通过全自动分割比较冠状面快速低角度激发序列（FLASH）和矢状面双回波稳态磁共振成像（MRI）检测软骨纵向厚度变化——来自美国国立卫生研究院（NIH）生物标志物队列的数据
Comparison between coronal FLASH and sagittal double echo steady state MRI in detecting longitudinal cartilage thickness change by fully automated segmentation - Data from the FNIH biomarker cohort.
作者信息
Eckstein Felix, Chaudhari Akshay S, Hunter David J, Wirth Wolfgang
机构信息
Research Program for Musculoskeletal Imaging, Center for Anatomy and Cell Biology & Ludwig Boltzmann Institute of Arthritis and Rehabilitation (LBIAR), Paracelsus Medical University (PMU) Salzburg, Austria.
Chondrometrics GmbH, Freilassing, Germany.
出版信息
Osteoarthr Cartil Open. 2025 Aug 5;7(3):100657. doi: 10.1016/j.ocarto.2025.100657. eCollection 2025 Sep.
OBJECTIVE
Artificial intelligence (AI-) based automated cartilage analysis demonstrated similar sensitivity to change and only slighty inferior differentiation between radiographic progressors and non-progressors compared with manual segmentation. However, this finding was based on DESS MRI from the Osteoarthritis Initiative (OAI), whereas the vast majority of multicenter clinical trials rely on T1-weighted gradient echo (e.g. FLASH). Here we directly compare fully automated analysis of coronal FLASH vs. sagittal DESS, and vs. manually segmented DESS, in a sample with both FLASH and DESS MRI acquisitions.
DESIGN
Convolutional neural network (CNN) algorithms were trained on 86 radiographically osteoarthritic knees with manual expert segmentation of the medial and lateral femorotibial cartilages (coronal FLASH and sagittal DESS). Post-processing involved automated registration of CNN-based subchondral bone segmentation to reference areas. The models were applied to baseline and two-year follow-up MRIs of radiographic progressor and non-progressor knees in the Foundation of the NIH Biomarker sample of the OAI.
RESULTS
Of the 322 FNIH knees with both FLASH and DESS; 157 were radiographic progressors. Sensitivity to medial femorotibial cartilage thickness change (standardized response mean) in the progressor subcohort was -0.81 for FLASH (automated analysis), -0.74 for automatically, and -0.72 for manually segmented DESS. Differentiation from non-progressors (Cohen's D) was -0.82. -0.70, and -0.87, respectively.
CONCLUSIONS
Fully automated, AI-based cartilage segmentation with advanced post-processing reveals that coronal FLASH is at least as discriminative between radiographic progressor vs. non-progressor knees as sagittal DESS MRI. Yet, performance of fully automated segmentation is slightly inferior to manual analysis with expert quality control.
TRIAL ID
Clinicaltrials.gov identification: NCT00080171.
目的
基于人工智能（AI）的自动软骨分析显示，与手动分割相比，其对变化的敏感性相似，在区分影像学进展者和非进展者方面仅略逊一筹。然而，这一发现是基于骨关节炎倡议（OAI）的双激发稳态采集（DESS）磁共振成像（MRI），而绝大多数多中心临床试验依赖于T1加权梯度回波（如快速小角度激发（FLASH））。在此，我们在同时采集了FLASH和DESS MRI的样本中，直接比较冠状面FLASH、矢状面DESS以及手动分割的DESS的全自动分析结果。
设计
利用86个影像学诊断为骨关节炎的膝关节，对股骨胫侧软骨（冠状面FLASH和矢状面DESS）进行手动专家分割，训练卷积神经网络（CNN）算法。后处理包括将基于CNN的软骨下骨分割自动配准到参考区域。将这些模型应用于OAI的美国国立卫生研究院生物标志物样本库中影像学进展者和非进展者膝关节的基线和两年随访MRI。
结果
在322个同时有FLASH和DESS图像的FNIH膝关节中，157个为影像学进展者。在进展者亚组中，对于股骨胫侧内侧软骨厚度变化的敏感性（标准化反应均值），FLASH（自动分析）为-0.81，自动分割的DESS为-0.74，手动分割的DESS为-0.72。与非进展者的区分度（科恩d值）分别为-0.82、-0.70和-0.87。
结论
基于AI的全自动软骨分割结合先进的后处理显示，冠状面FLASH在区分影像学进展者和非进展者膝关节方面至少与矢状面DESS MRI一样具有鉴别力。然而，全自动分割的性能略逊于有专家质量控制的手动分析。
试验编号
Clinicaltrials.gov标识符：NCT00080171。
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