Detection of DTI white matter abnormalities in multiple sclerosis patients.
The emergence of new modalities such as Diffusion Tensor Imaging (DTI) is of great interest for the characterization and the temporal study of Multiple Sclerosis (MS). DTI indeed gives information on water diffusion within tissues and could therefore reveal alterations in white matter fibers before being visible in conventional MRI. However, recent studies generally rely on scalar measures derived from the tensors such as FA or MD instead of using the full tensor itself. Therefore, a certain amount of information is left unused. In this article, we present a framework to study the benefits of using the whole diffusion tensor information to detect statistically significant differences between each individual MS patient and a database of control subjects. This framework, based on the comparison of the MS patient DTI and a mean DTI atlas built from the control subjects, allows us to look for differences both in normally appearing white matter but also in and around the lesions of each patient. We present a study on a database of 11 MS patients, showing the ability of the DTI to detect not only significant differences on the lesions but also in regions around them, enabling an early detection of an extension of the MS disease.
Commowick
Olivier
O
Computational Radiology Laboratory, Department of Radiology, Children's Hospital, 300 Longwood Avenue, Boston, MA 02115, USA. Olivier.Commowick@childrens.harvard.edu
Fillard
Pierre
P
Clatz
Olivier
O
Warfield
Simon K
SK
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009103:Multiple Sclerosis / Q000473:pathology*; D009413:Nerve Fibers, Myelinated / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity