More MS news articles for Nov 2001

Multiprotocol MR image segmentation in multiple sclerosis: experience with over 1,000 studies

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=11721811&dopt=Abstract

Acad Radiol 2001 Nov;8(11):1116-26
Udupa JK, Nyul LG, Ge Y, Grossman RI.
Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia 19104-6021, USA.

RATIONALE AND OBJECTIVES:

Multiple sclerosis (MS) is an acquired disease of the central nervous system. Several clinical measures are commonly used to express the severity of the disease, including the Expanded Disability Status Scale and the ambulation index. These measures are subjective and may be difficult to reproduce. The aim of this research is to investigate the possibility of developing more objective measures derived from MR imaging.

MATERIALS AND METHODS:

Various magnetic resonance (MR) imaging protocols are being investigated for the study of MS. Seeking to replace the Expanded Disability Status Scale and ambulation index with an objective means to assess the natural course of the disease and its response to therapy, the authors have developed multiprotocol MR image segmentation methods based on fuzzy connectedness to quantify both macrosopic features of the disease (lesions, gray matter, white matter, cerebrospinal fluid, and brain parenchyma) and the microscopic appearance of diseased white matter. Over 1,000 studies have been processed to date.

RESULTS:

By far the strongest correlations with the clinical measures were demonstrated by the magnetization transfer ratio histogram parameters obtained for the various segmented tissue regions. These findings emphasize the importance of considering the microscopic and diffuse nature of the disease in the individual tissue regions. Brain parenchymal volume also demonstrated a strong correlation with clinical measures, which suggests that brain atrophy is an important disease indicator.

CONCLUSION:

Fuzzy connectedness is a viable, highly reproducible segmentation method for studying MS.
 

PMID: 11721811 [PubMed - in process]