SA-Profile：基于超分辨率MRI的自动滑车沟角剖面分析
SA-Profile: Automated Sulcus Angle Profiling from Super-Resolution MRI
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中文总结 AI 辅助
提出SA-Profile框架，利用隐式神经表示重建超分辨率MRI并采用双U-Net检测标志点，实现滑车沟角连续剖面自动测量，误差11.6°，可更好表征滑车发育不良。
中文摘要 AI 辅助
滑车发育不良（TD）是股骨滑车的一种异常，与前膝疼痛和髌骨不稳定相关。滑车沟角（SA）用于评估滑车形态，但通常在单个轴向MR切片上测量，且缺乏关于选择哪个切片的明确指导，因此对切片选择和标志点放置敏感。我们提出了一种从超分辨率MR体数据中进行连续SA剖面分析的自动框架。利用隐式神经表示将临床获取的轴向、冠状和矢状MR扫描结合，重建高分辨率体数据。使用两个标志点检测U-Net模型在滑车区域计算SA测量值。该方法在公开的fastMRI数据集和一个小的内部TD患者队列上进行了评估。与传统的单切片SA手动测量相比，所提出的自动化方法产生了11.6°的平均绝对误差，同时提供了滑车形态的连续表征。人群水平分析显示，公开队列和内部TD队列之间的平均SA剖面存在显著差异，突出了基于剖面的评估在表征TD方面的潜力。通过减少对单个手动选择的轴向切片的依赖，所提出的框架将传统的SA评估扩展到滑车形态的连续剖面描述，无需额外成像，同时在概念上与当前的临床评估保持联系。需要进一步验证。代码可获取：此https URL。
英文摘要
Trochlear dysplasia (TD) is an abnormality of the femoral trochlea associated with anterior knee pain and patellar instability. The sulcus angle (SA) is used to assess trochlear morphology, but it is typically measured on a single axial MR slice with no clear guidance on which to select, making it sensitive to slice selection and landmark placement. We propose an automatic framework for continuous SA profiling from super-resolved MR volumes. Clinically acquired axial, coronal, and sagittal MR scans are combined using implicit neural representations to reconstruct a high-resolution volume. SA measurements are computed across the trochlear region using two landmark detection U-Net models. The approach was evaluated on the public fastMRI dataset and a small in-house cohort of patients with TD. Compared with conventional manual single-slice SA measurements, the proposed automated method yielded a mean absolute error of 11.6$^\circ$ while providing continuous characterization of trochlear morphology. Population-level analysis demonstrated distinct mean SA profiles between the public cohort and the in-house TD cohort, highlighting the potential of profile-based assessment to characterize TD. By reducing reliance on a single manually selected axial slice, the proposed framework extends conventional SA assessment to a continuous profile-based description of trochlear morphology without additional imaging, while remaining conceptually linked to current clinical assessment. Further validation is required. The code is available: https://github.com/wehrlimi/SA_Profile.
发表机构
- University Basel（巴塞尔大学）
- University Children’s Hospital Basel（巴塞尔大学儿童医院）
机构由 AI 辅助整理，请以论文原文为准。
