Computation of the mid-sagittal plane in 3-D brain images

• Published in: IEEE transactions on medical imaging Vol. 21; no. 2; pp. 122 - 138
• Main Authors: Prima, S., Ourselin, S., Ayache, N.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-02-2002; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anatomical structure; Anisotropy; Area measurement; Biological and medical sciences; Brain; Brain - anatomy & histology; Brain - diagnostic imaging; Computer Simulation; Convergence; Databases, Factual; Grid computing; Humans; Image Processing, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Iterative methods; Magnetic heads; Magnetic noise; Magnetic resonance; Magnetic Resonance Imaging - methods; Medical sciences; Models, Neurological; Models, Statistical; Radiography; Reproducibility of Results; Robustness; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Stochastic Processes; Studies; Symmetry; Tomography
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/42.993131

We present a new method to automatically compute, reorient, and recenter the mid-sagittal plane in anatomical and functional three-dimensional (3-D) brain images. This iterative approach is composed of two steps. At first, given an initial guess of the mid-sagittal plane (generally, the central plane of the image grid), the computation of local similarity measures between the two sides of the head allows to identify homologous anatomical structures or functional areas, by way of a block matching procedure. The output is a set of point-to-point correspondences: the centers of homologous blocks. Subsequently, we define the mid-sagittal plane as the one best superposing the points on one side and their counterparts on the other side by reflective symmetry. Practically, the computation of the parameters characterizing the plane is performed by a least trimmed squares estimation. Then, the estimated plane is aligned with the center of the image grid, and the whole process is iterated until convergence. The robust estimation technique we use allows normal or abnormal asymmetrical structures or areas to be treated as outliers, and the plane to be mainly computed from the underlying gross symmetry of the brain. The algorithm is fast and accurate, even for strongly tilted heads, and even in presence of high acquisition noise and bias field, as shown on a large set of synthetic data. The algorithm has also been visually evaluated on a large set of real magnetic resonance (MR) images. We present a few results on isotropic as well as anisotropic anatomical (MR and computed tomography) and functional (single photon emission computed tomography and positron emission tomography) real images, for normal and pathological subjects.