Automatic Quantification of Changes in Bone in Serial MR Images of Joints

Abstract

Recent innovations in drug therapies have made it highly desirable to obtain sensitive biomarkers of disease progression that can be used to quantify the performance of candidate disease modifying drugs. In order to measure potential image-based biomarkers of disease progression in an experimental model of rheumatoid arthritis (RA), we present two different methods to automatically quantify changes in a bone in in-vivo serial magnetic resonance (MR) images from the model. Both methods are based on rigid and nonrigid image registration to perform the analysis. The first method uses segmentation propagation to delineate a bone from the serial MR images giving a global measure of temporal changes in bone volume. The second method uses rigid body registration to determine intensity change within a bone, and then maps these into a reference coordinate system using nonrigid registration. This gives a local measure of temporal changes in bone lesion volume. We detected significant temporal changes in local bone lesion volume in five out of eight identified candidate bone lesion regions, and significant difference in local bone lesion volume between male and female subjects in three out of eight candidate bone lesion regions. But the global bone volume was found to be fluctuating over time. Finally, we compare our findings with histology of the subjects and the manual segmentation of bone lesions.

Spatio-Temporal Segmentation of Rheumatoid Arthritis Lesions in Serial MR Images of Joints

“…Previous results showed that bone lesions were localised in space, time, and intensity of the difference images because (a) spatially and temporally localised candidate bone lesion regions were found; (b) intensity thresholding technique worked reasonably well to delineate the bone lesions [18]. This motivated us to try to delineate the bone lesions directly from this space-time-intensity space.…”

“…As in our previous work [18], we segmented the highintensity bone lesions from the difference images. For each subject, 5 difference images were generated from the 6 time points.…”

“…We used ST to refer to the results and the method described in this paper, and OT to refer to a previous results and method in [18]. In general, ST performed better than OT.…”

“…This pair-wise analysis of the images does not make full use of the time-domain information available. It has previously been shown that bone lesion changes are localised in space and time [18]. We therefore propose a more integrated spatio-temporal analysis technique in which we build a 5D feature space by mapping the (x, y, z, intensity, time) value of each voxel to a point in the feature space.…”

“…This allows the comparison of localised bone lesion volume changes in anatomically corresponding locations between different subjects or groups. Using simulated and real MR images, we compare this new approach with a previous method [18] that applies Otsu's thresholding [23] and mathematical morphology to delineate bone lesions in the difference image generated from registered baseline and follow-up images.…”