行该程序的临床医生获得的信息应导致患者的程序时间和更好的结果。

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