Borga Magnus, Thomas E Louise, Romu Thobias, Rosander Johannes, Fitzpatrick Julie, Dahlqvist Leinhard Olof, Bell Jimmy D
Department of Biomedical Engineering, Linköping University, Sweden.
Centre for Medical Image Science and Visualization (CMIV), Linköping University, Sweden.
NMR Biomed. 2015 Dec;28(12):1747-53. doi: 10.1002/nbm.3432. Epub 2015 Nov 2.
Central obesity is the hallmark of a number of non-inheritable disorders. The advent of imaging techniques such as MRI has allowed for a fast and accurate assessment of body fat content and distribution. However, image analysis continues to be one of the major obstacles to the use of MRI in large-scale studies. In this study we assess the validity of the recently proposed fat-muscle quantitation system (AMRA(TM) Profiler) for the quantification of intra-abdominal adipose tissue (IAAT) and abdominal subcutaneous adipose tissue (ASAT) from abdominal MR images. Abdominal MR images were acquired from 23 volunteers with a broad range of BMIs and analysed using sliceOmatic, the current gold-standard, and the AMRA(TM) Profiler based on a non-rigid image registration of a library of segmented atlases. The results show that there was a highly significant correlation between the fat volumes generated by the two analysis methods, (Pearson correlation r = 0.97, p < 0.001), with the AMRA(TM) Profiler analysis being significantly faster (3 min) than the conventional sliceOmatic approach (40 min). There was also excellent agreement between the methods for the quantification of IAAT (AMRA 4.73 ± 1.99 versus sliceOmatic 4.73 ± 1.75 l, p = 0.97). For the AMRA(TM) Profiler analysis, the intra-observer coefficient of variation was 1.6% for IAAT and 1.1% for ASAT, the inter-observer coefficient of variation was 1.4% for IAAT and 1.2% for ASAT, the intra-observer correlation was 0.998 for IAAT and 0.999 for ASAT, and the inter-observer correlation was 0.999 for both IAAT and ASAT. These results indicate that precise and accurate measures of body fat content and distribution can be obtained in a fast and reliable form by the AMRA(TM) Profiler, opening up the possibility of large-scale human phenotypic studies.
中心性肥胖是多种非遗传性疾病的标志。诸如MRI等成像技术的出现使得能够快速、准确地评估身体脂肪含量及分布。然而，图像分析仍然是MRI在大规模研究中应用的主要障碍之一。在本研究中，我们评估了最近提出的脂肪-肌肉定量系统（AMRA™ Profiler）用于从腹部磁共振图像定量腹内脂肪组织（IAAT）和腹部皮下脂肪组织（ASAT）的有效性。从23名具有广泛BMI范围的志愿者获取腹部磁共振图像，并使用当前的金标准SliceOmatic以及基于分割图谱库的非刚性图像配准的AMRA™ Profiler进行分析。结果表明，两种分析方法生成的脂肪体积之间存在高度显著的相关性（Pearson相关系数r = 0.97，p < 0.001），且AMRA™ Profiler分析比传统的SliceOmatic方法（约40分钟）显著更快（约3分钟）。在IAAT定量方法之间也具有极好的一致性（AMRA为4.73 ± 1.99，而SliceOmatic为4.73 ± 1.75升，p = 0.97）。对于AMRA™ Profiler分析，IAAT的观察者内变异系数为1.6%，ASAT为1.1%，IAAT的观察者间变异系数为1.4%，ASAT为1.2%，IAAT的观察者内相关性为0.998，ASAT为0.999，IAAT和ASAT的观察者间相关性均为0.999。这些结果表明，AMRA™ Profiler能够以快速且可靠的方式获得身体脂肪含量及分布的精确测量结果，为大规模人类表型研究开辟了可能性。
