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Mapping brainstem internal structures on MRI scans
ABSTRACTS
M a p p i n g Brainstem Internal Structures on M R I Scans M. Furst (1) , R.A. Levine (2) R. T e n n y (1) ,B. Zilbershatz, (1) p. Dimitri(2) B.F. Fullerton (2), and J. Sundsten (3)
(1) Tel Aviv University, Tel Aviv, israel; (2) Mass. Eye & Ear Infirmary, Boston, USA, (3) University Of Washington, Seattle, USA. An algorithm for identifying internal anatomy structures in brainstem MRI scans has been developed. The algorithm develops a transfer flmction for matching the outlines of a computer-based atlas of the human brainstem to outlines of the brainstem from MRI scans. The algorithm is applied as follows: First, from the MRI scan and anatomical atlas 3 corresponding well-defined anatomical landmarks of the midsaggital plane (e.g. the pontomedullary junction) and two corresponding midlines of the brainstem of any axial and coronal planes are found. A 3-dimensional linear transformation is then calculated relating these two sets of anatomical data. For any 2-dimensional MRI image the match for the borders is improved by revising the linear transformation by the following procedure: (1) The brainstem borders of the MR image are detected according to threshold criteria; (2) The linear transformation is used as pseudo-adaptive filter for detecting the brainstem borders; (3) The image is then divided into quadrants, and for each quadrant a new linear transformation is calculated based on a distance criteria from the detected borders; (4) Finally a smoothed weighted sum of the 4 different linear transformations is obtained. Since the resulted mapping of the borders is non linear, a special algorithm to map internal structures is required. We have used Bookstein approach (1), which assumes that two homologous structures can best map one on the other if the roughness of the mapped structure relative to the original one is minimal. A nonlinear mapping of the internal structures is assumed, whose coefficients are determined by solving 2xn+6 linear equations, when n is the number of pixels in the brainstem outline which were already mapped. Six equations are obtained from the constraints that guarantees minimum roughness in the transformed brainstem. Two independent procedures have been used to evaluate this algorithm. (A) The auditory pathway was mapped on MRI scans of patients, some of whom had pontine lesions. Lesions were considered as overlapping the auditory system only if lesions in at least 2 perpendicular planes overlapped the same auditory structure. Good correlations have been found between lesion location and auditory behavioral results. (B) An MR sequence was developed for direct visualization of the medial lemniscus (ML). The algorithm was applied to locate the medial lemniscus, and its predictions were compared to manual indication of ML. Our results show an excellent correspondence between the predicted and actual locations of ML. These two sets of results provide support for the validity of this algorithm for locating any internal brainstem anatomy from outlines of the brainstem. Even though different planes were transformed by different transformations, the internal structures appear to be mapped correctly. 1. Bookstein, F.L., Lecture Notes in Biomathematics 24, 1978.
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M a p p i n g Brainstem Internal Structures on M R I Scans M. Furst (1) , R.A. Levine (2) R. T e n n y (1) ,B. Zilbershatz, (1) p. Dimitri(2) B.F. Fullerton (2), and J. Sundsten (3)
(1) Tel Aviv University, Tel Aviv, israel; (2) Mass. Eye & Ear Infirmary, Boston, USA, (3) University Of Washington, Seattle, USA. An algorithm for identifying internal anatomy structures in brainstem MRI scans has been developed. The algorithm develops a transfer flmction for matching the outlines of a computer-based atlas of the human brainstem to outlines of the brainstem from MRI scans. The algorithm is applied as follows: First, from the MRI scan and anatomical atlas 3 corresponding well-defined anatomical landmarks of the midsaggital plane (e.g. the pontomedullary junction) and two corresponding midlines of the brainstem of any axial and coronal planes are found. A 3-dimensional linear transformation is then calculated relating these two sets of anatomical data. For any 2-dimensional MRI image the match for the borders is improved by revising the linear transformation by the following procedure: (1) The brainstem borders of the MR image are detected according to threshold criteria; (2) The linear transformation is used as pseudo-adaptive filter for detecting the brainstem borders; (3) The image is then divided into quadrants, and for each quadrant a new linear transformation is calculated based on a distance criteria from the detected borders; (4) Finally a smoothed weighted sum of the 4 different linear transformations is obtained. Since the resulted mapping of the borders is non linear, a special algorithm to map internal structures is required. We have used Bookstein approach (1), which assumes that two homologous structures can best map one on the other if the roughness of the mapped structure relative to the original one is minimal. A nonlinear mapping of the internal structures is assumed, whose coefficients are determined by solving 2xn+6 linear equations, when n is the number of pixels in the brainstem outline which were already mapped. Six equations are obtained from the constraints that guarantees minimum roughness in the transformed brainstem. Two independent procedures have been used to evaluate this algorithm. (A) The auditory pathway was mapped on MRI scans of patients, some of whom had pontine lesions. Lesions were considered as overlapping the auditory system only if lesions in at least 2 perpendicular planes overlapped the same auditory structure. Good correlations have been found between lesion location and auditory behavioral results. (B) An MR sequence was developed for direct visualization of the medial lemniscus (ML). The algorithm was applied to locate the medial lemniscus, and its predictions were compared to manual indication of ML. Our results show an excellent correspondence between the predicted and actual locations of ML. These two sets of results provide support for the validity of this algorithm for locating any internal brainstem anatomy from outlines of the brainstem. Even though different planes were transformed by different transformations, the internal structures appear to be mapped correctly. 1. Bookstein, F.L., Lecture Notes in Biomathematics 24, 1978.
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