{"title":"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","authors":"Marco Barbieri, Anthony A Gatti, Feliks Kogan","doi":"10.1002/jmri.29646","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The Osteoarthritis Initiative (OAI) collected extensive imaging data, including Multi-Echo Spin-Echo (MESE) sequences for measuring knee cartilage T<sub>2</sub> relaxation times. Mono-exponential models are used in the OAI for T<sub>2</sub> fitting, which neglects stimulated echoes and B<sub>1</sub> inhomogeneities. Extended Phase Graph (EPG) modeling addresses these limitations but has not been applied to the OAI dataset.</p><p><strong>Purpose: </strong>To assess how different fitting methods, including EPG-based and exponential-based approaches, affect the accuracy and reproducibility of cartilage T<sub>2</sub> in the OAI dataset.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Population: </strong>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).</p><p><strong>Field strength/sequence: </strong>3-T, two-dimensional (2D) MESE sequence.</p><p><strong>Assessment: </strong>Femoral and tibial cartilages were segmented from DESS images, subdivided into seven sub-regions, and co-registered to MESE. T<sub>2</sub> 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 T<sub>2</sub> 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 T<sub>2</sub> accuracy and repeatability varying signal-to-noise ratio (SNR) were assessed with simulations.</p><p><strong>Statistical tests: </strong>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.</p><p><strong>Results: </strong>EPG-based methods demonstrated superior T<sub>2</sub> 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. T<sub>2</sub> values from EPG methods were systematically 10-17 msec lower than those from mono-exponential fitting.</p><p><strong>Data conclusion: </strong>EPG modeling improved agreement and reproducibility of cartilage T<sub>2</sub> mapping in subjects from the OAI dataset.</p><p><strong>Evidence level: </strong>3 TECHNICAL EFFICACY: Stage 1.</p>","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving Accuracy and Reproducibility of Cartilage T<sub>2</sub> Mapping in the OAI Dataset Through Extended Phase Graph Modeling.\",\"authors\":\"Marco Barbieri, Anthony A Gatti, Feliks Kogan\",\"doi\":\"10.1002/jmri.29646\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The Osteoarthritis Initiative (OAI) collected extensive imaging data, including Multi-Echo Spin-Echo (MESE) sequences for measuring knee cartilage T<sub>2</sub> relaxation times. Mono-exponential models are used in the OAI for T<sub>2</sub> fitting, which neglects stimulated echoes and B<sub>1</sub> inhomogeneities. Extended Phase Graph (EPG) modeling addresses these limitations but has not been applied to the OAI dataset.</p><p><strong>Purpose: </strong>To assess how different fitting methods, including EPG-based and exponential-based approaches, affect the accuracy and reproducibility of cartilage T<sub>2</sub> in the OAI dataset.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Population: </strong>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).</p><p><strong>Field strength/sequence: </strong>3-T, two-dimensional (2D) MESE sequence.</p><p><strong>Assessment: </strong>Femoral and tibial cartilages were segmented from DESS images, subdivided into seven sub-regions, and co-registered to MESE. T<sub>2</sub> 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 T<sub>2</sub> 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 T<sub>2</sub> accuracy and repeatability varying signal-to-noise ratio (SNR) were assessed with simulations.</p><p><strong>Statistical tests: </strong>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.</p><p><strong>Results: </strong>EPG-based methods demonstrated superior T<sub>2</sub> 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. T<sub>2</sub> values from EPG methods were systematically 10-17 msec lower than those from mono-exponential fitting.</p><p><strong>Data conclusion: </strong>EPG modeling improved agreement and reproducibility of cartilage T<sub>2</sub> mapping in subjects from the OAI dataset.</p><p><strong>Evidence level: </strong>3 TECHNICAL EFFICACY: Stage 1.</p>\",\"PeriodicalId\":3,\"journal\":{\"name\":\"ACS Applied Electronic Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Electronic Materials\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1002/jmri.29646\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/jmri.29646","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Improving Accuracy and Reproducibility of Cartilage T2 Mapping in the OAI Dataset Through Extended Phase Graph Modeling.
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.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
