Purpose: Magnetic resonance imaging (MRI) is often used to detect and treat neonatal cerebral disorders. However, neonatal MR image interpretation is limited by intra- and inter-observer variability. To reduce such variability, a template-based computer-aided diagnosis system is being developed, and several methods for creating templates were evaluated.
Method: Spatial normalization for each individual's MR images is used to accommodate the individual variation in brain shape. Because the conventional normalization uses as adult brain template, it can be difficult to analyze the neonatal brain, as there are large difference between the adult brain and the neonatal brain. This article investigates three approaches for defining a neonatal template for 1-week-old newborns for diagnosing neonatal cerebral disorders. The first approach uses an individual neonatal head as the template. The second approach applies skull stripping to the first approach, and the third approach produces a template by averaging brain MR images of 7 neonates. To validate the approaches, the normalization accuracy was evaluated using mutual information and anatomical landmarks.
Results: The experimental results of 7 neonates (revised age 5.6 ± 17.6 days) showed that normalization accuracy was significantly higher with the third approach than with the conventional adult template and the other two approaches (P < 0.01).
Conclusion: Three approaches to neonatal brain template matching for spinal normalization of MRI scans were applied, demonstrating that a population average gave the best results.