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EDGE-BASED PEAK POSITION SEARCH ALGORITHM FOR PET DETECTORS

EDGE-BASED PEAK POSITION SEARCH ALGORITHM FOR PET DETECTORS. Presenter: Kun Di Advisor: Dr. Chung-E Wang Dr. Ted Krovetz. Department of Computer Science California State University, Sacramento November 22, 2010. Agenda. Motivation Background Methodology Edge Detection

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EDGE-BASED PEAK POSITION SEARCH ALGORITHM FOR PET DETECTORS

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  1. EDGE-BASED PEAK POSITION SEARCH ALGORITHM FOR PET DETECTORS Presenter: Kun Di Advisor: Dr. Chung-E Wang Dr. Ted Krovetz Department of Computer ScienceCalifornia State University, Sacramento November 22, 2010

  2. Agenda • Motivation • Background • Methodology • Edge Detection • Gradient Information Analysis • Peaks Detection • Results • Summary and Future Work Department of Computer Science

  3. Motivation • The positron emission tomography (PET) scanner • Gamma Rays • Array of Scintillation Crystals • Photomultiplier Tubes (PMTs), Avalanche Photo Diodes (APDs) • Coincidence processing • Calibration: • Position Profile (Peak Positions) • Automatic peak detection is desirable because manually selecting each peak is time-consuming, especially for large crystal arrays (e.g. 20x20). Pictures (except the right-bottom one) are from http://en.wikipedia.org/wiki/Positron_emission_tomography Department of Computer Science

  4. Background • Previous Effort • Neural network based algorithm[2] • Non-rigid registration to a Fourier-based template [3] • PCA-based algorithm [4] • Morphological-based algorithm [5][6] • Main obstacles • Distortion • Peak fusion. Department of Computer Science

  5. Edge Detection • Canny Edge - Continuous Edge • Gaussian Smooth • Gradient (magnitude) • Non-Maximum Suppression • High and Low Threshold • Trace Edge • Gradient Department of Computer Science

  6. Gradient Information Analysis • Gradient Information • Magnitude • Direction • Special Points • Top, Bottom, Left, right • Break Points • Horizontal, Vertical Department of Computer Science

  7. Peaks Detection • Algorithm Raw image input; Generate Gaussian smoothed image; Edge detection according to gradient magnitude; Get interested region according to the edges and gradient directions of each point on each edge; Identify peaks according to the number of crystals and hit-sum of each interested region; Manually correct the peaks if necessary; Save the peaks; Department of Computer Science

  8. Tools Implementation • Automatic peaks detection • Adjustment of settings to achieve better result • Manual correction • Sorting peaks • Saving peak positions to text file for writing back to PET system Department of Computer Science

  9. Results Department of Computer Science

  10. Summary and Future Work • The tool works pretty good. Distortion problem has been solved. • The result depends upon the quality of edge detection. • Low quality of edge detection may cause peak missing in case of highly fused peaks. • In order to improve the performance, the whole image pattern should be considered. Therefore, a supplemental algorithm is going to be designed Department of Computer Science

  11. Reference • [1] http://en.wikipedia.org/wiki/Positron_emission_tomography • [2] D. Hu, B. Atkins, M. Lenox, B. Castleberry, and S. Siegel, “A neural network based algorithm for building crystal look-up table of PET block detector,” in Proc. IEEE Nuclear Science Symp. Conf. Rec., Nov. 2006, vol. 4, pp. 2458–2461. • [3] A. Chaudhari, A. Joshi, S. Bowen, R. Leahy, S. Cherry, and R. Badawi, “Crystal identification in positron emission tomography using nonrigid registration to a Fourier-based template,” Phys. Med. Biol., vol. 53, no. 18, pp. 5011–5027, Sep. 2008. • [4] J. Breuer and K. Wienhard, "PCA-Based Algorithm for Generation of Crystal Lookup Tables for PET Block Detecto," IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL. 56, NO. 3, JUNE 2009, pp. 602-607. • [5] Albert Mao, Student Volunteer, Imaging Physics Laboratory, Nuclear Medicine Department, Clinical Center, National Institutes of Health, "Positron Emission Tomograph Detector Module Calibration Through Morphological Algorithms and Interactive Correction," • [6] Z. Hu, C. Kao, W. Liu, Y. Dong, Z. Zhang, Q. Xie, C. Chen, "Semi-Automatic Position Calibration for a Dual-Head Small Animal PET Scanner," 2007 IEEE Nuclear Science Symposium Conference Record, pp. 1618-1621. • [7] P. Després, W. C. Barber, T. Funk, M. McClish, K. S. Shah, and B. H. Hasegawa, "Modeling and Correction of Spatial Distortion in Position-Sensitive Avalanche Photodiodes," IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL. 54, NO. 1, FEBRUARY 2007, pp. 23-29. • [8] J. Zhang, P. Olcott, and C. Levin, “A new positioning algorithm for position-sensitive avalanche photodiodes,” IEEE Trans. Nucl. Sci., vol. 54, no. 3, pp. 433–437, Jun. 2007. • [9] J. Canny, "A computational approach to edge detection", IEEE Trans. Pattern Analysis and Machine Intelligence, vol 8, pages 679-714, 1986. Department of Computer Science

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