发表机构
Changzhou Jinse Medical Information Technology Co., Ltd.; Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine（常州金色医疗信息技术有限公司; 上海交通大学医学院附属第九人民医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出THA-Flow生成模型，通过AutoencoderKL和三维UNet从术前CT生成三维假体几何，在1355个髋关节数据上验证，实现了THA三维手术规划的生成式AI首次应用。
AI 中文摘要
全髋关节置换术（THA）的术前规划通常被定义为为患者的骨解剖结构选择单一的假体构型与放置方式。然而在实际操作中，相同的解剖结构可能存在多种临床合理的解决方案，使得规划本质上是一个多对一问题，更适合用条件概率分布来表示。我们提出THA-Flow，一种条件流匹配模型，可直接从术前CT生成三维假体几何。独立的AutoencoderKL模型分别压缩术前骨解剖结构与假体几何，三维UNet在空间骨条件及可选结构化假体参数下学习从高斯噪声到假体潜在空间的整流流。回顾性队列包含1149名接受初次THA的患者的1355个髋关节。将术后CT刚性配准至术前CT后，实际术后假体根据骨盆和股骨配准进行独立变换，并表示为双通道截断符号距离场。假体自动编码器在验证集上达到峰值信噪比47.11 dB，结构相似性指数0.9964。生成了代表队列93.4%的7种主要柄模型的完整髋臼和股骨几何。重复的骨条件采样保留了部件位置、对齐方式及主要骨-假体界面，同时允许有限的局部几何变化。据我们所知，THA-Flow是生成式AI首次应用于THA的三维手术规划。
英文摘要
Preoperative planning for total hip arthroplasty (THA) is commonly framed as selecting a single prosthesis configuration and placement for a patient's osseous anatomy. In practice, however, the same anatomy may admit several clinically reasonable solutions, making planning inherently a one-to-many problem that is better represented by a conditional probability distribution. We present THA-Flow, a conditional flow-matching model that generates three-dimensional prosthesis geometry directly from preoperative CT. Separate AutoencoderKL models compress preoperative bone anatomy and prosthesis geometry, while a three-dimensional UNet learns a rectified flow from Gaussian noise to the prosthesis latent space under spatial bone conditioning and optional structured prosthesis parameters. The retrospective cohort comprised 1,355 hips from 1,149 patients undergoing primary THA. Following rigid registration of postoperative CT to preoperative CT, the actual postoperative prostheses were transformed independently according to the pelvic and femoral registrations and represented as a dual-channel truncated signed distance field. The prosthesis autoencoder achieved a peak signal-to-noise ratio of 47.11 dB and a structural similarity index of 0.9964 on the validation set. Complete acetabular and femoral geometries were generated across seven major stem models representing 93.4% of the cohort. Repeated bone-conditioned sampling preserved component position, alignment, and the principal bone-prosthesis interfaces while allowing limited local geometric variation. To our knowledge, THA-Flow represents the first application of generative AI to three-dimensional surgical planning for THA.
Comments17 pages, 7 figures, 2 tables
