Summary
Objectives:
Introduction of a new atlas-based method for analyzing functional data which takes into account the variability of individual human brains and the partial volume effects of functional emission computed tomography images in complex anatomical 3D regions, as well as describing the underlying multi-modal image processing principles.
Methods:
3D atlas extraction is done directly by automated segmentation of individual magnetic resonance images of the patient’s head. This is done in two steps: voxel-based classification of T1-weighted images for tissue differentiation (low-level processing) is followed by knowledge-based analysis of the classified images for extraction of 3D anatomical regions (high-level processing). For atlas-based quantification of co-registered functional images, 3D anatomical regions can be convoluted with an idealized point spread function of the emission computed tomography system, after which a partial volume-dependent threshold can be determined.
Results:
Quantitative evaluation studies, based on 50 realistic software head phantoms and 24 image data sets obtained from healthy subjects and patients, show low misclassification rates and stable results for the neural network-based classification approach (mean ± SD 3.587 ± 0.466%, range 2.726-4.927%) as well as for the adjustable parameters of the knowledge-based approach. Computation time is <5 min for classification, <1 min for most of the extraction algorithms. The influence of the partial volume-dependent threshold is shown for an activation study.
Conclusions:
This new method allows 3D atlas generation without the need to warp individual image data to an anatomical or statistical brain atlas. Going beyond the purely tissue-oriented approach, partial volume effects of emission computed tomography images can be analyzed in complex anatomical 3D regions.
Keywords
Computer-assisted image processing - neural networks (computer) - knowledge - brain - tomography
