Over the past 10 years we have developed and distributed a morphometry package for automatically characterizing and quantifying neuroanatomical structures in the human brain, including the automated construction of models of the hippocampus, amygdala, ventricular system and neocortex. These tools have been designed for use in a research setting, and have deepened our understanding of a wide variety of neurological disorders and effects such as schizophrenia, Huntington's disease, Alzheimer's disease, Semantic Dementia, phobias, autism, dyslexia, aging, and development. Unfortunately, design decisions made over a decade ago now hamper the adoption of these tools into clinical settings. In this project we propose to use state-of-the art software engineering methods in order to remove these restrictions by designing and engineering these algorithms using current graphics processor unit (GPU) technology, which has been shown to routinely provide on the order of 50-fold speed increases. An additional advantage will be the careful unit testing that can be built into the new tools, ensuring they operate with the high degree of accuracy and reliability required for point-of-care clinical tools. The result will be a suite of open-source algorithms that can run on a standard workstation with a commercially available graphics card, which can rapidly provide diagnostic information for multiple conditions at the point-of-care.
Neurodegenerative disorders result in different patterns of atrophy in the human brain, with each pattern giving rise to the characteristic clinical impairments typically used for diagnosis. Unfortunately, the behavioral changes typically used for diagnosis can be ambiguous, making neuroimaging a potentially valuable diagnostic tool. The goal of this project is to fill this unmet need, by providing a set of tools for quantifying neuroanatomical properties of the human brain from routine clinical MRI scans. Using currently available low- cost graphics cards we will be able to analyze this type of data on standard workstations resulting in information that can be used in diagnosing a wide array of neurological disorders by quantifying the size and shape of structures such as the hippocampus, the amygdala, the ventricular system and the neocortex. We will provide access to statistical information regarding normal variability in these structures, resulting in a tool that can automatically, rapidly and robustly detect abnormal brain anatomy indicative of disease process at the point-of-care.
|Lindemer, Emily R; Greve, Douglas N; Fischl, Bruce et al. (2017) Differential Regional Distribution of Juxtacortical White Matter Signal Abnormalities in Aging and Alzheimer's Disease. J Alzheimers Dis 57:293-303|
|Saygin, Z M; Kliemann, D; Iglesias, J E et al. (2017) High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas. Neuroimage 155:370-382|
|Bianciardi, Marta; Strong, Christian; Toschi, Nicola et al. (2017) A probabilistic template of human mesopontine tegmental nuclei from in vivo 7T MRI. Neuroimage :|
|Aganj, Iman; Fischl, Bruce (2017) Multimodal Image Registration through Simultaneous Segmentation. IEEE Signal Process Lett 24:1661-1665|
|Polimeni, Jonathan R; Renvall, Ville; Zaretskaya, Natalia et al. (2017) Analysis strategies for high-resolution UHF-fMRI data. Neuroimage :|
|Aganj, Iman; Iglesias, Juan Eugenio; Reuter, Martin et al. (2017) Mid-space-independent deformable image registration. Neuroimage 152:158-170|
|Thaker, A A; Weinberg, B D; Dillon, W P et al. (2017) Entorhinal Cortex: Antemortem Cortical Thickness and Postmortem Neurofibrillary Tangles and Amyloid Pathology. AJNR Am J Neuroradiol 38:961-965|
|Sabuncu, Mert R; Ge, Tian; Holmes, Avram J et al. (2016) Morphometricity as a measure of the neuroanatomical signature of a trait. Proc Natl Acad Sci U S A 113:E5749-56|
|Magnain, Caroline; Wang, Hui; Sakadži?, Sava et al. (2016) En face speckle reduction in optical coherence microscopy by frequency compounding. Opt Lett 41:1925-8|
|Yendiki, Anastasia; Reuter, Martin; Wilkens, Paul et al. (2016) Joint reconstruction of white-matter pathways from longitudinal diffusion MRI data with anatomical priors. Neuroimage 127:277-286|
Showing the most recent 10 out of 60 publications