Anterior vertebral tethering (AVT) is a non-invasive spine surgery technique, treating severe spine deformations and preserving lower back mobility. However, patient positioning and surgical strategies greatly influences postoperative results. Predicting the upright geometry from pediatric spines is needed to optimize patient positioning in the operating room (OR) and improve surgical outcomes, but remains a complex task due to immature bone properties. We propose a framework used in the OR predicting the upright spine geometry at the first visit following surgery in idiopathic scoliosis patients. The approach first creates a 3D model of the spine while the patient is on the operating table. For this, multiview Transformers that combine images from different viewpoints are used to generate the intraoperative pose. The postoperative upright shape is then predicted on-the-fly using implicit neural fields, which are trained from geometries at different time points and conditioned with surgical parameters. A Signed Distance Function for shape constellations is used to handle the variability in spine appearance, capturing a disentangled latent domain of the articulation vectors, with separate encoding vectors representing both articulation and shape parameters. A regularization criterion based on a pre-trained group-wise trajectory of spine transformations generates complete spine models. A training set of 652 patients with 3D models was used to train the model, tested on a distinct cohort of 83 surgical patients. The framework based on neural kernels predicted upright 3D geometries with a mean 3D error of 1.3±0.5mm in landmarks points, and IoU of 95.9% in vertebral shapes when compared to actual postop models, falling within the acceptable margins of error below 2 mm.

中文翻译：

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使用条件神经核场预测手术室中的直立关节脊柱形状


前椎栓系术 （AVT） 是一种非侵入性脊柱手术技术，可治疗严重的脊柱变形并保持下背部活动度。然而，患者体位和手术策略对术后结果有很大影响。为了优化患者在手术室 （OR） 中的定位并改善手术结果，需要预测儿科脊柱的直立几何形状，但由于骨骼特性不成熟，这仍然是一项复杂的任务。我们提出了一个用于手术室的框架，用于预测特发性脊柱侧弯患者手术后第一次就诊时的直立脊柱几何形状。该方法首先在患者坐在手术台上时创建脊柱的 3D 模型。为此，使用组合来自不同视点的图像的多视图 Transformer 来生成术中姿势。然后使用隐式神经场动态预测术后直立形状，这些神经场根据不同时间点的几何形状进行训练，并使用手术参数进行调节。形状星座的有符号距离函数用于处理脊柱外观的变化，捕获关节向量的解缠潜在域，使用单独的编码向量表示关节和形状参数。基于预先训练的 spine 转换的组级轨迹的正则化标准生成完整的 spine 模型。使用 652 名具有 3D 模型的患者的训练集来训练模型，并在 83 名手术患者的不同队列上进行了测试。基于神经内核的框架预测了直立的 3D 几何形状，与实际的术后模型相比，地标点的平均 3D 误差为 1.3±0.5 毫米，椎体形状的 IoU 为 95.9%，落在 2 毫米以下的可接受误差范围内。