Wavelet-based characterization of vertebral trabecular bone structure from magnetic resonance images at 3 T compared with micro-computed tomographic measurements

被引:14
|
作者
Krug, Roland [1 ]
Carballido-Gamio, Julio
Burghardt, Andrew J.
Haase, Sebastian
Sedat, John W.
Moss, William C.
Majumdar, Sharmila
机构
[1] Univ Calif San Francisco, MQIR, Dept Radiol, San Francisco, CA 94143 USA
[2] Univ Calif San Francisco, Dept Biochem & Biophys, San Francisco, CA 94143 USA
[3] Lawrence Livermore Natl Lab, Livermore, CA USA
关键词
magnetic resonance imaging; micro-CT; osteoporosis; trabecular thickness; wavelets;
D O I
10.1016/j.mri.2006.09.020
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Trabecular bone structure and bone density contribute to the strength of bone and are important in the study of osteoporosis. Wavelets are a powerful tool in characterizing and quantifying texture in an image. The purpose of this study was to validate wavelets as a tool in computing trabecular bone thickness directly from gray-level images. To this end, eight cylindrical cores of vertebral trabecular bone were imaged using 3-T magnetic resonance imaging (MRI) and micro-computed tomography (mu CT). Thickness measurements of the trabecular bone from the wavelet-based analysis were compared with standard 2D structural parameters analogous to bone histomorphometry (MR images) and direct 3D distance transformation methods (mu CT images). Additionally, bone volume fraction was determined using each method. The average difference in trabecular thickness between the wavelet and standard methods was less than the size of 1 pixel size for both MRI and mu CT analysis. A correlation (R) of .94 for mu CT measurements and that of .52 for MRI were found for the bone volume fraction. Based on these results, we conclude that wavelet-based methods deliver results comparable with those from established MR histomorphometric measurements. Because the wavelet transform is more robust with respect to image noise and operates directly on gray-level images, it could be a powerful tool for computing structural bone parameters from MR images acquired using high resolution and thus limited signal scenarios. (c) 2007 Elsevier Inc. All rights reserved.
引用
收藏
页码:392 / 398
页数:7
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