Abstract
Dynamic PET imaging provides important spatial-temporal information for metabolism analysis of organs and tissues, and generates a great reference for clinical diagnosis and pharmacokinetic analysis. Due to poor statistical properties of the measurement data in low count dynamic PET acquisition and disturbances from surrounding tissues, identifying small lesions inside the human body is still a challenging issue. The uncertainties in estimating the arterial input function will also limit the accuracy and reliability of the metabolism analysis of lesions. Furthermore, the sizes of the patients and the motions during PET acquisition will yield mismatch against general purpose reconstruction system matrix, this will also affect the quantitative accuracy of metabolism analyses of lesions. In this paper, we present a dynamic PET metabolism analysis framework by defining a patient adaptive system matrix to improve the lesion metabolism analysis. Both patient size information and potential small lesions are incorporated by simulations of phantoms of different sizes and individual point source responses. The new framework improves the quantitative accuracy of lesion metabolism analysis, and makes the lesion identification more precisely. The requirement of accurate input functions is also reduced. Experiments are conducted on Monte Carlo simulated data set for quantitative analysis and validation, and on real patient scans for assessment of clinical potential.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Rahmim, A., Tang, J., Zaidi, H.: Four-Dimensional (4D) Image Reconstruction Strategies in Dynamic PET: Beyond Conventional Independent Frame Reconstruction. Medical Physics 36(8), 3654 (2009)
Wang, G., Qi, J.: Generalized Algorithms for Direct Reconstruction of Parametric Images From Dynamic PET Data. IEEE Transactions on Medical Imaging 28(11), 1717–1726 (2009)
Gao, F., Liu, H., Shi, P.: Robust Estimation of Kinetic Parameters in Dynamic PET Imaging. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 492–499. Springer, Heidelberg (2011)
Moehrs, S., Defrise, M., Belcari, N., Guerra, A.D., Bartoli, A., Fabbri, S., Zanetti, G.: Multi-Ray-Based System Matrix Generation for 3D PET Reconstruction. Physics in Medicine and Biology 53(23), 6925 (2008)
Panin, V., Kehren, F., Rothfuss, H., Hu, D., Michel, C., Casey, M.: PET Reconstruction with System Matrix Derived from Point Source Measurements. IEEE Transactions on Nuclear Science 53(1), 152–159 (2006)
Zhang, L., Staelens, S., Holen, R.V., Beenhouwer, J.D., Verhaeghe, J., Kawrakow, I., Vandenberghe, S.: Fast and Memory-Efficient Monte Carlo-Based Image Reconstruction for Whole-Body PET. Medical Physics 37(7), 3667 (2010)
Su, K.H., Wu, L.C., Lee, J.S., Liu, R.S., Chen, J.C.: A Novel Method to Improve Image Quality for 2-D Small Animal PET Reconstruction by Correcting a Monte Carlo-Simulated System Matrix Using an Artificial Neural Network. IEEE Transactions on Nuclear Science 56(3), 704–714 (2009)
Wu, H., Pal, D.O., Sullivan, J., Tai, Y.C.: A Feasibility Study of a Prototype PET Insert Device to Convert a General-Purpose Animal PET Scanner to Higher Resolution. Journal of Nuclear Medicine 49(1), 79–87 (2008)
Li, Z., Li, Q., Yu, X., Conti, P., Leahy, R.: Lesion Detection in Dynamic FDG-PET Using Matched Subspace Detection. IEEE Transactions on Medical Imaging 28(2), 230–240 (2009)
Laffon, E., de Clermont, H., Vernejoux, J.M., Jougon, J., Marthan, R.: Feasibility of Assessing [18F]FDG Lung Metabolism with Late Dynamic PET Imaging. Molecular Imaging and Biology 13(2), 378–384 (2011)
Vriens, D., Visser, E.P., de Geus-Oei, L.F., Oyen, W.J.G.: Methodological Considerations in Quantification of Oncological FDG PET Studies. European Journal of Nuclear Medicine and Molecular Imaging 37(7), 1408–1425 (2010)
Fessler, J., Erdogan, H.: A Paraboloidal Surrogates Algorithm for Convergent Penalized-Likelihood Emission Image Reconstruction. In: Nuclear Science Symposium, Conference Record, vol. 2, pp. 1132–1135 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gao, F., Liu, H., Shi, P. (2012). Patient-Adaptive Lesion Metabolism Analysis by Dynamic PET Images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33454-2_69
Download citation
DOI: https://doi.org/10.1007/978-3-642-33454-2_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33453-5
Online ISBN: 978-3-642-33454-2
eBook Packages: Computer ScienceComputer Science (R0)