 A novel approach to automatic position calibration for pixelated crystals in gamma imaging

Document Type: Original Article


1 Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran

2 Department of Radiology, University of Massachusetts Medical School, Worcester, MA, USA

3 Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran, Iran

4 Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA

5 Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran


Introduction: The position estimation in gamma detection system will have constant misplacements which can be corrected in the calibration procedure. In the pixelated crystal uniformly irradiation of detector will produce irregular shape due to position estimation errors. This image is called flood field image and is used to calibrate the position estimation. In this work we present a novel approach to automatically calibrate pixelated crystal array position estimation.
Methods: In the flood image of a pixelated crystal array the local peaks represent the estimation of position for the gamma photons that interacted in a single crystal pixel. First, the method detects 2-D peak locations automatically in the case of blurred pixel responses in the presence of noise and disturbance of the image. The algorithm consists of a filtering step for smoothing the image followed by two rounds of local peak detection. After localizing image peaks, the correction routine will map the image locations to the crystal pixels using the thin-plate spline interpolation method.
Results:The algorithm is tested for two flood images obtained from developed detector with different irregularity levels. By configuring constant parameters according to the detector configuration the method detected all crystal pixels in the image and map them correctly. The method further has been tested for10 identical blocks and the result showed automatic peak detection routine for all the blocks.
Conclusion: An automatic peak detection is presented to work instead of time consuming manual calibration routines. The method shows robust performance in the presence of image noise.


Main Subjects

  1. Dahlbom M, Hoffman EJ. An evaluation of a two-dimensional array detector for high resolution PET. IEEE Trans Med Imaging. 1988;7(4):264-72.
  2. Akbarzadeh A, Saba V, Ay MR. New approach for calibration of pixelated scintillation detectors of intraoperative gamma cameras. Iran J Nucl Med. 2017;25(1):34-42.
  3. Kaviani S, Zeraatkar N, Sajedi S, Akbarzadeh A, Gorjizadeh N, Farahani MH, Teimourian B, Ghafarian P, Sabet H, Ay MR. Design and development of a dedicated portable gamma camera system for intra-operative imaging. Phys Med. 2016 Jul;32(7):889-97.
  4. Sajedi S, Zeraatkar N, Taheri M, Kaviani S, Khanmohammadi H, Sarkar S, Sabet H, Ay MR. A Generic, scalable, and cost-effective detector front-end block for PET. IEEE Nucl Sci Symp Med Imaging Conf. (NSS/MIC), 2017.
  5. Levin CS, Zaidi H. Current trends in preclinical PET system design. PET Clin. 2007 Apr;2(2):125-60.
  6. Madsen MT. Recent advances in SPECT imaging. J Nucl Med. 2007 Apr;48(4):661-73.
  7. Flower MA. Webb's physics of medical imaging. 2nd ed. CRC Press; 2012.
  8. Schellenberg G, Stortz G, Goertzen AL. An algorithm for automatic crystal identification in pixelated scintillation detectors using thin plate splines and Gaussian mixture models. Phys Med Biol. 2016 Feb 7;61(3):N90-N101.
  9. Stonger KA, Johnson MT, Optimal calibration of PET crystal position maps using Gaussian mixture models. IEEE Trans Nucl Sci. 2004;51(1):85-90.
  10. Yoshida E, Kimura Y, Kitamura K, Murayama H. Calibration procedure for a DOI detector of high resolution PET through a Gaussian mixture model.  IEEE Trans Nucl Sci. 2004;51(5):2543-2549.
  11. Hu D, Atkins BE, Lenox MW, Castleberry B, Siegel SB. A neural network based algorithm for building crystal look-up table of PET block detector. IEEE Nucl Sci Symp Conf Record, 2006.
  12. Hu D, Gremillion T. Verification of neural network based algorithm for crystal identification of PET block detector. IEEE Nucl Sci Symp Conf Record, 2007.
  13. Bookstein FL. Principal warps: Thin-plate splines and the decomposition of deformations.  IEEE Trans Pattern Anal Mach Intell. 1989;11(6):567-585.
  14. Zeraatkar N, Sajedi S, Kaviani S, Taheri M, Khanmohammadi H, Sarkar S, Ay MR.  Development of a preclinical PET system based on pixelated LYSO crystals and SiPM. IEEE Nucl Sci Symp Med Imaging Conf. (NSS/MIC), 2017.