Skip to main content
Log in

Automated Method for Small-Animal PET Image Registration with Intrinsic Validation

  • Research Article
  • Published:
Molecular Imaging and Biology Aims and scope Submit manuscript

Abstract

Purpose

We propose and compare different registration approaches to align small-animal PET studies and a procedure to validate the results by means of objective registration consistency measurements.

Procedures

We have applied a registration algorithm based on information theory, using different approaches to mask the reference image. The registration consistency allows for the detection of incorrect registrations. This methodology has been evaluated on a test dataset (FDG-PET rat brain images).

Results

The results show that a multiresolution two-step registration approach based on the use of the whole image at the low resolution step, while masking the brain at the high resolution step, provides the best robustness (87.5% registration success) and highest accuracy (0.67-mm average).

Conclusions

The major advantages of our approach are minimal user interaction and automatic assessment of the registration error, avoiding visual inspection of the results, thus facilitating the accurate, objective, and rapid analysis of large groups of rodent PET images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Friston KJ, Holmes AP, Worsley KJ et al (1995) Statistical parametric maps in functional imaging: a general linear approach. Hum Brain Mapp 2:189–210

    Article  Google Scholar 

  2. Thompson PM, Woods RP, Mega MS et al (2000) Mathematical/computational challenges in creating deformable and probabilistic atlases of the human brain. Hum Brain Mapp 9(2):81–92

    Article  PubMed  CAS  Google Scholar 

  3. Friston KJ, Ashburner J, Frith C et al (1995) Spatial registration and normalization of images. Hum Brain Mapp 2:165–189

    Article  Google Scholar 

  4. Rubins DJ, Melega WP, Lacan G et al (2003) Development and evaluation of an automated atlas-based image analysis method for microPET studies of the rat brain. Neuroimage 20(4):2100–2118

    Article  PubMed  Google Scholar 

  5. Gispert JD, Pascau J, Reig S et al (2003) Influence of the normalization template on the outcome of statistical parametric mapping of PET scans. Neuroimage 19(3):601–612

    Article  PubMed  CAS  Google Scholar 

  6. Toga AW, Thompson PM (2001) The role of image registration in brain mapping. Image Vis Comput 19(1–2):3–24

    Article  Google Scholar 

  7. Pluim JP, Maintz JB, Viergever MA (2003) Mutual-information-based registration of medical images: a survey. IEEE Trans Med Imag 22(8):986–1004

    Article  Google Scholar 

  8. Maintz JB, Viergever MA (1998) A survey of medical image registration. Med Image Anal 2(1):1–36

    Article  PubMed  CAS  Google Scholar 

  9. Vaquero JJ, Desco M, Pascau J et al (2001) PET, CT, and MR image registration of the rat brain and skull. IEEE Trans Nucl Sci 48(4):1440–1445

    Article  Google Scholar 

  10. Rowland DJ, Garbow JR, Laforest R et al (2005) Registration of F-18 FDG microPET and small-animal MRI. Nucl Med Biol 32(6):567–572

    Article  PubMed  CAS  Google Scholar 

  11. Fei BW, Wang HS, Muzic RF et al (2006) Deformable and rigid registration of MRI and microPET images for photodynamic therapy of cancer in mice. Med Phys 33(3):753–760

    Article  PubMed  Google Scholar 

  12. Woods RP, Grafton ST, Holmes CJ et al (1998) Automated image registration: I. General methods and intrasubject, intramodality validation. J Comput Assist Tomogr 22(1):139–152

    Article  PubMed  CAS  Google Scholar 

  13. Studholme C, Hill DLG, Hawkes DJ (1999) An overlap invariant entropy measure of 3D medical image alignment. Pattern Recogn 32(1):71–86

    Article  Google Scholar 

  14. Maes F, Collignon A, Vandermeulen D et al (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med Imag 16(2):187–198

    Article  CAS  Google Scholar 

  15. Thurfjell L, Lau YH, Andersson JL et al (2000) Improved efficiency for MRI-SPET registration based on mutual information. Eur J Nucl Med 27(7):847–856

    Article  PubMed  CAS  Google Scholar 

  16. Pluim JP, Maintz JB, Viergever MA (2000) Interpolation artefacts in mutual information based image registration. Comput Vis Image Underst 77(2):211–232

    Article  Google Scholar 

  17. Tsao J (2003) Interpolation artifacts in multimodality image registration based on maximization of mutual information. IEEE Trans Med Imag 22(7):854–864

    Article  Google Scholar 

  18. Maes F, Vandermeulen D, Suetens P (1999) Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information. Med Image Anal 3(4):373–386

    Article  PubMed  CAS  Google Scholar 

  19. Pascau J, Vaquero J, Abella M et al (2006) Multimodality workstation for small animal image visualization and analysis. Mol Imaging Biol 8(2):97–98

    Google Scholar 

  20. Hill DL, Batchelor PG, Holden M et al (2001) Medical image registration. Phys Med Biol 46(3):R1–R45

    Article  PubMed  CAS  Google Scholar 

  21. Holden M, Hill DL, Denton ER et al (2000) Voxel similarity measures for 3-D serial MR brain image registration. IEEE Trans Med Imag 19(2):94–102

    Article  CAS  Google Scholar 

  22. Arun KS, Huang TS, Blostein SD (1987) Least-squares fitting of two 3-D point sets. IEEE Trans Pattern Anal Mach Intell 9(5):698–700

    Article  Google Scholar 

  23. Fitzpatrick JM, West JB, Maurer CR Jr (1998) Predicting error in rigid-body point-based registration. IEEE Trans Med Imag 17(5):694–702

    Article  CAS  Google Scholar 

  24. Thanos PK, Michaelides M, Gispert JD et al (2008) Differences in response to food stimuli in a rat model of obesity: in-vivo assessment of brain glucose metabolism. Int J Obes (May 13, 2008)

  25. Qi J, Leahy RM, Cherry SR et al (1998) High-resolution 3D Bayesian image reconstruction using the microPET small-animal scanner. Phys Med Biol 43(4):1001–1013

    Article  PubMed  CAS  Google Scholar 

  26. Ruangma A, Laforest R, Bai B et al (2004) Characterization of USC-MAP image reconstruction on MicroPET-R4. IEEE Nucl Sci Symp Conf Rec 6:3449–3453

    Article  Google Scholar 

  27. Casteels C, Vermaelen P, Nuyts J et al (2006) Construction and evaluation of multitracer small-animal PET probabilistic atlases for voxel-based functional mapping of the rat brain. J Nucl Med 47(11):1858–1866

    PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by projects CIBER CB06/01/0079 (Ministerio de Sanidad y Consumo) and CDTEAM (CENIT program, Ministerio de Industria). Further support came from NIAAA Intramural Research Program (AA 11034 and AA07574, AA07611) and the US Department of Energy (DE-AC02-98CH10886).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Javier Pascau.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pascau, J., Gispert, J., Michaelides, M. et al. Automated Method for Small-Animal PET Image Registration with Intrinsic Validation. Mol Imaging Biol 11, 107–113 (2009). https://doi.org/10.1007/s11307-008-0166-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11307-008-0166-z

Key words

Navigation