PURPOSE: To study the use of image registration in the analysis of multiple sclerosis (MS) lesion volume and compare this with repositioning error and observer-based variability. MATERIALS AND METHODS: The normalized mutual information (NMI) algorithm is evaluated in an accuracy study using a phantom, followed by a validation study on magnetic resonance (MR) data of MS patients. Further, using scan-rescan MR data, the effect of registration on MS lesion volume compared with repositioning error and observer-based variability is assessed. RESULTS: The registration accuracy was near perfect in the phantom study, while the in vivo validation study demonstrated an accuracy on the order of 0.2-0.3 mm. In the scan-rescan study, quantification accounted for 15.6% of the relative variance, repositioning for 44.4%, and registration for 40.0%. CONCLUSION: NMI resulted in robust and accurate alignment of MR brain images of MS patients. Its use in the detection of changes in MS using large serial MR imaging (MRI) data warrants future evaluation. Copyright 2002 Wiley-Liss, Inc.
PURPOSE: To study the use of image registration in the analysis of multiple sclerosis (MS) lesion volume and compare this with repositioning error and observer-based variability. MATERIALS AND METHODS: The normalized mutual information (NMI) algorithm is evaluated in an accuracy study using a phantom, followed by a validation study on magnetic resonance (MR) data of MSpatients. Further, using scan-rescan MR data, the effect of registration on MS lesion volume compared with repositioning error and observer-based variability is assessed. RESULTS: The registration accuracy was near perfect in the phantom study, while the in vivo validation study demonstrated an accuracy on the order of 0.2-0.3 mm. In the scan-rescan study, quantification accounted for 15.6% of the relative variance, repositioning for 44.4%, and registration for 40.0%. CONCLUSION: NMI resulted in robust and accurate alignment of MR brain images of MSpatients. Its use in the detection of changes in MS using large serial MR imaging (MRI) data warrants future evaluation. Copyright 2002 Wiley-Liss, Inc.