ABSTRACT
PURPOSE: To develop an automated quantification program, which is called FALBA (Functional & Anatomical Labeling of Brain Activation), and to provide information on the brain centers, brain activity (%) and hemispheric lateralization index on the basis of a brain activation map obtained from functional MR imaging. MATERIALS AND METHODS: The 3-dimensional activation MR images were processed by a statistical parametric mapping program (SPM99, The Wellcome Department of Cognitive Neurology, University College London, UK) and MRIcro software (www.mricro.com). The 3-dimensional images were first converted into 2-dimensional sectional images, and then overlapped with the corresponding T1-weighted images. Then, the image dataset was extended to -59 mm to 83 mm with a 2 mm slice-gap, giving 73 axial images. By using a pixel subtraction method, the differences in the R, G, B values between the T1-weighted images and the activation images were extracted, in order to produce black & white (B/W) differentiation images, in which each pixel is represented by 24-bit R, G, B true colors. Subsequently, another pixel differentiation method was applied to two template images, namely one functional and one anatomical index image, in order to generate functional and anatomical differentiation images containing regional brain activation information based on the Brodmann's and anatomical areas, respectively. In addition, the regional brain lateralization indices were automatically determined, in order to evaluate the hemispheric predominance, with the positive (+) and negative (-) indices showing left and right predominance, respectively. RESULTS: The manual counting method currently used is time consuming and has limited accuracy and reliability in the case of the activated cerebrocortical regions. The FALBA program we developed was 240 times faster than the manual counting method: -10 hours for manual accounting and -2.5 minutes for the FALBA program using a Pentium IV processor. Compared with the FALBA program, the manual quantification method showed an average error of 0.334+/-0.007 (%). Thus, the manual counting method gave less accurate quantitative information on brain activation than the FALBA program. CONCLUSION: The FALBA program is capable of providing accurate quantitative results, including the identification of the brain activation region and lateralization index with respect to the functional and anatomical areas. Also, the processing time was dramatically shortened in comparison with the manual counting method.