in Multi-Center Projects
The National Alliance for Medical Image Computing (NA-MIC) is a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who develop computational tools for the analysis and visualization of medical image data. The purpose of the Center is to provide the infrastructure and environment for the development of computational algorithms and open-source technologies, and then oversee the training and dissemination of these tools to the medical research community.
The Algorithms Core (NA-MIC Core 1) investigates mathematical methods for medical image analysis. Algorithm design is an essential step in attempting to answer clinical questions based on medical imagery. The Algorithms Core collaborates with Core 2 to provide tools which Core 3 can leverage to enhance clinical studies. Our team members represent a broad cross-section of medical image groups. Specific driving clinical biological projects include TBI (traumatic brain injury), AFIB (atrial fibrillation), Huntington Disease (HD), and adaptive radiotherapy in head and neck cancer.
Main page: http://www.na-mic.org/
Main wiki page: http://www.na-mic.org/Wiki/index.php/Main_Page
Specific Projects guided by Guido Gerig: http://www.na-mic.org/Wiki/index.php/Algorithm:Utah2
This project is part of the Carolina Institute for Developmental Disabilities (www.cidd.unc.edu )
The Autism Centers of Excellence (ACE) Network is a collaborative effort by investigators at four clinical sites: University of North Carolina (UNC), University of Washington (UW), Washington University (WU), and Yale University; one data coordinating center (DCC) at the Montreal Neurological Institute (MNI), and image processing centers at the University of North Carolina (UNC) and the University of Utah, to conduct a longitudinal MRI/DTI and behavioral study of infants at high risk for autism (i.e., siblings of autistic individuals) at 6, 12 and 24 months (m) of age, with a total of 650 longitudinal infant MRI/DTI.
Our image analysis teams are developing and applying image quality control, image processing and analysis tools specifically designed for longitudinal image data (3 Tesla MRI/DTI) in the range birth to 2 years.
The Silvio O. Conte Center “Prospective Studies of the Pathogenesis of Schizophrenia,” will answer three key questions in an effort to synthesize neurodevelopmental mechanisms, genetic vulnerability, and the development of schizophrenia: 1) At what stage of development does cortical pathology arise in children at risk for schizophrenia? 2) How does cortical pathology contribute to the developmental expression of cognitive deficits and clinical symptoms of schizophrenia? and 3) Can an apparently diverse set of developmental mechanisms and risk genes give rise to a common cortical pathology implicated in schizophrenia? The clinical projects of the UNC Conte Center will use state-of-the-art multimodal imaging and image analysis to study the development of cortical structure and function in children at genetic high risk for schizophrenia during the two critical periods of cortical synaptic development: synaptic elaboration during early childhood and synaptic remodeling and elimination during adolescence.
Guido Gerig is acting as the director of the Imaging Core, jointly with Martin Styner at UNC Chapel Hill.
Besides supervision of the whole Imaging Core as PI, Guido Gerig’s research team at Utah will in particular focus on image analysis of infantl MRI/DTI image data, on processing of adult MRI/DTI, and on preparing measurements for statistical analysis by the UNC biostatistics team.
The ultimate goal of this project is to develop dedicated imaging hardware and software for imaging very young normal children without sedation that will allow a detailed characterization of normal brain development. Due to limited subject cooperation, investigations of brain structure in normal children and in children with, or at high risk for, neurodevelopmental disorders will require developing new MR methodologies. Recent advances in parallel imaging with multichannel coils offer an ideal solution for imaging very young children without sedation. The pediatric brain offers unique advantages for the design of both imaging sequences and coils. For example, the smaller head size allows the use of a much smaller surface coil than typically used for adult imaging, maximizing signal-to-noise ratio (SNR) without concern for the reduced coil sensitivity for deep brain structures. In conjunction with unique surface coils, parallel imaging acquisition techniques can reduce scan time significantly. Finally, new image analysis tools specifically designed for pediatric brains can further augment our ability to quantitatively measure normal brain development.
Guido Gerig’s research team is involved in the development of improved methodologies for image bias correction and infant brain segmentation.
Guido Gerig’s team is directing the Neuroimaging Core, jointly with Martin Styner at UNC Chapel Hill. The primary objective of
the Neuroimaging Core is to serve the clinical
projects utilizing image acquisition and processing technology (Project 1,
Project 2, Project 3) for MRI imaging and for quantitative measurements of
structural MRI (sMRI) and MR Diffusion Tensor
Imaging (DTI) and to prepare the quantitative results for analysis by the
Biostatistics Core. The core will provide state-of-the-art high-field scanner
MRI technology including optimized pulse sequences for imaging of neonates
(3T Siemens Allegra head-only), adults (3T Siemens Trio) and animals (Bruker 9.4T high-field system). The core will provide
well established and validated image analysis methods and also introduce novel
methods dedicated to the needs of this project.
Spatio-Temporal Image Analysis (STIA):
The special nature of longitudinal or repeated, time-series data of individual subjects, with the inherent correlation of structure and function across the sequence of images, results in the development of a variety of new image processing and analysis approaches tackling the challenging issues of registration, segmentation and analysis in the presence of geometric and contrast changes over time. New methodologies are rapidly evolving, driven by challenging driving applications.
of Longitudinal 3D Image Data
Research by Marcel Prastawa, Suyash Awate, Avantika Vardhan, Sylvain Gouttard, Guido Gerig
4D Shape Regression and Analysis
Research by James Fishbaugh, Stanley Durrleman, Guido Gerig
4D Analysis of
White Matter Diffusion (MRI-DT Data)
Research by Sylvain Gouttard, Marcel Prastawa, Anuja Sharma, Guido Gerig
· 4D Modeling of Image Appearance Changes
Research by Neda Sadhegi, Marcel Prastawa, Guido Gerig
Modeling and Statistical Analysis of White Matter Fiber Tracts
Research by Sylvain
Gouttard, Casey Goodlett,
Anuja Sharma, Guido Gerig
Image Bias Correction via Parallel Coil Sensitivity Analysis
Research by Xiaoyue Hang, Marcel Prastawa, Guido Gerig
Image Segmentation in the Presence of Pathology
Modeling of Tumor and Edema Growth: Synthetic Simulation System Segmentation Validation Marcel Prastawa, Elizabeth Bullitt and Guido Gerig
modeling and segmentation
Marcel Prastawa, Guido Gerig
Injury: Segmentation of pathology and of change across time
Research by Bo Wang, Marcel Prastawa, Guido Gerig (Utah), Jack van Horn, Paul Vespa, David Hovda, Arthur Toga (UCLA)