ICP based neonatal brain MRI normalization method

Abstract

Magnetic resonance (MR) images are widely used to diagnose cerebral diseases. The diseases may deform the brain shape, and the deformed region differs among types of diseases. To evaluate the brain shape deformation, MR image registration (IR) has been used. There are some IR methods for brain MR images but they mainly use MR signal based likelihood. We cannot directly apply methods for adult brain to neonatal brain because there are large differences in MR signal distribution and brain shape. This paper focuses on neonatal brain MR images, and introduces a sulcus extraction method using Hessian matrix based on a feature called sulcal-distribution index (SDI). SDI is calculated from MR signal on the cerebral surface. Next, this paper proposes an iterative closest point (ICP) based brain shape registration method using the extracted sulci. The proposed method will be effective for neonatal brain in which the accurate delineation of cerebral surface is difficult because the method evaluates the correspondence of cerebral sulci distribution. Results in seven neonates (modified age was between 3 weeks and 2 years) showed that the method registered one brain with the other brain successfully.