List-event Data Resampling for Quantitative Improvement of PET Image

PET 영상의 정량적 개선을 위한 리스트-이벤트 데이터 재추출

  • Woo, Sang-Keun (Molecular Imaging Research Center, Korea Institute of Radiological and Medical Sciences) ;
  • Ju, Jung Woo (Molecular Imaging Research Center, Korea Institute of Radiological and Medical Sciences) ;
  • Kim, Ji Min (Molecular Imaging Research Center, Korea Institute of Radiological and Medical Sciences) ;
  • Kang, Joo Hyun (Molecular Imaging Research Center, Korea Institute of Radiological and Medical Sciences) ;
  • Lim, Sang Moo (Molecular Imaging Research Center, Korea Institute of Radiological and Medical Sciences) ;
  • Kim, Kyeong Min (Molecular Imaging Research Center, Korea Institute of Radiological and Medical Sciences)
  • 우상근 (한국원자력의학원 방사선의학연구소 분자영상연구부) ;
  • 유정우 (한국원자력의학원 방사선의학연구소 분자영상연구부) ;
  • 김지민 (한국원자력의학원 방사선의학연구소 분자영상연구부) ;
  • 강주현 (한국원자력의학원 방사선의학연구소 분자영상연구부) ;
  • 임상무 (한국원자력의학원 방사선의학연구소 분자영상연구부) ;
  • 김경민 (한국원자력의학원 방사선의학연구소 분자영상연구부)
  • Received : 2012.12.07
  • Accepted : 2012.12.10
  • Published : 2012.12.31

Abstract

Multimodal-imaging technique has been rapidly developed for improvement of diagnosis and evaluation of therapeutic effects. In despite of integrated hardware, registration accuracy was decreased due to a discrepancy between multimodal image and insufficiency of count in accordance with different acquisition method of each modality. The purpose of this study was to improve the PET image by event data resampling through analysis of data format, noise and statistical properties of small animal PET list data. Inveon PET listmode data was acquired as static data for 10 min after 60 min of 37 MBq/0.1 ml $^{18}F$-FDG injection via tail vein. Listmode data format was consist of packet containing 48 bit in which divided 8 bit header and 40 bit payload space. Realigned sinogram was generated from resampled event data of original listmode by using adjustment of LOR location, simple event magnification and nonparametric bootstrap. Sinogram was reconstructed for imaging using OSEM 2D algorithm with 16 subset and 4 iterations. Prompt coincidence was 13,940,707 count measured from PET data header and 13,936,687 count measured from analysis of list-event data. In simple event magnification of PET data, maximum was improved from 1.336 to 1.743, but noise was also increased. Resampling efficiency of PET data was assessed from de-noised and improved image by shift operation of payload value of sequential packet. Bootstrap resampling technique provides the PET image which noise and statistical properties was improved. List-event data resampling method would be aid to improve registration accuracy and early diagnosis efficiency.

다중영상화기술은 진단 및 치료 반응평가의 성능향상을 위하여 활발히 연구되고 있으며 하드웨어의 통합에도 불구하고 기기간의 획득방법의 차이에 따라 영상간의 불일치와 계수부족으로 인하여 정합도를 떨어뜨린다. 이에 본 연구에서는 소동물 PET 리스트모드 데이터의 저장형식을 분석하고 잡음 및 통계적 특성을 향상시키기 위하여 이벤트 데이터를 재추출하여 정량적으로 개선된 PET 영상을 획득하고자 하였다. 소동물 리스트모드 Inveon PET 데이터는 소동물에 37 MBq/0.1 ml를 꼬리정맥에 주사하고 60분 후 10분 동안 정적데이터를 획득하였다. 생체신호와 같이 획득된 리스트모드 데이터형식은 48 비트의 패킷크기로 이루어져 있으며 패킷 내에서는 8 비트의 헤더와 40 비트의 payload 영역으로 나누어져 있다. 사이노그램 생성은 그레이코드로 각 패킷의 순서와 흐름을 평가하고 각 패킷의 순서를 CPU에서 검출기위치 변환과 단순 증가 그리고 비모수 부트스트랩 기법을 이용하여 재추출하여 새로운 사이노그램을 생성하였다. 영상은 3 span과 31 ring difference로 설정하여 생성된 사이노그램은 산란 및 감쇠보정을 고려하지 않고 16부분 집합으로 4회 반복하는 OSEM 2D 알고리즘을 이용하여 재구성하였다. 획득된 PET 데이터의 헤더정보에서의 동시계수의 총수는 1,394만 계수였으며, 리스트-이벤트 데이터의 패킷을 분석한 동시계수의 총수는 1,293만 계수였다. PET 데이터의 단순 증가는 최대값이 1.336에서 1.743으로 향상되었으나 잡음이 같이 증가됨을 확인하였다. PET 데이터 재추출 성능은 순차적인 패킷의 payload 값을 시프트연산을 통해 데이터의 위치를 이동시킴으로써 특정 잡음이 제거되거나 대조도가 향상되는 영상을 획득할 수 있었다. 부트스트랩 재추출 기법은 영상의 잡음과 통계적 특성이 개선된 PET 영상을 제공하여 다중영상화시 정합도를 향상시켜 질환의 조기 진단 성능을 향상시킬 수 있을 것으로 기대된다.

Keywords

References

  1. Pichler BJ, Wehrl HF, Kolb A, Judenhofer MS: Positron emission tomography/magnetic resonance imaging: the next generation of multimodality imaging?. Semin Nucl Med 38(3): 199-208 (2008) https://doi.org/10.1053/j.semnuclmed.2008.02.001
  2. Shan ZY, Mateja SJ, Reddick WE, Glass JO, Shulkin BL: Retrospective evaluation of PET-MRI registration algorithms. J Digit Imaging 24(3):485-493 (2011) https://doi.org/10.1007/s10278-010-9300-y
  3. Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P: Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 16(2):187-198 (1997) https://doi.org/10.1109/42.563664
  4. Wells WM 3rd, Viola P, Atsumi H, Nakajima S, Kikinis R: Multi-modal volume registration by maximization of mutual information. Med Image Anal 1(1):35-51 (1996) https://doi.org/10.1016/S1361-8415(01)80004-9
  5. Pluim JP, Maintz JB, Viergever MA: Mutual-informationbased registration of medical images: a survey. IEEE Trans Med Imaging 22(8):986-1004 (2003) https://doi.org/10.1109/TMI.2003.815867
  6. Gan R, Wu J, Chung AC, Yu SC, Wells WM 3rd: Multiresolution image registration based on Kullback-Leibler distance. Barillot C, Haynor DR, Hellier P: In The 7th International Conference on Medical Image Computing and Computer Assisted Intervention. Springer-Verlag, Berlin, Heidelberg (2004), pp. 599-606
  7. Awate SP, Whitaker RT: Feature-preserving MRI denoising: a nonparametric empirical Bayes approach. IEEE Trans Med Imaging 26(9):1242-1255 (2007) https://doi.org/10.1109/TMI.2007.900319
  8. Cohen-Adad J, Descoteaux M, Wald LL: Quality assessment of high angular resolution diffusion imaging data using bootstrap on Q-ball reconstruction. J Magn Reson Imaging 33(5):1194-1208 (2011) https://doi.org/10.1002/jmri.22535
  9. Habib J, Auer DP, Morgan PS: A quantitative analysis of the benefits of cardiac gating In practical diffusion tensor imaging of the brain. Magn Reson Med 63(4):1098-1103 (2010) https://doi.org/10.1002/mrm.22232
  10. Büther F, Dawood M, Stegger L, et al: List mode-driven cardiac and respiratory gating in PET. J Nucl Med 50(5):674-681 (2009) https://doi.org/10.2967/jnumed.108.059204
  11. Huang SC, Hu Y, Wardak M, et al: A bootstrap method for identifying image regions affected by intra-scan body movement during a PET/CT scan. IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). Valencia, (2011), pp. 2905-2908
  12. Buvat I: A non-parametric bootstrap approach for analysing the statistical properties of SPECT and PET images, Phys Med Biol 47(10):1761-1775 (2002) https://doi.org/10.1088/0031-9155/47/10/311
  13. Kukreja SL, Gunn RN: Bootstrapped DEPICT for error estimation in PET functional imaging. NeuroImage 21(3):1096-1104 (2004) https://doi.org/10.1016/j.neuroimage.2003.10.015
  14. Groiselle CJ, Glick SJ: Using the bootstrap method to evaluate image noise for investigation of axial collimation in hybrid PET. IEEE Trans Nucl Sci 52(1):95-101 (2005) https://doi.org/10.1109/TNS.2005.843625
  15. Som P, Atkins HL, Bandoypadhyay D, et al: A fluorinated glucose analog, 2-fluoro-2-deoxy-D-glucose(F-18): nontoxic tracer for rapid tumor detection. J Nucl Med 21(7):670-675 (1980)
  16. Woo SK, Kim KM, Chun KJ: Small animal [18F]FDG PET imaging for tumor model study. Nucl Med Mol Imaging 42(1):1-7 (2008)
  17. Woo SK, Lee TS, Kim KM, et al: Anesthesia condition for (18)F-FDG imaging of lung metastasis tumors using small animal PET. Nucl Med Biol 35(1):143-150 (2008) https://doi.org/10.1016/j.nucmedbio.2007.10.003
  18. Bao Q, Newport D, Chen M, David BS, Arion FC: Performance evaluation of the Inveon dedicated PET preclinical tomograph based on the NEMA NU-4 standards. J Nucl Med 50:401-408 (2009) https://doi.org/10.2967/jnumed.108.056374
  19. Hudson HM, Larkin RS: Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans Med Imaging 13(4):601-609 (1994) https://doi.org/10.1109/42.363108
  20. Haynor DR, Woods SD: Resampling estimates of precision in emission tomography. IEEE Trans Med Imaging 8(4):337-343 (1989) https://doi.org/10.1109/42.41486
  21. Dalbohm M: Estimation of Image Noise in PET Using the Bootstrap Method. IEEE Trans Nucl Sci 49(5):2062-2066 (2002) https://doi.org/10.1109/TNS.2002.803688
  22. Lartizien C, Aubin JB, Buvat I: Comparison of Bootstrap Resampling Methods for 3-D PET Imaging. IEEE Trans Med Imaging 29(7):1442-1454 (2010) https://doi.org/10.1109/TMI.2010.2048119