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Research

Active Grants

PI:Yu, Source: NSF/CBET, No: 1540898, Period: 06/01/2012-05/31/2017
Title: CAREER: Development and Application of CS-based Interior Tomography 
Goal: The goal of this CAREER proposal is to advance the CS-based interior tomography theory and algorithms, and make a paradigm shift from traditional global filtered back-projection (FBP) to contemporary interior reconstruction.

PI: Yu, Source: NSF/DMS, No: 1619550, Period: 07/15/2012-03/31/2017 
Title: Collaborative Research: Mathematical Aspects of Interior Problem of Tomography
Goal: The objective of the proposed research is to assemble a team of pure and applied mathematicians as well as specialists in computations and tomography who will develop, implement, and test image reconstruction algorithms for solving the interior problem of transmission and emission tomographies.

PI:Yu, Source: NIH/NIBIB, No: R21EB019074, Period: 09/11/2015-06/30/2017  
Title:Tensor-based dictionary for imaging biomarkers
Goal: The overall goal of this R21 project is to develop tensor-based dictionary learning method to extract features for classifications. The example application is the ultra-low-dose lung cancer screening.

Subcontract PI: Yu, Source: NIH/NIBIB, No: U01EB017140, Period: 09/17/2014-07/31/2018
Title: High dose efficiency CT system
Goal: The goal is to develop a CT system with much higher dose efficiency for general imaging applications than today's state of the art.


Recent Completed Grants

Subcontract PI: Yu, Source: NIH/NIBIB, No: R01EB011785, Period: 02/01/2010-01/31/2015
Title: Cardiac CT: Advanced Architectures and Algorithms
Goal: The overall goal of this project is to develop novel cardiac CT architectures and the associated reconstruction algorithms, and define the next-generation cardiac CT system.

PI: Yu, Source: WFUHS Pilot Grant, Period: 07/01/2014-06/30/2015
Title: Pilot study for learning based imaging bioinformatics
Goal: The goal of this project is to demonstrate the feasibility of dictionary learning based method for imaging bioinformatics.

Co-PI: Yu, Source: NSF/CBET, No. 1348097, Period: 04/01/2014-03/31/2015
Title: Support for US Young Investigators to attend IEEE ISBI, Beijing, China, April 28-May 2, 2014
Goal: The goal of project is support US young investigators to attend the IEEE ISBI’14 conference in Beijing, China, April 28-May 2, 2014.

Subcontract PI: Yu, Source: NSF/MRI, No: 0923297, Period: 08/15/2009-08/14/2013 
Title: Development of the next-generation nano-CT system for ROI-focused scanning and exact interior reconstruction
Goal: The goal is to develop the next-generation x-ray nano-CT system, which will permit faithful reconstruction of deeply embedded local details in 50nm resolution with a 10-fold lower radiation dose within a sample that is up to 10 times larger than what is currently possible

PI: Yu, Source: WFIRM  Seed Grant, Period: 03/01/2011-12/31/2012
Title: True-color micro-CT characterization of liver vasculature for regenerative medicine
Goal: The goal of this project is to characterize the vascular network of the acellular liver scaffold using the cutting-edge true-color scanner, interior reconstruction theory and modern statistical methods.

PI: Yu, Source: TSI Pilot Grant, Period: 09/01/2011-12/31/2011
Title: Pilot study for compressive sensing based low-dose CT
Goal: The goal of this project is to generate some supportive results of dictionary learning based method for ultra-low-dose lung cancer screening.

PI : Yu, Source: NIH/NIBIB, No: R03EB007288, Period: 09/01/2007-08/31/2010
Title: Data consistency based motion artifact reduction for head CT
Goal: The goal of this project is to develop effective CT methods for motion estimation based image reconstruction, improve the diagnostic performance, and enable new clinical applications.

PI: Yu, Source: NIH/NIBIB, No: R43EB009275, Period: 07/01/2009-12/31/2010
Title: Development of Methods and Software for Interior Tomography Applications
Goal: The overall goal of this project is to develop software for interior tomography in terms of analytic and iterative algorithms.