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 #
5P01CA087634-08
Application #
8234850
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
$85,947
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 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
Nyati, Shyam; Young, Grant; Ross, Brian Dale et al. (2017) Quantitative and Dynamic Imaging of ATM Kinase Activity. Methods Mol Biol 1596:131-145
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
Piert, Morand; Montgomery, Jeffrey; Kunju, Lakshmi Priya et al. (2016) 18F-Choline PET/MRI: The Additional Value of PET for MRI-Guided Transrectal Prostate Biopsies. J Nucl Med 57:1065-70
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

Showing the most recent 10 out of 92 publications