Abstract
The combination of functional and anatomical imaging technologies such as Positron Emission Tomography (PET) and Computed Tomography (CT) has shown its value in the preclinical and clinical fields. In PET/CT hybrid acquisition systems, CT-derived attenuation maps enable a more accurate PET reconstruction. However, CT provides only very limited soft-tissue contrast and exposes the patient to an additional radiation dose. In comparison, Magnetic Resonance Imaging (MRI) provides good soft-tissue contrast and the ability to study functional activation and tissue microstructures, but does not directly provide patient-specific electron density maps for PET reconstruction.
The aim of the proposed work is to improve PET/MR reconstruction by generating synthetic CTs and attenuation-maps. The synthetic images are generated through a multi-atlas information propagation scheme, locally matching the MRI-derived patient’s morphology to a database of pre-acquired MRI/CT pairs. Results show improvements in CT synthesis and PET reconstruction accuracy when compared to a segmentation method using an Ultrashort-Echo-Time MRI sequence.
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Keywords
- Positron Emission Tomography
- Attenuation Correction
- Label Fusion
- Atlas Database
- Positron Emission Tomography Reconstruction
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
Von Schulthess, G.K., Kuhn, F.P., Kaufmann, P., Veit-Haibach, P.: Clinical positron emission tomography/magnetic resonance imaging applications. Seminars in Nuclear Medicine 43(1), 3–10 (2013)
Martinez-Möller, A., Souvatzoglou, M., Delso, G., Bundschuh, R.A., Chefd’hotel, C., Ziegler, S.I., Navab, N., Schwaiger, M., Nekolla, S.G.: Tissue classification as a potential approach for attenuation correction in whole-body PET/MRI: evaluation with PET/CT data. Journal of Nuclear Medicine 50(4), 520–526 (2009)
Schleyer, P.J., Schaeffter, T., Marsden, P.K.: The effect of inaccurate bone attenuation coefficient and segmentation on reconstructed PET images. Nuclear Medicine Communications 31(8), 708–716 (2010)
Keereman, V., Fierens, Y., Broux, T., De Deene, Y., Lonneux, M., Vandenberghe, S.: MRI-based attenuation correction for PET/MRI using ultrashort echo time sequences. Journal of Nuclear Medicine 51(5), 812–818 (2010)
Berker, Y., Franke, J., Salomon, A., Palmowski, M., Donker, H.C.W., Temur, Y., Izquierdo-Garcia, D., Fayad, Z.A., Kiessling, F., Schulz, V.: MRI-based attenuation correction for hybrid PET/MRI systems: a 4-class tissue segmentation technique using a combined ultrashort-echo-time/Dixon MRI sequence. JNM 53(5) (2012)
Johansson, A., Karlsson, M., Nyholm, T.: CT substitute derived from MRI sequences with ultrashort echo time. Medical Physics 38(5), 2708 (2011)
Schreibmann, E., Nye, J.A., Schuster, D.M., Martin, D.R., Votaw, J., Fox, T.: MR-based attenuation correction for hybrid PET-MR brain imaging systems using deformable image registration. Medical Physics 37(5), 2101 (2010)
Hofmann, M., Steinke, F., Scheel, V., Charpiat, G., Farquhar, J., Aschoff, P., Brady, M., Schölkopf, B., Pichler, B.J.: MRI-based attenuation correction for PET/MRI: a novel approach combining pattern recognition and atlas registration. Journal of Nuclear Medicine 49(11), 1875–1883 (2008)
Heckemann, R.A., Hajnal, J.V., Aljabar, P., Rueckert, D., Hammers, A.: Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage 33(1), 115–126 (2006)
Sabuncu, M.R., Van Leemput, K., Fischl, B., Golland, P.: A generative model for image segmentation based on label fusion. TMI 29(10) (2010)
Ourselin, S., Roche, A., Subsol, G.: Reconstructing a 3D structure from serial histological sections. Image and Vision Computing 19(2001), 25–31 (2000)
Modat, M., Ridgway, G.R., Taylor, Z.A., Lehmann, M., Barnes, J., Hawkes, D.J., Fox, N.C., Ourselin, S.: Fast free-form deformation using graphics processing units. Computer Methods and Programs in Biomedicine 98(3), 278–284 (2010)
Yushkevich, P.A., Wang, H., Pluta, J., Das, S.R., Craige, C., Avants, B.B., Weiner, M.W., Mueller, S.: Nearly automatic segmentation of hippocampal subfields in in vivo focal T2-weighted MRI. NeuroImage 53(4), 1208–1224 (2010)
Cachier, P., Bardinet, E., Dormont, D., Pennec, X., Ayache, N.: Iconic feature based nonrigid registration: the PASHA algorithm. CVIU 89(2-3) (2003)
Cardoso, M.J., Wolz, R., Modat, M., Fox, N.C., Rueckert, D., Ourselin, S.: Geodesic Information Flows. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 262–270. Springer, Heidelberg (2012)
Burger, C., Goerres, G., Schoenes, S., Buck, A., Lonn, A.H.R., Von Schulthess, G.K.: PET attenuation coefficients from CT images: experimental evaluation of the transformation of CT into PET 511-keV attenuation coefficients. European Journal of Nuclear Medicine and Molecular Imaging 29(7), 922–927 (2002)
Pedemonte, S., Bousse, A., Erlandsson, K., Modat, M., Arridge, S., Hutton, B.F., Ourselin, S.: GPU accelerated rotation-based emission tomography reconstruction. In: IEEE Nuclear Science Symposuim, pp. 2657–2661 (2010)
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Burgos, N. et al. (2013). Attenuation Correction Synthesis for Hybrid PET-MR Scanners. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8149. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40811-3_19
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DOI: https://doi.org/10.1007/978-3-642-40811-3_19
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