Over the past ten years, the PI has developed a flexible set of software tools for accurate computational morphometry, named the Computational Morphometry Toolkit (CMTK), which has been used in numerous studies, ranging from insect brain connection mapping to investigations of effects of alcoholism and aging on the human brain. CMTK's registration tools have also been successfully applied to extra-cranial problems, such as patient motion correction in contrast-enhanced MR breast imaging and CT subtraction angiography. The proposed project will make CMTK publicly available, improve its usability, and ensure its future support and development. The existing software consists of three parts: (1) a library with application-proven implementations of a large number of image processing and analysis algorithms;(2) command line tools for image restoration, registration, segmentation, and statistical modeling;(3) scripts for complex combinations of the elementary registration and segmentation tasks, such as longitudinal and cross-sectional morphometric studies. CMTK has a unique set of features, as it combines robust tools for image registration, segmentation, filtering, and modeling. These tools can be applied to data from different imaging modalities, and are useful for analysis of large, under-analyzed archival data sets. The software is end-user focused and computationally efficient, enabled by state-of-the-art parallel computation techniques. We propose three specific aims: (1) Access. Legacy third-party code in the existing software that prevents open source distribution will be replaced with freely available alternatives. (2) Usability. We will improve and document the pre-defined processing pipelines for complex studies (e.g., longitudinal deformation-based morphometry with a general linear model) and provide a user manual. (3) Maintenance. We will review and complete source code documentation, replace the current configuration and build environment, and set up auto- mated testing and bug tracking with state-of-the-art systems that are publicly available. We will improve interoperability with selected related soft- ware development efforts, which provide complementary functions to CMTK. (4) Enhancement. We will port key algorithms to state-of-the-art high-performance computing hardware. We will also implement an image-relationship database for transformation bookkeeping. By making publicly available a proven software system for morphometric studies, this project will enable any institution with appropriate image data to perform data analysis with efficiency, reliability, and reproducibility. Once released as the result of this project, CMTK will continue to enable new scientific insights in many fields of neuroscience, from human applications to insect science. The state-of-the-art registration tools in CMTK will furthermore continue to support clinically relevant applications such as treatment target delineation and patient motion correction.

Public Health Relevance

Computerized morphometric analysis based on three-dimensional imaging enables high-throughput studies of disease (e.g., alcohol abuse, HIV/AIDS, stroke, dementia, and cancer), normal development, and aging effects on human and animal brain and body structure. Structural differences between diseased and control groups can be used to help identify potential biological causes of such conditions as Alzheimer's disease or schizophrenia. This project will enhance a proven soft- ware system for imaging-based morphometric studies and make it freely avail- able to the scientific community.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB008381-02
Application #
7803684
Study Section
Special Emphasis Panel (ZRG1-BST-Q (01))
Program Officer
Pai, Vinay Manjunath
Project Start
2009-04-10
Project End
2011-09-30
Budget Start
2010-04-01
Budget End
2011-09-30
Support Year
2
Fiscal Year
2010
Total Cost
$121,548
Indirect Cost
Name
Sri International
Department
Type
DUNS #
009232752
City
Menlo Park
State
CA
Country
United States
Zip Code
94025
Zahr, Natalie M; Mayer, Dirk; Rohlfing, Torsten et al. (2014) Imaging neuroinflammation? A perspective from MR spectroscopy. Brain Pathol 24:654-64
Müller-Oehring, Eva M; Schulte, Tilman; Rohlfing, Torsten et al. (2013) Visual search and the aging brain: discerning the effects of age-related brain volume shrinkage on alertness, feature binding, and attentional control. Neuropsychology 27:48-59
Pfefferbaum, Adolf; Rohlfing, Torsten; Rosenbloom, Margaret J et al. (2013) Variation in longitudinal trajectories of regional brain volumes of healthy men and women (ages 10 to 85 years) measured with atlas-based parcellation of MRI. Neuroimage 65:176-93
Zahr, Natalie M; Mayer, Dirk; Rohlfing, Torsten et al. (2013) In vivo glutamate measured with magnetic resonance spectroscopy: behavioral correlates in aging. Neurobiol Aging 34:1265-76
Schulte, Tilman; Maddah, Mahnaz; Müller-Oehring, Eva M et al. (2013) Fiber tract-driven topographical mapping (FTTM) reveals microstructural relevance for interhemispheric visuomotor function in the aging brain. Neuroimage 77:195-206
Sullivan, Edith V; Muller-Oehring, Eva; Pitel, Anne-Lise et al. (2013) A selective insular perfusion deficit contributes to compromised salience network connectivity in recovering alcoholic men. Biol Psychiatry 74:547-55
Rohlfing, Torsten (2012) Image similarity and tissue overlaps as surrogates for image registration accuracy: widely used but unreliable. IEEE Trans Med Imaging 31:153-63
Pfefferbaum, Adolf; Rosenbloom, Margaret J; Sassoon, Stephanie A et al. (2012) Regional brain structural dysmorphology in human immunodeficiency virus infection: effects of acquired immune deficiency syndrome, alcoholism, and age. Biol Psychiatry 72:361-70
Bilgic, Berkin; Pfefferbaum, Adolf; Rohlfing, Torsten et al. (2012) MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping. Neuroimage 59:2625-35
Pitel, Anne-Lise; Chanraud, Sandra; Rohlfing, Torsten et al. (2012) Face-name association learning and brain structural substrates in alcoholism. Alcohol Clin Exp Res 36:1171-9

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