Computerized methods for X-ray-based small bone densitometry
Section snippets
Introduction and background
A major difficulty in evaluating in vivo changes in small animal studies has been the lack of accurate and precise methods to non-invasively determine bone quality. In studies of osteoporosis and osteopenia (bone loss) in human patients [1], [2], [3], [4], [5] and in animal models such as the hindlimb suspension model [6], [7], [8], the state of the bone is routinely assessed by measurement and interpretation of bone mineral density (BMD). BMD is the amount of bone mineral, calcium
Design considerations
We perform analysis of BMD routinely in our lab [21]. When attempting to use mice instead of rats, however, it became obvious that resolution limitations of DEXA would preclude us from using this method. In addition, initially the outline of the bone was traced manually, leading to long analysis times and potential subjective observer influences. For this reason, we developed a computer algorithm based on the methods described by Colbert et al. [17] and Colbert and Bachtell [22]. The main
Image acquisition and processing
All X-ray images were exposed on a HP 43805N X-ray machine using Kodak X-Omat TL film. The developed film was digitized at 500 dpi and 12 bits per pixel (bpp) using an Agfa Duoscan 2500 scanner. Image analysis was performed with Scion Image (Scion Corp., Frederick, MA), which is a PC port of NIH Image. Algorithms were designed using the NIH Image macro-language. A step wedge used as calibration phantom was custom-machined from aluminum with 0.5 mm step height increments.
Bone density and geometry phantoms
To characterize the
Status report
The implementation of the algorithms using NIH Image has been used in our lab for 2 years. Generally, it was found that both the wedge detection and the bone contour detection worked satisfactorily. Representative examples are given below.
Lessons learned
The algorithm has been routinely used for over 2 years. The main goals (shorter analysis time and reduced random scatter) have been achieved. Particularly effective was the introduction of the soft tissue correction, because the X-ray attenuation caused by soft tissue was in the same order of magnitude as the attenuation caused by the inner cavity of the femoral bone. In other words, bone density appeared elevated by up to 100%, the exact value partially depending on the thickness of the
Future plans
Since the algorithm has been proven to be robust and reliable, future plans include an implementation using the C programming language. First experiments using the GTK visual toolkit showed that the two disadvantages of the NIH Image implementation could be overcome. Bone segmentation requires about 100–200 ms in a compiled language, which allows real-time manipulation of the parameters. Also, array handling and post-processing of the segmentation results allow the detection of segmentation
Acknowledgements
We thank David Simonton at the Radiology Research Lab of the San Diego Veterans Administration Hospital for the preparation of the radiographs. This work was funded by NIH grants AR-46797 and HL-40696.
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