nach oben

European Journal of Nuclear Medicine and Molecular Imaging

Erschienen in:

03.06.2016 | Original Article

One registration multi-atlas-based pseudo-CT generation for attenuation correction in PET/MRI

verfasst von: Hossein Arabi, Habib Zaidi

Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging | Ausgabe 11/2016

Einloggen, um Zugang zu erhalten

Abstract

Purpose

The outcome of a detailed assessment of various strategies for atlas-based whole-body bone segmentation from magnetic resonance imaging (MRI) was exploited to select the optimal parameters and setting, with the aim of proposing a novel one-registration multi-atlas (ORMA) pseudo-CT generation approach.

Methods

The proposed approach consists of only one online registration between the target and reference images, regardless of the number of atlas images (N), while for the remaining atlas images, the pre-computed transformation matrices to the reference image are used to align them to the target image. The performance characteristics of the proposed method were evaluated and compared with conventional atlas-based attenuation map generation strategies (direct registration of the entire atlas images followed by voxel-wise weighting (VWW) and arithmetic averaging atlas fusion). To this end, four different positron emission tomography (PET) attenuation maps were generated via arithmetic averaging and VWW scheme using both direct registration and ORMA approaches as well as the 3-class attenuation map obtained from the Philips Ingenuity TF PET/MRI scanner commonly used in the clinical setting. The evaluation was performed based on the accuracy of extracted whole-body bones by the different attenuation maps and by quantitative analysis of resulting PET images compared to CT-based attenuation-corrected PET images serving as reference.

Results

The comparison of validation metrics regarding the accuracy of extracted bone using the different techniques demonstrated the superiority of the VWW atlas fusion algorithm achieving a Dice similarity measure of 0.82 ± 0.04 compared to arithmetic averaging atlas fusion (0.60 ± 0.02), which uses conventional direct registration. Application of the ORMA approach modestly compromised the accuracy, yielding a Dice similarity measure of 0.76 ± 0.05 for ORMA-VWW and 0.55 ± 0.03 for ORMA-averaging. The results of quantitative PET analysis followed the same trend with less significant differences in terms of SUV bias, whereas massive improvements were observed compared to PET images corrected for attenuation using the 3-class attenuation map. The maximum absolute bias achieved by VWW and VWW-ORMA methods was 06.4 ± 5.5 in the lung and 07.9 ± 4.8 in the bone, respectively.

Conclusions

The proposed algorithm is capable of generating decent attenuation maps. The quantitative analysis revealed a good correlation between PET images corrected for attenuation using the proposed pseudo-CT generation approach and the corresponding CT images. The computational time is reduced by a factor of 1/N at the expense of a modest decrease in quantitative accuracy, thus allowing us to achieve a reasonable compromise between computing time and quantitative performance.

Nur mit Berechtigung zugänglich

Judenhofer MS, Wehrl HF, Newport DF, Catana C, Siegel SB, Becker M, et al. Simultaneous PET-MRI: a new approach for functional and morphological imaging. Nat Med. 2008;14:459–65.CrossRefPubMed

Zaidi H, Del Guerra A. An outlook on future design of hybrid PET/MRI systems. Med Phys. 2011;38:5667–89.CrossRefPubMed

Wiesmuller M, Quick HH, Navalpakkam B, Lell MM, Uder M, Ritt P, et al. Comparison of lesion detection and quantitation of tracer uptake between PET from a simultaneously acquiring whole-body PET/MR hybrid scanner and PET from PET/CT. Eur J Nucl Med Mol Imaging. 2013;40:12–21.CrossRefPubMed

Becker M, Zaidi H. Imaging in head and neck squamous cell carcinoma: the potential role of PET/MRI. Br J Radiol. 2014;87:20130677.

Spick C, Herrmann K, Czernin J. 18F-FDG PET/CT and PET/MRI perform equally well in cancer patients: evidence from studies in more than 2300 patients. J Nucl Med. 2016;57:420–30.CrossRefPubMed

Bezrukov I, Mantlik F, Schmidt H, Scholkopf B, Pichler BJ. MR-based PET attenuation correction for PET/MR imaging. Semin Nucl Med. 2013;43:45–59.CrossRefPubMed

Mehranian A, Arabi H, Zaidi H. Vision 20/20: magnetic resonance imaging-guided attenuation correction in PET/MRI: challenges, solutions, and opportunities. Med Phys. 2016;43:1130–55.CrossRefPubMed

Zaidi H, Montandon M-L, Slosman DO. Magnetic resonance imaging-guided attenuation and scatter corrections in three-dimensional brain positron emission tomography. Med Phys. 2003;30:937–48.CrossRefPubMed

Martinez-Moller A, Souvatzoglou M, Delso G, Bundschuh RA, Chefd’hotel C, Ziegler SI, et al. Tissue classification as a potential approach for attenuation correction in whole-body PET/MRI: evaluation with PET/CT data. J Nucl Med. 2009;50:520–6.CrossRefPubMed

10.

Schulz V, Torres-Espallardo I, Renisch S, Hu Z, Ojha N, Börnert P, et al. Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data. Eur J Nucl Med Mol Imaging. 2011;38:138–52.CrossRefPubMed

11.

Montandon M-L, Zaidi H. Atlas-guided non-uniform attenuation correction in cerebral 3D PET imaging. Neuroimage. 2005;25:278–86.CrossRefPubMed

12.

Hofmann M, Bezrukov I, Mantlik F, Aschoff P, Steinke F, Beyer T, et al. MRI-based attenuation correction for whole-body PET/MRI: quantitative evaluation of segmentation- and Atlas-based methods. J Nucl Med. 2011;52:1392–9.CrossRefPubMed

13.

Arabi H, Zaidi H. Magnetic resonance imaging-guided attenuation correction in whole-body PET/MRI using a sorted atlas approach. Med Image Anal. 2016;31:1–15.CrossRefPubMed

14.

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. J Nucl Med. 2010;51:812–8.CrossRefPubMed

15.

Berker Y, Franke J, Salomon A, Palmowski M, Donker HC, Temur Y, et al. 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. 2012;53:796–804.CrossRefPubMed

16.

Delso G, Wiesinger F, Sacolick L, Kaushik S, Shanbhag D, Hullner M, et al. Clinical evaluation of zero echo time MRI for the segmentation of the skull. J Nucl Med. 2015;56:417–22.CrossRefPubMed

17.

Rezaei A, Defrise M, Bal G, Michel C, Conti M, Watson C, et al. Simultaneous reconstruction of activity and attenuation in time-of-flight PET. IEEE Trans Med Imag. 2012;31:2224–33.

18.

Mehranian A, Zaidi H. Joint estimation of activity and attenuation in whole-body TOF PET/MRI using constrained Gaussian mixture models. IEEE Trans Med Imaging. 2015;34:1808–21.CrossRefPubMed

19.

Izquierdo-Garcia D, Hansen AE, Forster S, Benoit D, Schachoff S, Furst S, et al. An SPM8-based approach for attenuation correction combining segmentation and nonrigid template formation: application to simultaneous PET/MR brain imaging. J Nucl Med. 2014;55:1825–30.CrossRefPubMedPubMedCentral

20.

Schreibmann E, Nye JA, Schuster DM, Martin DR, Votaw J, Fox T. MR-based attenuation correction for hybrid PET-MR brain imaging systems using deformable image registration. Med Phys. 2010;37:2101–9.CrossRefPubMed

21.

Fei B, Yang X, Nye JA, Aarsvold JN, Raghunath N, Cervo M, et al. MRPET quantification tools: registration, segmentation, classification, and MR-based attenuation correction. Med Phys. 2012;39:6443–54.CrossRefPubMedPubMedCentral

22.

Hofmann M, Steinke F, Scheel V, Charpiat G, Farquhar J, Aschoff P, et al. MRI-based attenuation correction for PET/MRI: a novel approach combining pattern recognition and Atlas registration. J Nucl Med. 2008;49:1875–83.CrossRefPubMed

23.

Malone IB, Ansorge RE, Williams GB, Nestor PJ, Carpenter TA, Fryer TD. Attenuation correction methods suitable for brain imaging with a PET/MRI scanner: a comparison of tissue atlas and template attenuation map approaches. J Nucl Med. 2011;52:1142–9.CrossRefPubMed

24.

Burgos N, Cardoso M, Thielemans K, Modat M, Schott J, Duncan J, et al. Attenuation correction synthesis for hybrid PET-MR scanners: application to brain studies. IEEE Trans Med Imaging. 2014;33:2332–41.CrossRefPubMed

25.

Sjölund J, Forsberg D, Andersson M, Knutsson H. Generating patient specific pseudo-CT of the head from MR using atlas-based regression. Phys Med Biol. 2015;60:825–39.CrossRefPubMed

26.

Mehranian A, Arabi H, Zaidi H. Quantitative analysis of MRI-guided attenuation correction techniques in time-of-flight brain PET/MRI. Neuroimage. 2016;130:123–33.CrossRefPubMed

27.

Bezrukov I, Schmidt H, Mantlik F, Schwenzer N, Brendle C, Scholkopf B, et al. MR-based attenuation correction methods for improved PET quantification in lesions within bone and susceptibility artifact regions. J Nucl Med. 2013;54:1768–74.CrossRefPubMed

28.

Hermosillo G, Raykar V, Zhou X. Learning to locate cortical bone in MRI. In: Wang F, Shen D, Yan P, Suzuki K, editors. Machine learning in medical imaging. Berlin: Springer; 2012. p. 168–75.

29.

Marshall HR, Patrick J, Laidley D, Prato FS, Butler J, Theberge J, et al. Description and assessment of a registration-based approach to include bones for attenuation correction of whole-body PET/MRI. Med Phys. 2013;40:082509

30.

Uh J, Merchant TE, Li Y, Li X, Hua C. MRI-based treatment planning with pseudo CT generated through atlas registration. Med Phys. 2014;41:051711–8.CrossRefPubMed

31.

Arabi H, Zaidi H. Comparison of atlas-based bone segmentation methods in whole-body PET/MRI. IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). Seattle, WA, USA; 2014.

32.

Zaidi H, Ojha N, Morich M, Griesmer J, Hu Z, Maniawski P, et al. Design and performance evaluation of a whole-body Ingenuity TF PET-MRI system. Phys Med Biol. 2011;56:3091–106.CrossRefPubMedPubMedCentral

33.

Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW. elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging. 2010;29:196–205.CrossRefPubMed

34.

Arabi H, Rager O, Alem A, Varoquaux A, Becker M, Zaidi H. Clinical assessment of MR-guided 3-class and 4-class attenuation correction in PET/MR. Mol Imaging Biol. 2015;17:264–76.CrossRefPubMed

35.

Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Analy Machine Intell. 1990;12:629–39.CrossRef

36.

Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging. 2010;29:1310–20.CrossRefPubMedPubMedCentral

37.

McAuliffe MJ, Lalonde FM, McGarry D, Gandler W, Csaky K, Trus BL. Medical Image Processing, analysis and visualization in clinical research. Proceedings 14th IEEE Symposium on Computer-Based Medical Systems, CBMS. 2001. pp. 381–6.

38.

Mattes D, Haynor DR, Vesselle H, Lewellen TK, Eubank W. PET-CT image registration in the chest using free-form deformations. IEEE Trans Med Imaging. 2003;22:120–8.CrossRefPubMed

39.

Park H, Bland PH, Hero 3rd AO, Meyer CR. Least biased target selection in probabilistic atlas construction. Med Image Comput Comput Assist Interv. 2005;8:419–26.PubMed

40.

Bookstein FL. Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans Pattern Analy Machine Intell. 1989;11:567–85.CrossRef

41.

Young FW, Hamer RM. Multidimensional scaling: history, theory, and applications. New York: Taylor & Francis; 1987.

42.

Artaechevarria X, Munoz-Barrutia A, Ortiz-de-Solorzano C. Combination strategies in multi-atlas image segmentation: application to brain MR data. IEEE Trans Med Imaging. 2009;28:1266–77.CrossRefPubMed

43.

Burgos N, Cardoso MJ, Modat M, Pedemonte S, Dickson J, Barnes A, et al. Attenuation correction synthesis for hybrid PET-MR scanners. Med Image Comput Comput Assist Interv. 2013;16:147–54.PubMed

44.

Yushkevich PA, Wang H, Pluta J, Das SR, Craige C, Avants BB, et al. Nearly automatic segmentation of hippocampal subfields in in vivo focal T2-weighted MRI. Neuroimage. 2010;53:1208–24.CrossRefPubMedPubMedCentral

45.

Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26:297–302.CrossRef

46.

Xia Y, Fripp J, Chandra SS, Schwarz R, Engstrom C, Crozier S. Automated bone segmentation from large field of view 3D MR images of the hip joint. Phys Med Biol. 2013;58:7375–90.CrossRefPubMed

47.

Heckemann RA, Hajnal JV, Aljabar P, Rueckert D, Hammers A. Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage. 2006;33:115–26.CrossRefPubMed

48.

Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage. 2006;31:1116–28.CrossRefPubMed

49.

Lötjönen JMP, Wolz R, Koikkalainen JR, Thurfjell L, Waldemar G, Soininen H, et al. Fast and robust multi-atlas segmentation of brain magnetic resonance images. Neuroimage. 2010;49:2352–65.CrossRefPubMed

50.

Rohlfing T, Brandt R, Menzel R, Maurer Jr CR. Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. Neuroimage. 2004;21:1428–42.CrossRefPubMed

Titel: One registration multi-atlas-based pseudo-CT generation for attenuation correction in PET/MRI
verfasst von: Hossein Arabi
Habib Zaidi
Publikationsdatum: 03.06.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging / Ausgabe 11/2016
Print ISSN: 1619-7070
Elektronische ISSN: 1619-7089
DOI: https://doi.org/10.1007/s00259-016-3422-5

Springer Medizin

Abstract

Purpose

Methods

Results

Conclusions

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 11/2016

Cerebrospinal fluid lactate levels and brain [18F]FDG PET hypometabolism within the default mode network in Alzheimer’s disease

Ik-Kyung Jang (Ed): Cardiovascular OCT Imaging

A PET/CT approach to spinal cord metabolism in amyotrophic lateral sclerosis

Comparison of RECIST, EORTC criteria and PERCIST for evaluation of early response to chemotherapy in patients with non-small-cell lung cancer

Recommended administered activities for 68Ga-labelled peptides in paediatric nuclear medicine

Current knowledge on the sensitivity of the 68Ga-somatostatin receptor positron emission tomography and the SUVmax reference range for management of pancreatic neuroendocrine tumours