PDF(Pubmed)
Abstract：
UNASSIGNED: Magnetic resonance imaging (MRI) cartilage transverse relaxation time (T2) reflects cartilage composition, mechanical properties, and early osteoarthritis (OA). T2 analysis requires cartilage segmentation. In this study, we clinically validate fully automated T2 analysis at 1.5 Tesla (T) in anterior cruciate ligament (ACL)-injured and healthy knees.
UNASSIGNED: We studied 71 participants: 20 ACL-injured patients with, and 22 without dynamic knee instability, 13 with surgical reconstruction, and 16 healthy controls. Sagittal multi-echo-spin-echo (MESE) MRIs were acquired at baseline and 1-year follow-up. Femorotibial cartilage was segmented manually; a convolutional neural network (CNN) algorithm was trained on MRI data from the same scanner.
UNASSIGNED: Dice similarity coefficients (DSCs) of automated versus manual segmentation in the 71 participants were 0.83 (femora) and 0.89 (tibiae). Deep femorotibial T2 was similar between automated (45.7±2.6 ms) and manual (45.7±2.7 ms) segmentation (P=0.828), whereas superficial layer T2 was slightly overestimated by automated analysis (53.2±2.2 vs. 52.1±2.1 ms for manual; P<0.001). T2 correlations were r=0.91-0.99 for deep and r=0.86-0.97 for superficial layers across regions. The only statistically significant T2 increase over 1 year was observed in the deep layer of the lateral femur [standardized response mean (SRM) =0.58 for automated vs. 0.52 for manual analysis; P<0.001]. There was no relevant difference in baseline/longitudinal T2 values/changes between the ACL-injured groups and healthy participants, with either segmentation method.
UNASSIGNED: This clinical validation study suggests that automated cartilage T2 analysis from MESE at 1.5T is technically feasible and accurate. More efficient 3D sequences and longer observation intervals may be required to detect the impact of ACL injury induced joint instability on cartilage composition (T2).
UNASSIGNED: We studied 71 participants: 20 ACL-injured patients with, and 22 without dynamic knee instability, 13 with surgical reconstruction, and 16 healthy controls. Sagittal multi-echo-spin-echo (MESE) MRIs were acquired at baseline and 1-year follow-up. Femorotibial cartilage was segmented manually; a convolutional neural network (CNN) algorithm was trained on MRI data from the same scanner.
UNASSIGNED: Dice similarity coefficients (DSCs) of automated versus manual segmentation in the 71 participants were 0.83 (femora) and 0.89 (tibiae). Deep femorotibial T2 was similar between automated (45.7±2.6 ms) and manual (45.7±2.7 ms) segmentation (P=0.828), whereas superficial layer T2 was slightly overestimated by automated analysis (53.2±2.2 vs. 52.1±2.1 ms for manual; P<0.001). T2 correlations were r=0.91-0.99 for deep and r=0.86-0.97 for superficial layers across regions. The only statistically significant T2 increase over 1 year was observed in the deep layer of the lateral femur [standardized response mean (SRM) =0.58 for automated vs. 0.52 for manual analysis; P<0.001]. There was no relevant difference in baseline/longitudinal T2 values/changes between the ACL-injured groups and healthy participants, with either segmentation method.
UNASSIGNED: This clinical validation study suggests that automated cartilage T2 analysis from MESE at 1.5T is technically feasible and accurate. More efficient 3D sequences and longer observation intervals may be required to detect the impact of ACL injury induced joint instability on cartilage composition (T2).
摘要：
■磁共振成像（MRI）软骨横向松弛时间（T2）反映了软骨组成，机械性能，和早期骨关节炎（OA）。T2分析需要软骨分割。在这项研究中,我们在临床上验证了前交叉韧带（ACL）损伤和健康膝盖在1.5特斯拉（T）的全自动T2分析。
■我们研究了71名参与者：20名ACL损伤患者，和22没有动态的膝盖不稳定，13进行手术重建，和16个健康对照。在基线和1年随访时获得矢状多回波自旋回波（MESE）MRI。手动分割股骨软骨;对来自同一扫描仪的MRI数据训练卷积神经网络(CNN)算法。
■71名参与者的自动分割与手动分割的骰子相似性系数（DSC）分别为0.83（股骨）和0.89（胫骨）。在自动分割（45.7±2.6ms）和手动分割（45.7±2.7ms）之间，股深T2相似（P=0.828），而表层T2通过自动分析略有高估(53.2±2.2vs.手动52.1±2.1ms；P<0.001）。深层的T2相关性为r=0.91-0.99，跨区域的表层的T2相关性为r=0.86-0.97。在股骨外侧的深层观察到1年内唯一具有统计学意义的T2增加[自动化与自动化的标准化反应平均值（SRM）=0.58手动分析为0.52；P<0.001]。ACL损伤组和健康参与者之间的基线/纵向T2值/变化没有相关差异，无论采用哪种分割方法。
■这项临床验证研究表明，从1.5T的MESE进行的自动化软骨T2分析在技术上是可行且准确的。可能需要更有效的3D序列和更长的观察间隔来检测ACL损伤诱导的关节不稳定性对软骨组成(T2)的影响。
■我们研究了71名参与者：20名ACL损伤患者，和22没有动态的膝盖不稳定，13进行手术重建，和16个健康对照。在基线和1年随访时获得矢状多回波自旋回波（MESE）MRI。手动分割股骨软骨;对来自同一扫描仪的MRI数据训练卷积神经网络(CNN)算法。
■71名参与者的自动分割与手动分割的骰子相似性系数（DSC）分别为0.83（股骨）和0.89（胫骨）。在自动分割（45.7±2.6ms）和手动分割（45.7±2.7ms）之间，股深T2相似（P=0.828），而表层T2通过自动分析略有高估(53.2±2.2vs.手动52.1±2.1ms；P<0.001）。深层的T2相关性为r=0.91-0.99，跨区域的表层的T2相关性为r=0.86-0.97。在股骨外侧的深层观察到1年内唯一具有统计学意义的T2增加[自动化与自动化的标准化反应平均值（SRM）=0.58手动分析为0.52；P<0.001]。ACL损伤组和健康参与者之间的基线/纵向T2值/变化没有相关差异，无论采用哪种分割方法。
■这项临床验证研究表明，从1.5T的MESE进行的自动化软骨T2分析在技术上是可行且准确的。可能需要更有效的3D序列和更长的观察间隔来检测ACL损伤诱导的关节不稳定性对软骨组成(T2)的影响。
