Computer-aided tracking of MS lesions

Multiple Sclerosis (MS) lesions are known to change over time. The location, size and shape characteristics of lesions are often used to diagnose and to track disease progression. We have improved our lesion-browsing tool that allows users to automatically locate successive significant lesions in a MRI stack. In addition, an automatic alignment feature was implemented to facilitate comparisons across stacks. A lesion stack is formed that can be browsed independently or in tandem with the image windows. Lesions of interest can then be measured, rendered and rotated. Multiple windows allow the viewer to compare the size and shape of lesions from the MRI images of the same patient taken at different time intervals.

[1]  R Kikinis,et al.  Automatic identification of gray matter structures from MRI to improve the segmentation of white matter lesions. , 1995, Journal of image guided surgery.

[2]  M. Stella Atkins,et al.  Visualization of time-varying MRI data for MS lesion analysis , 2001, SPIE Medical Imaging.

[3]  Jagath C. Rajapakse,et al.  Statistical approach to segmentation of single-channel cerebral MR images , 1997, IEEE Transactions on Medical Imaging.

[4]  R. Kikinis,et al.  Quantitative follow‐up of patients with multiple sclerosis using MRI: Technical aspects , 1999, Journal of magnetic resonance imaging : JMRI.

[5]  A J Thompson,et al.  Survey of the distribution of lesion size in multiple sclerosis: implication for the measurement of total lesion load , 1997, Journal of neurology, neurosurgery, and psychiatry.

[6]  H Azhari,et al.  Automated detection and characterization of multiple sclerosis lesions in brain MR images. , 1998, Magnetic resonance imaging.

[7]  H. Azhari,et al.  Three‐dimensional analysis of the geometry of individual multiple sclerosis lesions: Detection of shape changes over time using spherical harmonics , 2003, Journal of magnetic resonance imaging : JMRI.

[8]  Zhengrong Liang,et al.  Quantitative analysis of multiple sclerosis: a feasibility study , 2006, SPIE Medical Imaging.

[9]  R. Kikinis,et al.  The evolution of multiple sclerosis lesions on serial MR. , 1995, AJNR. American journal of neuroradiology.

[10]  W. Raub From the National Institutes of Health. , 1990, JAMA.

[11]  Zhengrong Liang,et al.  Parameter estimation and tissue segmentation from multispectral MR images , 1994, IEEE Trans. Medical Imaging.