Image Analysis
SCI’s imaging work addresses fundamental questions in 2D and 3D image processing, including filtering, segmentation, surface reconstruction, and shape analysis. In low-level image processing, this effort has produce new nonparametric methods for modeling image statistics, which have resulted in better algorithms for denoising and reconstruction. Work with particle systems has led to new methods for visualizing and analyzing 3D surfaces. Our work in image processing also includes applications of advanced computing to 3D images, which has resulted in new parallel algorithms and real-time implementations on graphics processing units (GPUs). Application areas include medical image analysis, biological image processing, defense, environmental monitoring, and oil and gas.
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Extracting Anatomical Structures |
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The ability to create accurately segmented three dimensional models from imaging devices such as MRI, PET, CT, and others is crucial to the understanding of structural development.
The image shows the result of automatic extraction of anatomical structures from a patient MRI using software developed by the Utah Center for Neuroimage Analysis. Measurements of subcortical brain structures are of specific interest in studying structure-to-function relationship. Research in autism, schizophrenia and Alzheimer’s disease is particularly interested in volumes and shape of hippocampus (green), amygdala (red) and caudate (yellow).
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Diffusion Tensor MRI Population Analysis |
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Diffusion tensor magnetic resonance imaging (DT-MRI) is relatively new imaging technique which provides new insight into the structure of brain white matter by measuring the local diffusion of water in the brain. In this project associated with the national alliance for medical image computing (NAMIC) images are combined from a population into a template atlas which reflects the average properties of the population. White matter bundles are extracted from in the template atlas to serve as a coordinate system for measuring diffusion properties and how they differ between populations. In a study of neurodevelopment in association with the CONTE center at University of North Carolina - Chapel Hill, an atlas was developed based on subjects at one and two years of age. Statistical comparison of the diffusion properties between one and two year olds indicates significant changes that may reflect underlying changes in myelination and axon development. The above image shows differences in the fractional anisotropy (FA), a measure of diffusion tensors which is thought to reflect axon development, from one to two years. The red regions indicate the largest increase of the FA value. |
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Infant MRI Head Coil Design |
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Improved MRI methodology for infant imaging: We study head/brain growth and create statistical models of neonates, 6mo, 1yr, 2yr and 4yr. Based on these models, the MGH group creates new parallel coils for the scanner. We then get these parallel images and combine them back with new signal processing. |
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