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Chaibi, H, Nourine, R. (1396). New Pseudo-CT Generation Approach from Magnetic Resonance Imaging using a Local Texture Descriptor. سامانه مدیریت نشریات علمی, 8(1), 53-64.
H Chaibi; R Nourine. "New Pseudo-CT Generation Approach from Magnetic Resonance Imaging using a Local Texture Descriptor". سامانه مدیریت نشریات علمی, 8, 1, 1396, 53-64.
Chaibi, H, Nourine, R. (1396). 'New Pseudo-CT Generation Approach from Magnetic Resonance Imaging using a Local Texture Descriptor', سامانه مدیریت نشریات علمی, 8(1), pp. 53-64.
Chaibi, H, Nourine, R. New Pseudo-CT Generation Approach from Magnetic Resonance Imaging using a Local Texture Descriptor. سامانه مدیریت نشریات علمی, 1396; 8(1): 53-64.

New Pseudo-CT Generation Approach from Magnetic Resonance Imaging using a Local Texture Descriptor

Journal of Biomedical Physics and Engineering
مقاله 6، دوره 8، شماره 1، خرداد 2018، صفحه 53-64    اصل مقاله (1.08 M)
نوع مقاله: Original Research
نویسندگان
H Chaibi* ؛ R Nourine
Lab. LITIO, University of Oran 1 Ahmed Ben Bella- Algeria.
چکیده
Background: One of the challenges of PET/MRI combined systems is to derive an attenuation map to correct the PET image. For that, the pseudo-CT image could be used to correct the attenuation. Until now, most existing scientific researches construct this pseudo-CT image using the registration techniques. However, these techniques suffer from the local minima of the non-rigid deformation energy function which leads to unsatisfactory results.Objective: We propose in this paper a new approach for the generation of a pseudo-CT image from an MR image.Materials and Methods: This approach is based on a dense stereo matching concept, for that, we encode each pixel according to a shape related coordinates method, and we apply a local texture descriptor to put into correspondence pixels between MRI patient and MRI atlas images. The proposed approach was tested on a real MRI data, and in order to show the effectiveness of the proposed local descriptor, it has been compared to three other local descriptors: SIFT, SURF and DAISY. Also it was compared to registration method.Results: The calculation of structural similarity (SSIM) index and DICE coefficients, between the pseudo-CT image and the corresponding real CT image show that the proposed stereo matching approach outperforms a registration one.Conclusion: The use of dense matching with atlas promises good results in the creation of pseudo-CT. The proposed approach can be recommended as an alternative to registration techniques.
کلیدواژه‌ها
Pseudo-CT؛ Attenuation Correction؛ Stereo Matching؛ Local Texture Descriptor for Matching؛ PET/MRI
مراجع
  1. 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. doi.org/10.2967/jnumed.107.049353. PubMed PMID: 18927326.
  2. Rousseau F, Habas PA, Studholme C. A supervised patch-based approach for human brain labeling. IEEE Trans Med Imaging. 2011;30:1852-62. doi.org/10.1109/TMI.2011.2156806. PubMed PMID: 21606021. PubMed PMCID: 3318921.
  3. Ay MR, Akbarzadeh A, Ahmadian A, Zaidi H. Classification of bones from MR images in torso PET-MR imaging using a statistical shape model. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2014;734:196-200. doi.org/10.1016/j.nima.2013.09.007.
  4. 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. doi.org/10.2967/jnumed.109.065425. PubMed PMID: 20439508.
  5. Ribeiro AS, Kops ER, Herzog H, Almeida P. Skull segmentation of UTE MR images by probabilistic neural network for attenuation correction in PET/MR. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2013;702:114-6. doi.org/10.1016/j.nima.2012.09.005.
  6. Mollet P, Keereman V, Clementel E, Vandenberghe S. Simultaneous MR-compatible emission and transmission imaging for PET using time-of-flight information. IEEE Trans Med Imaging. 2012;31:1734-42. doi.org/10.1109/TMI.2012.2198831. PubMed PMID: 22948340.
  7. 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. doi.org/10.1109/TMI.2015.2409157. PubMed PMID: 25769148.
  8. Ribeiro AS, Kops ER, Herzog H, Almeida P. Hybrid approach for attenuation correction in PET/MR scanners. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2014;734:166-70. doi.org/10.1016/j.nima.2013.09.034.
  9. Kops ER, Herzog H, editors. Alternative methods for attenuation correction for PET images in MR-PET scanners. 2007 IEEE Nuclear Science Symposium Conference Record. IEEE: 2008.
  10. Kops ER, Herzog H, editors. Template based attenuation correction for PET in MR-PET scanners. 2008 IEEE Nuclear Science Symposium Conference Record; 2008: IEEE.
  11. 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. doi.org/10.1118/1.3377774. PubMed PMID: 20527543.
  12. 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. doi.org/10.1016/j.media.2016.02.002. PubMed PMID: 26948109.
  13. Hirsch M, Hofmann M, Mantlik F, Pichler BJ, Schölkopf B, Habeck M, editors. A blind deconvolution approach for pseudo CT prediction from MR image pairs. 2012 19th IEEE International Conference on Image Processing; 2012: IEEE.
  14. Torrado-Carvajal A, Herraiz JL, Alcain E, Montemayor AS, Garcia-Canamaque L, Hernandez-Tamames JA, et al. Fast Patch-Based Pseudo-CT Synthesis from T1-Weighted MR Images for PET/MR Attenuation Correction in Brain Studies. J Nucl Med. 2016;57:136-43. doi.org/10.2967/jnumed.115.156299. PubMed PMID: 26493204.
  15. Burgos N, Cardoso MJ, Thielemans K, Modat M, Pedemonte S, Dickson J, et al. Attenuation correction synthesis for hybrid PET-MR scanners: application to brain studies. IEEE Trans Med Imaging. 2014;33:2332-41. doi.org/10.1109/TMI.2014.2340135. PubMed PMID: 25055381.
  16. Mérida I, Costes N, Heckemann RA, Drzezga A, Förster S, Hammers A, editors. Evaluation of several multi-atlas methods for PSEUDO-CT generation in brain MRI-PET attenuation correction. 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI); 2015: IEEE.
  17. Gui Y, Su A, Du J. Point-pattern matching method using SURF and Shape Context. Optik-International Journal for Light and Electron Optics. 2013;124:1869-73. doi.org/10.1016/j.ijleo.2012.05.037.
  18. Toews M, Wells WM, Efficient and robust model-to-image alignment using 3D scale-invariant features. Med Image Anal. 2013;17:271-82. doi.org/10.1016/j.media.2012.11.002. PubMed PMID: 23265799. PubMed PMCID: 3606671.
  19. Vinay A, Hebbar D, Shekhar VS, Murthy KB, Natarajan S. Two Novel Detector-Descriptor Based Approaches for Face Recognition Using SIFT and SURF. Procedia Computer Science. 2015;70:185-97. doi.org/10.1016/j.procs.2015.10.070.
  20. Miao Q, Wang G, Shi C, Lin X, Ruan Z. A new framework for on-line object tracking based on SURF. Pattern Recognition Letters. 2011;32:1564-71. doi.org/10.1016/j.patrec.2011.05.017.
  21. Zigh E, Belbachir MF. Soft computing strategy for stereo matching of multi spectral urban very high resolution IKONOS images. Applied soft computing. 2012;12:2156-67. doi.org/10.1016/j.asoc.2012.02.014.
  22. Juntu J, Sijbers J, De Backer S, Rajan J, Van Dyck D. Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft-tissue tumors in T1-MRI images. J Magn Reson Imaging. 2010;31:680-9. doi.org/10.1002/jmri.22095. PubMed PMID: 20187212.
  23. Mayerhoefer ME, Breitenseher MJ, Kramer J, Aigner N, Hofmann S, Materka A. Texture analysis for tissue discrimination on T1-weighted MR images of the knee joint in a multicenter study: Transferability of texture features and comparison of feature selection methods and classifiers. J Magn Reson Imaging. 2005;22:674-80. doi.org/10.1002/jmri.20429. PubMed PMID: 16215966.
  24. Chaibi H, Nourine R. Skull Segmentation of MR images based on texture features for attenuation correction in PET/MR. 2nd International Conference on Signal, Image, Vision and their Applications (SIVA’13): Guelma, Algeria; 2013.
  25. Lowe DG. Distinctive image features from scale-invariant keypoints. International journal of computer vision. 2004;60:91-110. doi.org/10.1023/B:VISI.0000029664.99615.94.
  26. Valenzuela REG, Schwartz WR, Pedrini H, editors. Dimensionality reduction through PCA over SIFT and SURF descriptors. Cybernetic Intelligent Systems (CIS), 2012 IEEE 11th International Conference on; 2012.
  27. Tola E, Lepetit V, Fua P, editors. A fast local descriptor for dense matching. Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on: 2008.
  28. In: The Retrospective Image Registration Evaluation Project. The Retrospective Image Registration Evaluation Project, Version 2.0. [cited April 2013]; Available from: http://www.insight-journal.org/rire/.
  29. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004;13:600-12. doi.org/10.1109/TIP.2003.819861. PubMed PMID: 15376593.
  30. Balan AG, Traina AJ, Ribeiro MX, Marques PM, Traina C, Jr. Smart histogram analysis applied to the skull-stripping problem in T1-weighted MRI. Comput Biol Med. 2012;42:509-22. doi.org/10.1016/j.compbiomed.2012.01.004. PubMed PMID: 22336779.
  31. Poynton CB, Chen KT, Chonde DB, Izquierdo-Garcia D, Gollub RL, Gerstner ER, et al. Probabilistic atlas-based segmentation of combined T1-weighted and DUTE MRI for calculation of head attenuation maps in integrated PET/MRI scanners. Am J Nucl Med Mol Imaging. 2014;4:160-71. PubMed PMID: 24753982. PubMed PMCID: 3992209.
  32. In: SPM. Statistical Parametric Mapping. Available from: http://www.fil.ion.ucl.ac.uk/spm/.
  33. In: Elasti. A toolbox for rigid and nonrigid registration of images. Available from: http://elastix.isi.uu.nl/.
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