A new method for analyzing diffusion tensor imaging (DTI) of the brain, based on a recently introduced algorithm, lambda chart analysis (LCA), is presented. Pretreatment of a given DTI data set with LCA, which effectively segregates isotropic and anisotropic components, allows for total removal of the anisotropic component from the DTI data set. The remaining pure isotropic component can therefore be subjected to further analysis similar to that applied in the trace histogram method. Deconvolution of the trace function yielded 3 Gaussian elements. Remapping of these 3 deconvoluted isotropic elements back onto the 2-dimensional image plane provided anatomical correlates of each element. The algorithm, referred to here as isotropic component trace analysis, can be used as a pictorial analytic tool, as well as a numerical analytical tool, for the noninvasive assessment of isotropic parenchymal components. The presented method provides quantitative indices of certain parenchymal parameters with better clarity than currently available methods. A ready-to-use program, EZ-LCA, for this powerful method is provided (available at http://coe.bri. niigata-u.ac.jp).
- Diffusion anisotropy
- Diffusion tensor imaging
- Lambda chart analysis
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Clinical Neurology
- Radiological and Ultrasound Technology