Improving Accuracy and Reproducibility of Cartilage T2 Mapping in the OAI Dataset Through Extended Phase Graph Modeling.

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-10-28 DOI:10.1002/jmri.29646
Marco Barbieri, Anthony A Gatti, Feliks Kogan
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引用次数: 0

Abstract

Background: The Osteoarthritis Initiative (OAI) collected extensive imaging data, including Multi-Echo Spin-Echo (MESE) sequences for measuring knee cartilage T2 relaxation times. Mono-exponential models are used in the OAI for T2 fitting, which neglects stimulated echoes and B1 inhomogeneities. Extended Phase Graph (EPG) modeling addresses these limitations but has not been applied to the OAI dataset.

Purpose: To assess how different fitting methods, including EPG-based and exponential-based approaches, affect the accuracy and reproducibility of cartilage T2 in the OAI dataset.

Study type: Retrospective.

Population: From OAI dataset, 50 subjects, stratified by osteoarthritis (OA) severity using Kellgren-Lawrence grades (KLG), and 50 subjects without OA diagnosis during OAI duration were selected (each group: 25 females, mean ages ~61 years).

Field strength/sequence: 3-T, two-dimensional (2D) MESE sequence.

Assessment: Femoral and tibial cartilages were segmented from DESS images, subdivided into seven sub-regions, and co-registered to MESE. T2 maps were obtained using three EPG-based methods (nonlinear least squares, dictionary matching, and deep learning) and three mono-exponential approaches (linear least squares, nonlinear least squares, and noise-corrected exponential). Average T2 values within sub-regions were obtained. Pair-wise agreement among fitting methods was evaluated using the stratified subjects, while reproducibility using healthy subjects. Each method's T2 accuracy and repeatability varying signal-to-noise ratio (SNR) were assessed with simulations.

Statistical tests: Bland-Altman analysis, Lin's concordance coefficient, and coefficient of variation assessed agreement, repeatability, and reproducibility. Statistical significance was set at P-value <0.05.

Results: EPG-based methods demonstrated superior T2 accuracy (mean absolute error below 0.5 msec at SNR > 100) compared to mono-exponential methods (error > 7 msec). EPG-based approaches had better reproducibility, with limits of agreement 1.5-5 msec narrower than exponential-based methods. T2 values from EPG methods were systematically 10-17 msec lower than those from mono-exponential fitting.

Data conclusion: EPG modeling improved agreement and reproducibility of cartilage T2 mapping in subjects from the OAI dataset.

Evidence level: 3 TECHNICAL EFFICACY: Stage 1.

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通过扩展相图建模提高 OAI 数据集中软骨 T2 映射的准确性和可重复性
背景:骨关节炎倡议(OAI)收集了大量成像数据,包括用于测量膝关节软骨T2弛豫时间的多回波自旋回波(MESE)序列。OAI 采用单指数模型进行 T2 拟合,忽略了刺激回波和 B1 不均匀性。目的:评估不同的拟合方法(包括基于 EPG 和基于指数的方法)如何影响 OAI 数据集中软骨 T2 的准确性和可重复性:研究对象从 OAI 数据集中选择了 50 名受试者,根据骨关节炎(OA)严重程度使用 Kellgren-Lawrence 分级(KLG)进行分层,以及 50 名在 OAI 持续时间内未确诊 OA 的受试者(每组:25 名女性,平均年龄约 61 岁):场强/序列:3-T,二维(2D)MESE 序列:评估:从 DESS 图像中分割股骨和胫骨软骨,将其细分为七个子区域,并与 MESE 共同登记。使用三种基于 EPG 的方法(非线性最小二乘法、字典匹配法和深度学习法)和三种单指数法(线性最小二乘法、非线性最小二乘法和噪声校正指数法)获得 T2 图。得出了子区域内的平均 T2 值。使用分层受试者评估了拟合方法之间的配对一致性,同时使用健康受试者评估了可重复性。每种方法的 T2 准确性和可重复性随信噪比(SNR)的变化进行了模拟评估:统计检验:采用 Bland-Altman 分析、Lin 一致性系数和变异系数评估一致性、重复性和再现性。统计显著性以 P 值为标准:与单指数方法(误差大于 7 毫秒)相比,基于 EPG 的方法显示出更高的 T2 准确性(信噪比大于 100 时,平均绝对误差低于 0.5 毫秒)。基于 EPG 的方法具有更好的可重复性,与基于指数的方法相比,其一致性范围缩小了 1.5-5 毫秒。EPG 方法得出的 T2 值比单指数拟合方法得出的 T2 值系统性地低 10-17 毫秒:EPG建模提高了OAI数据集中受试者软骨T2映射的一致性和可重复性。
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CiteScore
7.20
自引率
4.30%
发文量
567
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