Attenuation correction synthesis for hybrid PET-MR scanners: Application to brain studies

IEEE Transactions on Medical Imaging

Volumn 33, Issue 12, 2014, Pages 2332-2341

  Burgos, Ninon ^a   Cardoso, M Jorge ^a   Thielemans, Kris ^b   Modat, Marc ^a   Pedemonte, Stefano ^a,c,d   Dickson, John ^b   Barnes, Anna ^b   Ahmed, Rebekah ^a   Mahoney, Colin J ^a   Schott, Jonathan M ^a   Duncan, John S ^a   Atkinson, David ^a   Arridge, Simon R ^a   Hutton, Brian F ^b,e   Ourselin, Sebastien ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

^c MASSACHUSETTS GENERAL HOSPITAL   (United States)

^d AALTO UNIVERSITY   (Finland)

^e UNIVERSITY OF WOLLONGONG   (Australia)

Author keywords

Attenuation correction; image synthesis; positron emission tomography magnetic resonance (PET MR)

Indexed keywords

ALGORITHM; ANATOMY AND HISTOLOGY; BRAIN; HUMAN; MULTIMODAL IMAGING; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; POSITRON EMISSION TOMOGRAPHY; PROCEDURES; SCINTISCANNING;

ALGORITHMS; BRAIN; HUMANS; MAGNETIC RESONANCE IMAGING; MULTIMODAL IMAGING; NEUROIMAGING; POSITRON-EMISSION TOMOGRAPHY;

EID: 84914112740     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2014.2340135     Document Type: Article

References (38)

1. G. K. von Schulthess, F. P. Kuhn, P. Kaufmann, and P. Veit-Haibach, "Clinical positron emission tomography/magnetic resonance imaging applications," Seminars Nucl. Med., vol. 43, no. 1, pp. 3-10, 2013.
2. I. Bezrukov, F. Mantlik, H. Schmidt, B. Schölkopf, and B. J. Pichler, "MR-based PET attenuation correction for PET/MR imaging," Seminars Nucl. Med., vol. 43, no. 1, pp. 45-59, 2013.
3. A. Salomon, A. Goedicke, B. Schweizer, T. Aach, and V. Schulz, "Simultaneous reconstruction of activity and attenuation for PET/MR," IEEE Trans. Med. Imag., vol. 30, no. 3, pp. 804-813, Mar. 2011.
4. R. Boellaard,M. Hofman, O. Hoekstra, and A. Lammertsma, "Accurate PET/MR quantification using time of flight MLAA image reconstruction," Mol. Imag. Biol., pp. 1-9, 2014.
5. A. Rezaei, M. Defrise, G. Bal, C. Michel, M. Conti, C. Watson, and J. Nuyts, "Simultaneous reconstruction of activity and attenuation in time-of-flight PET," IEEE Trans. Med. Imag., vol. 31, no. 12, pp. 2224-2233, Dec. 2012.
6. A. Martinez-Möller,M. Souvatzoglou, G. Delso, R. A. Bundschuh, C. Chefd'hotel, S. I. Ziegler, N. Navab, M. Schwaiger, and S. G. Nekolla, "Tissue classification as a potential approach for attenuation correction in whole-body PET/MRI: evaluation with PET/CT data," J. Nucl. Med., vol. 50, no. 4, pp. 520-526, 2009.
7. P. J. Schleyer, T. Schaeffter, and P. K. Marsden, "The effect of inaccurate bone attenuation coefficient and segmentation on reconstructed PET images," Nucl. Med. Commun., vol. 31, no. 8, pp. 708-716, 2010.
8. C. Catana, A. van der Kouwe, T. Benner, C. J. Michel, M. Hamm, M. Fenchel, B. Fischl, B. Rosen, M. Schmand, and A. G. Sorensen, "Toward implementing an MRI-based PET attenuation-correction method for neurologic studies on the MR-PET brain prototype," J. Nucl. Med., vol. 51, no. 9, pp. 1431-1438, 2010.
9. V. Keereman, Y. Fierens, T. Broux, Y. De Deene, M. Lonneux, and S. Vandenberghe, "MRI-based attenuation correction for PET/MRI using ultrashort echo time sequences," J. Nucl. Med., vol. 51, no. 5, pp. 812-818, 2010.
10. A. P. Aitken, D. Giese, C. Tsoumpas, P. Schleyer, S. Kozerke, C. Prieto, and T. Schaeffter, "Improved UTE-based attenuation correction for cranial PET-MR using dynamic magnetic field monitoring," Med. Phys., vol. 41, no. 1, p. 012302, 2014.
11. Y. Berker, J. Franke, A. Salomon, M. Palmowski, H. C. W. Donker, Y. Temur, F. M. Mottaghy, C. Kuhl, D. Izquierdo-Garcia, Z. A. Fayad, F. Kiessling, andV. Schulz, "MRI-based attenuation correction for hybrid PET/MRI systems: A 4-class tissue segmentation technique using a combined ultrashort-echo-time/Dixon MRI sequence," J. Nucl. Med., vol. 53, no. 5, pp. 796-804, 2012.
12. X. Yang and B. Fei, "Multiscale segmentation of the skull in MR images for MRI-based attenuation correction of combined MR/PET," J. Am. Med. Informat. Assoc., pp. 1-9, 2013.
13. E. Schreibmann, J. A. Nye, D. M. Schuster, D. R. Martin, J. Votaw, and T. Fox, "MR-based attenuation correction for hybrid PET-MR brain imaging systems using deformable image registration," Med. Phys., vol. 37, no. 5, p. 2101, 2010.
14. A. Johansson, M. Karlsson, and T. Nyholm, "CT substitute derived from MRI sequences with ultrashort echo time," Med. Phys., vol. 38, no. 5, p. 2708, 2011.
15. A. Johansson, A. Garpebring, M. Karlsson, T. Asklund, and T. Nyholm, "Improved quality of computed tomography substitute derived from magnetic resonance (MR) data by incorporation of spatial information- potential application forMR-only radiotherapy and attenuation correction in positron emission tomography," ActaOncologica, vol. 52, no. 7, pp. 1369-1373, 2013.
16. M. Hofmann, F. Steinke, V. Scheel, G. Charpiat, J. Farquhar, P. Aschoff, M. Brady, B. Schölkopf, and B. J. Pichler, "MRI-based attenuation correction for PET/MRI: A novel approach combining pattern recognition and atlas registration," J. Nucl. Med., vol. 49, no. 11, pp. 1875-1883, 2008.
17. B. K. Navalpakkam, H. Braun, T. Kuwert, and H. H. Quick, "Magnetic resonance-based attenuation correction for PET/MR hybrid imaging using continuous valued attenuation maps," Invest. Radiol., vol. 48, no. 5, pp. 323-332, 2013.
18. H. R. Marshall, J. Patrick, D. Laidley, F. S. Prato, J. Butler, J. Théberge, R. T. Thompson, and R. Z. Stodilka, "Description and assessment of a registration-based approach to include bones for attenuation correction of whole-body PET/MRI,"Med. Phys., vol. 40, no. 8, p. 082509, 2013.
19. F. L.Andersen, C. N. Ladefoged, T. Beyer, S.H. Keller,A. E.Hansen, L. Højgaard, A. Kjær, I. Law, and S. Holm, "Combined PET/MR imaging in neurology: MR-based attenuation correction implies a strong spatial bias when ignoring bone," NeuroImage, vol. 84, pp. 206-216, 2014.
20. J. C. Dickson, C. O'Meara, and A. Barnes, "A comparison of CT- and MR-based attenuation correction in neurological PET," Eur. J. Nucl. Med. Mol. Imag., 2014.
21. R. A. Heckemann, J. V. Hajnal, P. Aljabar, D. Rueckert, and A. Hammers, "Automatic anatomical brainMRI segmentation combining label propagation and decision fusion," NeuroImage, vol. 33, no. 1, pp. 115-126, 2006.
22. M. R. Sabuncu, K. VanLeemput, B. Fischl, and P. Golland, "A generative model for image segmentation based on label fusion," IEEE Trans. Med. Imag., vol. 29, no. 10, pp. 1714-1729, Oct. 2010.
23. N. Burgos, M. Cardoso, M. Modat, S. Pedemonte, J. Dickson, A. Barnes, J. S. Duncan, D. Atkinson, S. R. Arridge, B. F. Hutton, and S. Ourselin, "Attenuation correction synthesis for hybrid PET-MR scanners," in Proc. MICCAI, 2013, pp. 147-154.
24. J.G. Sled, A. P.Zijdenbos, and A. C. Evans, "Anonparametric method for automatic correction of intensity nonuniformity in MRI data," IEEE Trans. Med. Imag., vol. 17, no. 1, pp. 87-97, Feb. 1998.
25. M. Modat, D. M. Cash, P. Daga, G. P. Winston, J. S. Duncan, and S. Ourselin, "A symmetric block-matching framework for global registration," in Proc. SPIE Med. Imag., 2014, vol. 9034, p. 90341D.
26. S. Ourselin, A. Roche, and G. Subsol, "Reconstructing a 3D structure from serial histological sections," Image Vis. Comput., vol. 19, no. 2000, pp. 25-31, 2001.
27. M. Modat, G. R. Ridgway, Z. A. Taylor, M. Lehmann, J. Barnes, D. J. Hawkes, N. C. Fox, and S. Ourselin, "Fast free-formdeformation using graphics processing units," Comput. Methods Programs Biomed., vol. 98, no. 3, pp. 278-284, 2010.
28. P. Cachier, E. Bardinet, D. Dormont, X. Pennec, and N. Ayache, "Iconic feature based nonrigid registration: The PASHA algorithm," Comput. Vis. Image Understand., vol. 89, no. 2-3, pp. 272-298, 2003.
29. P. A. Yushkevich, H.Wang, J. Pluta, S. R. Das, C. Craige, B. B. Avants, M.W.Weiner, and S.Mueller, "Nearly automatic segmentation of hippocampal subfields in in vivo focal T2-weighted MRI," NeuroImage, vol. 53, no. 4, pp. 1208-24, 2010.
30. M. Cardoso, R. Wolz, M. Modat, N. C. Fox, D. Rueckert, and S. Ourselin, "Geodesic information flows," in Proc. MICCAI, 2012, vol. 7511, pp. 262-270.
31. C. Burger, G. Goerres, S. Schoenes, A. Buck, A. H. R. Lonn, and G. K. Von Schulthess, "PET attenuation coefficients from CT images: Experimental evaluation of the transformation of CT into PET 511-keV attenuation coefficients," Eur. J. Nucl. Med. Mol. Imag., vol. 29, no. 7, pp. 922-927, 2002.
32. M. R. Ay, M. Shirmohammad, S. Sarkar, A. Rahmim, and H. Zaidi, "Comparative assessment of energy-mapping approaches in CT-based attenuation correction for PET," Mol. Imag. Biol., vol. 13, no. 1, pp. 187-198, 2011.
33. C. Watson, "New, faster, image-based scatter correctionfor 3D PET," IEEE Trans. Nucl. Sci., vol. 47, no. 4, pp. 1587-1594, Aug. 2000.
34. K. Thielemans, C. Tsoumpas, S. Mustafovic, T. Beisel, P. Aguiar, N. Dikaios, andM.W. Jacobson, "STIR: Software for tomographic image reconstruction release 2," Phys. Med. Biol., vol. 57, no. 4, pp. 867-83, 2012.
35. S. Minoshima, K. A. Frey, N. L. Foster, and D. E. Kuhl, "Preserved pontine glucose metabolism in alzheimer disease a reference region for functional brain image (PET) analysis," J. Comput. Assist. Tomogr., vol. 19, no. 4, pp. 541-547, 1995.
36. E.M.Lee,G.Y. Park,K. C. Im, S.T.Kim,C.-W.Woo, J. H.Chung,K. S. Kim, J. S. Kim, Y.-M. Shon, Y. I. Kim, and J. K. Kang, "Changes in glucose metabolism and metabolites during the epileptogenic process in the lithium-pilocarpine model of epilepsy," Epilepsia, vol. 53, no. 5, pp. 860-869, 2012.
37. M. J. Cardoso, M. J. Clarkson, G. R. Ridgway, M. Modat, N. C. Fox, and S. Ourselin, "LoAd: A locally adaptive cortical segmentation algorithm," NeuroImage, vol. 56, no. 3, pp. 1386-1397, 2011.
38. T. Rohlfing, R. Brandt, J.Maurer, C. R. , and R.Menzel, "Bee brains, B-splines and computational democracy: Generating an average shape atlas," in Proc. IEEE Workshop Math. Methods Biomed. Image Anal., 2001, pp. 187-194