CORE C: The staff of the Biostatistics Core will be responsible for providing statistical support to the research of this program. The Biostatistics Core is under the supervision of Dr. Timothy D. Johnson of the Biostatistics Department at the University of Michigan, School of Public Health. The core provides assistance in the design, analysis and interpretation of preclinical and clinical experiments of the program project. Core personnel will interact with project investigators to ensure that appropriate designs and methods of analysis are used. Design issues involve dose selection, randomization, time of measurements and sample size. For analysis of data, the core will ensure that efficient methods are used. Standard graphical, group comparison and correlation methods of analysis will be used for initial investigation of the experimental data. Mixed models methods will be used for efficient use of data in experiments involving repeated measures. Core personnel are experienced in the design and analysis of both animal and clinical data. This will ensure that all data obtained from imaging measurements, tumor histology, net cell kill associated with therapy and patient outcome will be collected efficiently and analyzed appropriately.

Public Health Relevance

Overall, this research effort will provide the rationale for the use of state-of-the-art imaging registration techniques and quantitative imaging techniques for the management of clinical cancer patients.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA087634-10
Application #
8445397
Study Section
Special Emphasis Panel (ZCA1-GRB-P)
Project Start
Project End
2015-02-28
Budget Start
2013-03-01
Budget End
2014-02-28
Support Year
10
Fiscal Year
2013
Total Cost
$132,056
Indirect Cost
$64,964
Name
University of Michigan Ann Arbor
Department
Type
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Muckley, Matthew J; Noll, Douglas C; Fessler, Jeffrey A (2015) Fast parallel MR image reconstruction via B1-based, adaptive restart, iterative soft thresholding algorithms (BARISTA). IEEE Trans Med Imaging 34:578-88
Weller, Daniel S; Ramani, Sathish; Fessler, Jeffrey A (2014) Augmented Lagrangian with variable splitting for faster non-Cartesian L1-SPIRiT MR image reconstruction. IEEE Trans Med Imaging 33:351-61
Zhao, Feng; Fessler, Jeffrey A; Wright, Steven M et al. (2014) Regularized estimation of magnitude and phase of multi-coil b1 field via Bloch-Siegert B1 mapping and coil combination optimizations. IEEE Trans Med Imaging 33:2020-30
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Watanabe, Takanori; Kessler, Daniel; Scott, Clayton et al. (2014) Disease prediction based on functional connectomes using a scalable and spatially-informed support vector machine. Neuroimage 96:183-202
Allison, Michael J; Ramani, Sathish; Fessler, Jeffrey A (2013) Accelerated regularized estimation of MR coil sensitivities using augmented Lagrangian methods. IEEE Trans Med Imaging 32:556-64
Ramani, Sathish; Weller, Daniel S; Nielsen, Jon-Fredrik et al. (2013) Non-cartesian MRI reconstruction with automatic regularization Via Monte-Carlo SURE. IEEE Trans Med Imaging 32:1411-22
Sripada, Chandra Sekhar; Kessler, Daniel; Welsh, Robert et al. (2013) Distributed effects of methylphenidate on the network structure of the resting brain: a connectomic pattern classification analysis. Neuroimage 81:213-21
Chun, Se Young; Fessler, Jeffrey A (2013) Noise properties of motion-compensated tomographic image reconstruction methods. IEEE Trans Med Imaging 32:141-52
Matakos, Antonios; Ramani, Sathish; Fessler, Jeffrey A (2013) Accelerated edge-preserving image restoration without boundary artifacts. IEEE Trans Image Process 22:2019-29

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