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.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Program Projects (P01)
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Special Emphasis Panel (ZCA1-GRB-P)
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University of Michigan Ann Arbor
Ann Arbor
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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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