The goal of this proposal is to develop and evaluate new CT image reconstruction algorithms for the purpose of detecting and characterizing malignant tumors in the lung. New, accurate and efficient algorithms are needed to take advantage of the large cone-angle acceptance of novel detectors that are expected to be employed in future medical CT systems. The developed and existing algorithms will be tested against physical factors such as partial volume averaging, polychromatic x-ray sources, signal noise and x-ray scatter. These physical factors are present in real CT systems and are known to degrade image quality. Algorithms for taking advantage of possible, x-ray energy resolving capability of future detectors will also be explored. Such a capability may alleviate beam-hardening artifacts, and provide additional diagnostic information. One clinical area that the new helical, cone-beam CT technology might be applied to is lung cancer screening. The rapid volume scanning will reduce motion artifacts, and detection of x-rays in different energy windows may assist radiologists in distinguishing malignant and benign nodules in the lung. The developed and existing image reconstruction algorithms will be assessed initially by performance of specific detection and estimation tasks on simple computer-generated phantoms. In order to provide a better means for assessing image reconstruction algorithms for the purpose of lung cancer screening, computer simulated, anthropomorphic chest phantoms, modeling the physical properties of normal and pathological structure of the lung, will be developed. Such phantoms will provide a unique opportunity to assess image reconstruction algorithms, because the data sets will have similar features to actual patient data, the number of generated data sets can be arbitrarily large, and the pathology of each data set is known. Computer-aided diagnosis programs for lung nodule detection may be employed as an observer to assess various image reconstruction and processing algorithms.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Scientist Development Award - Research & Training (K01)
Project #
5K01EB003913-02
Application #
6929758
Study Section
Special Emphasis Panel (ZEB1-OSR-B (M1))
Program Officer
Khachaturian, Henry
Project Start
2004-08-01
Project End
2008-07-31
Budget Start
2005-08-01
Budget End
2006-07-31
Support Year
2
Fiscal Year
2005
Total Cost
$81,706
Indirect Cost
Name
University of Chicago
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
005421136
City
Chicago
State
IL
Country
United States
Zip Code
60637
Pan, Xiaochuan; Sidky, Emil Y; Vannier, Michael (2009) Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction? Inverse Probl 25:1230009
LaRoque, Samuel J; Sidky, Emil Y; Pan, Xiaochuan (2008) Accurate image reconstruction from few-view and limited-angle data in diffraction tomography. J Opt Soc Am A Opt Image Sci Vis 25:1772-82
Sidky, Emil Y; Pan, Xiaochuan (2008) Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Phys Med Biol 53:4777-807
Xia, Dan; Yu, Lifeng; Sidky, Emil Y et al. (2007) Noise properties of chord-image reconstruction. IEEE Trans Med Imaging 26:1328-44
Anastasio, Mark A; Zou, Yu; Sidky, Emil Y et al. (2007) Local cone-beam tomography image reconstruction on chords. J Opt Soc Am A Opt Image Sci Vis 24:1569-79
Yu, Lifeng; Zou, Yu; Sidky, Emil Y et al. (2006) Region of interest reconstruction from truncated data in circular cone-beam CT. IEEE Trans Med Imaging 25:869-81
Pan, Xiaochuan; Zou, Yu; Xia, Dan et al. (2005) Reconstruction of 3D regions-of-interest from data in reduced helical cone-beam scans. Technol Cancer Res Treat 4:143-50
Zou, Yu; Pan, Xiaochuan; Sidky, Emil Y (2005) Theory and algorithms for image reconstruction on chords and within regions of interest. J Opt Soc Am A Opt Image Sci Vis 22:2372-84
Sidky, Emil Y; Zou, Yu; Pan, Xiaochuan (2005) Minimum data image reconstruction algorithms with shift-invariant filtering for helical, cone-beam CT. Phys Med Biol 50:1643-57