Quantifying Small Changes in Brain Ventricular Volume Using Non-rigid Registration

Non-rigid registration can automatically quantify small changes in volume of anatomical structures over time by means of segmentation propagation. Here we use a non-rigid registration algorithm based on optimising normalised mutual information to quantify small changes in brain ventricular volume in MR images of a group of five patients treated with growth hormone replacement therapy and a control group of six volunteers. The lateral ventricles are segmented from each subject image by registering the brainweb image [1] which has this structure delineated. The mean (standard deviation) volume change measurements are 1.09cc (0.73cc) for the patient group and 0.08cc (0.62cc) for the volunteer group, this difference is statistically significant at the 1% level. We validate our volume change measurements by comparing them to previously published results obtained by visual inspection of difference images, and demonstrate high rank correlation coefficient (p = 0.7, n=11).

[1]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[2]  Ruzena Bajcsy,et al.  Multiresolution elastic matching , 1989, Comput. Vis. Graph. Image Process..

[3]  D L Hill,et al.  Automated three-dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measures. , 1997, Medical physics.

[4]  Benoit M. Dawant,et al.  Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations. I. Methodology and validation on normal subjects , 1999, IEEE Transactions on Medical Imaging.

[5]  N Roberts,et al.  Automatic measurement of changes in brain volume on consecutive 3D MR images by segmentation propagation. , 2000, Magnetic resonance imaging.

[6]  E. Denton,et al.  The identification of cerebral volume changes in treated growth hormone-deficient adults using serial 3D MR image processing. , 2000, Journal of computer assisted tomography.

[7]  D. Louis Collins,et al.  Design and construction of a realistic digital brain phantom , 1998, IEEE Transactions on Medical Imaging.

[8]  M. Greenwood An Introduction to Medical Statistics , 1932, Nature.

[9]  Jean-Philippe Thirion,et al.  Image matching as a diffusion process: an analogy with Maxwell's demons , 1998, Medical Image Anal..

[10]  Michael I. Miller,et al.  Deformable templates using large deformation kinematics , 1996, IEEE Trans. Image Process..

[11]  David J. Hawkes,et al.  Validation of Non-rigid Registration Using Finite Element Methods , 2001, IPMI.