We propose a general and unified framework to harness the abundant performance of current and future generation commodity graphics hardware (GPUs) for the purpose of tomographic reconstruction from projections. Preliminary results indicate that a performance improvement on the order of 1-2 magnitudes over traditional CPU-based approaches can be obtained. To proof and ensure the generality of our framework and approach, our proposal will address the reconstruction from a diverse set of raw data (such as kV X-rays, MV X-rays, and protons), with a diverse set of reconstruction algorithms (such as maximum likelihood algorithms, algebraic methods, and Feldkamp-style filtered backprojection), and within a diverse set of application scenarios (such as CT, SPECT, PET, and Proton CT). Starting from the basic reconstruction operators, we will model all dominant physical and algorithmic effects that occur in radiation-based tomography, such as depth weighting, detector geometric response, attenuation weighting, and scatter compensation, using implementations that map optimally to the graphics hardware and take full advantage of its computational architecture. The rapid speeds of reconstruction will also enable a new concept that we call Visual Reconstruction Steering (VRS). This VRS framework will consist of a visual interface in which users can build and interactively control reconstructions as they occur on the GPU, and in which they can visualize and assess the results in real time. Our research results, all software, and documentation will be disseminated via a dedicated website, www.Rapid-CT.org.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21EB004099-01A1
Application #
6921678
Study Section
Special Emphasis Panel (ZRG1-BCHI (01))
Program Officer
Peng, Grace
Project Start
2005-04-01
Project End
2007-03-31
Budget Start
2005-04-01
Budget End
2006-03-31
Support Year
1
Fiscal Year
2005
Total Cost
$216,262
Indirect Cost
Name
State University New York Stony Brook
Department
Biostatistics & Other Math Sci
Type
Schools of Engineering
DUNS #
804878247
City
Stony Brook
State
NY
Country
United States
Zip Code
11794
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Li, Shengying; Jackowski, Marcel; Dione, Donald P et al. (2010) Refraction corrected transmission ultrasound computed tomography for application in breast imaging. Med Phys 37:2233-46
Xu, Fang; Xu, Wei; Jones, Mel et al. (2010) On the efficiency of iterative ordered subset reconstruction algorithms for acceleration on GPUs. Comput Methods Programs Biomed 98:261-70
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Giesen, Joachim; Mueller, Klaus; Schuberth, Eva et al. (2007) Conjoint analysis to measure the perceived quality in volume rendering. IEEE Trans Vis Comput Graph 13:1664-71
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Xu, Fang; Mueller, Klaus (2007) Real-time 3D computed tomographic reconstruction using commodity graphics hardware. Phys Med Biol 52:3405-19