The Computing Core will provide software-, data-, and computing-related services to all participants of the Program Project. The majority of the Core's activities revolve around a number of applications that perform multi-modality, 3D, automated registration (or fusion) on medical imaging datasets. The algorithms comprising these registration applications will, in general, employ information theoretic techniques as described in the Introduction/Program Narrative. The applications themselves will typically be implemented in AVS5, a data analysis and visualization environment. The individual Projects will use registered datasets as described in the individual Project write-ups. A number of services will be offered by the Computing Core to support these new registration algorithms and to manage the large 3D image datasets on which they operates. The principle services of the Core are (1) to provide a standardized computing environment (consisting of centralized compute servers, a common computing lab, and coordinated computer code on all systems) and (2) to maintain centralized and coordinated retrieval, storage, archiving, and management of imaging data and other information. Other services include providing Project participants with training and consulting as needed for successful use of the Core's facilities and for success in carrying out the aims of the individual Projects.

Public Health Relevance

This core will provide program computing support for the successful development of state-of-the-art imaging registration and quantitative imaging techniques for the management of individual clinical cancer patient's therapy.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA087634-08
Application #
8234848
Study Section
Special Emphasis Panel (ZCA1)
Project Start
Project End
Budget Start
2011-03-01
Budget End
2012-02-29
Support Year
8
Fiscal Year
2011
Total Cost
$216,413
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Type
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Olafsson, Valur T; Noll, Douglas C; Fessler, Jeffrey A (2018) Fast Spatial Resolution Analysis of Quadratic Penalized Least-Squares Image Reconstruction With Separate Real and Imaginary Roughness Penalty: Application to fMRI. IEEE Trans Med Imaging 37:604-614
Nyati, Shyam; Young, Grant; Ross, Brian Dale et al. (2017) Quantitative and Dynamic Imaging of ATM Kinase Activity. Methods Mol Biol 1596:131-145
Nyati, Shyam; Young, Grant; Ross, Brian Dale et al. (2017) Quantitative and Dynamic Imaging of ATM Kinase Activity by Bioluminescence Imaging. Methods Mol Biol 1599:97-111
Nataraj, Gopal; Nielsen, Jon-Fredrik; Fessler, Jeffrey A (2017) Optimizing MR Scan Design for Model-Based ${T}_{1}$ , ${T}_{2}$ Estimation From Steady-State Sequences. IEEE Trans Med Imaging 36:467-477
Jintamethasawat, Rungroj; Zhang, Xiaohui; Carson, Paul L et al. (2017) Acoustic beam anomalies in automated breast imaging. J Med Imaging (Bellingham) 4:045001
Keith, Lauren; Ross, Brian D; Galbán, Craig J et al. (2016) Semiautomated Workflow for Clinically Streamlined Glioma Parametric Response Mapping. Tomography 2:267-275
Berisha, Visar; Wisler, Alan; Hero, Alfred O et al. (2016) Empirically Estimable Classification Bounds Based on a Nonparametric Divergence Measure. IEEE Trans Signal Process 64:580-591
Hero, Alfred O; Rajaratnam, Bala (2016) Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining. Proc IEEE Inst Electr Electron Eng 104:93-110
Nataraj, Gopal; Nielsen, Jon-Fredrick; Fessler, Jeffrey (2016) Optimizing MR Scan Design for Model-Based T1, T2 Estimation from Steady-State Sequences. IEEE Trans Med Imaging :
Larson, Eric D; Lee, Won-Mean; Roubidoux, Marilyn A et al. (2016) Automated Breast Ultrasound: Dual-Sided Compared with Single-Sided Imaging. Ultrasound Med Biol 42:2072-82

Showing the most recent 10 out of 92 publications