Ms. Janet Blumenfeld Profile

Janet Blumenfeld

at Univ of California San Francisco

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Proceedings Article | 26 March 2008 Paper

Three-dimensional image registration of MR proximal femur images for the analysis of trabecular bone parameters

Janet Blumenfeld, Colin Studholme, Julio Carballido-Gamio, Thomas Link, Sharmila Majumdar

Proceedings Volume 6914, 69140G (2008) https://doi.org/10.1117/12.770460

KEYWORDS: Bone, Image registration, Magnetic resonance imaging, Image resolution, 3D image processing, Image analysis, Image segmentation, Medical imaging, Image processing, In vivo imaging

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This study investigated the feasibility of automatic image registration of MR high-spatial resolution proximal femur trabecular bone images as well as the effects of gray-level interpolation and volume of interest (VOI) misalignment on MR-derived trabecular bone structure parameters. For six subjects, a baseline scan and a follow-up scan of the proximal femur were acquired on the same day. An automatic image registration technique, based on mutual information, utilized a baseline and a follow-up scan to compute transform parameters that aligned the two images. These parameters were subsequently used to transform the follow-up image with three different gray-level interpolators. Nearest neighbor interpolation and b-spline approximation did not significantly alter bone parameters, while linear interpolation significantly modified bone parameters (p<0.01). Improvement in image alignment due to the automatic registration was determined by visually inspecting difference images and 3D renderings. This work demonstrates the first application of automatic registration, without prior segmentation, of high-spatial resolution trabecular bone MR images of the proximal femur. Additionally, effects due to imprecise analysis volume alignment are investigated. Inherent heterogeneity in trabecular bone structure and imprecise positioning of the VOI along the slice (A/P) direction resulted in significant changes in bone parameters (p<0.01). Results suggest that automatic mutual information registration using nearest-neighbor gray-level interpolation to transform the final image ensures VOI alignment between baseline and follow-up images and does not compromise the integrity of MR-derived trabecular bone parameters.

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