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A computerized MRI biomarker quantification scheme for a canine model of Duchenne muscular dystrophy

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose   Golden retriever muscular dystrophy (GRMD) is a widely used canine model of Duchenne muscular dystrophy (DMD). Recent studies have shown that magnetic resonance imaging (MRI) can be used to non-invasively detect consistent changes in both DMD and GRMD. In this paper, we propose a semiautomated system to quantify MRI biomarkers of GRMD.

Methods   Our system was applied to a database of 45 MRI scans from 8 normal and 10 GRMD dogs in a longitudinal natural history study. We first segmented six proximal pelvic limb muscles using a semiautomated full muscle segmentation method. We then performed preprocessing, including intensity inhomogeneity correction, spatial registration of different image sequences, intensity calibration of T2-weighted and T2-weighted fat-suppressed images, and calculation of MRI biomarker maps. Finally, for each of the segmented muscles, we automatically measured MRI biomarkers of muscle volume, intensity statistics over MRI biomarker maps, and statistical image texture features.

Results The muscle volume and the mean intensities in T2 value, fat, and water maps showed group differences between normal and GRMD dogs. For the statistical texture biomarkers, both the histogram and run-length matrix features showed obvious group differences between normal and GRMD dogs. The full muscle segmentation showed significantly less error and variability in the proposed biomarkers when compared to the standard, limited muscle range segmentation.

Conclusion   The experimental results demonstrated that this quantification tool could reliably quantify MRI biomarkers in GRMD dogs, suggesting that it would also be useful for quantifying disease progression and measuring therapeutic effect in DMD patients.

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References

  1. Moser H (1984) Duchenne muscular dystrophy: pathogenetic aspects and genetic prevention. Hum Genet 66:17–40

    Article  PubMed  CAS  Google Scholar 

  2. Hoffman EP, Brown RH Jr, Kunkel LM (1987) Dystrophin: the protein product of the Duchenne muscular dystrophy locus. Cell 51:919–928

    Article  PubMed  CAS  Google Scholar 

  3. Koenig M, Hoffman EP, Bertelson CJ, Monaco AP, Feener C, Kunkel LM (1987) Complete cloning of the Duchenne muscular dystrophy (DMD) cDNA and preliminary genomic organization of the DMD gene in normal and affected individuals. Cell 50:509–517

    Google Scholar 

  4. Bulfield G, Siller WG, Wight PA, Moore KJ (1984) X chromosome-linked muscular dystrophy (mdx) in the mouse. Proc Natl Acad Sci USA 81:1189–1192

    Article  PubMed  CAS  Google Scholar 

  5. Gaschen FP, Hoffman EP, Gorospe JR, Uhl EW, Senior DF, Cardinet GH 3rd, Pearce LK (1992) Dystrophin deficiency causes lethal muscle hypertrophy in cats. J Neurol Sci 110:149–159

    Google Scholar 

  6. Kornegay JN, Tuler SM, Miller DM, Levesque DC (1988) Muscular dystrophy in a litter of golden retriever dogs. Muscle Nerve 11:1056–1064

    Google Scholar 

  7. Cooper BJ, Winand NJ, Stedman H, Valentine BA, Hoffman EP, Kunkel LM, Scott MO, Fischbeck KH, Kornegay JN, Avery RJ, Williams JR, Schmickel RD, Sylvester JE (1988) The homologue of the Duchenne locus is defective in X-linked muscular dystrophy of dogs. Nature 334:154–156

    Google Scholar 

  8. Kornegay JN, Bogan JR, Bogan DJ, Childers MK, Li J, Nghiem P, Detwiler DA, Larsen CA, Grange RW, Bhavaraju-Sanka RK, Tou S, Keene BP, Howard JF Jr, Wang J, Fan Z, Schatzberg SJ, Styner MA, Flanigan KM, Xiao X, Hoffman EP (2012) Canine models of Duchenne muscular dystrophy and their use in therapeutic strategies. Mamm Genome 23:85–108

    Article  PubMed  CAS  Google Scholar 

  9. Kobayashi M, Nakamura A, Hasegawa D, Fujita M, Orima H, Takeda S (2009) Evaluation of dystrophic dog pathology by fat-suppressed T2-weighted imaging. Muscle Nerve 40:815–826

    Article  PubMed  Google Scholar 

  10. Thibaud JL, Monnet A, Bertoldi D, Barthelemy I, Blot S, Carlier PG (2007) Characterization of dystrophic muscle in golden retriever muscular dystrophy dogs by nuclear magnetic resonance imaging. Neuromuscul Disord 17:575–584

    Article  PubMed  Google Scholar 

  11. Yokota T, Lu QL, Partridge T, Kobayashi M, Nakamura A, Takeda S, Hoffman E (2009) Efficacy of systemic morpholino exon-skipping in Duchenne dystrophy dogs. Ann Neurol 65:667–676

    Google Scholar 

  12. Lamminen AE (1990) Magnetic resonance imaging of primary skeletal muscle diseases: patterns of distribution and severity of involvement. Br J Radiol 63:946–950

    Article  PubMed  CAS  Google Scholar 

  13. Lovitt S, Moore SL, Marden FA (2006) The use of MRI in the evaluation of myopathy. Clin Neurophysiol 117:486–495

    Article  PubMed  Google Scholar 

  14. Marden FA, Connolly AM, Siegel MJ, Rubin DA (2005) Compositional analysis of muscle in boys with Duchenne muscular dystrophy using MR imaging. Skeletal Radiol 34:140–148

    Article  PubMed  Google Scholar 

  15. Liu GC, Jong YJ, Chiang CH, Jaw TS (1993) Duchenne muscular dystrophy: MR grading system with functional correlation. Radiology 186:475–480

    Google Scholar 

  16. Karpati G, Ajdukovic D, Arnold D, Gledhill RB, Guttmann R, Holland P, Koch PA, Shoubridge E, Spence D, Vanasse M, Watters GV, Abrahamowicz M, Duff C, Worton RG (1993) Myoblast transfer in Duchenne muscular dystrophy. Ann Neurol 34: 8–17

    Google Scholar 

  17. Miller RG, Sharma KR, Pavlath GK, Gussoni E, Mynhier M, Lanctot AM, Greco CM, Steinman L, Blau HM (1997) Myoblast implantation in Duchenne muscular dystrophy: the San Francisco study. Muscle Nerve 20:469–478

    Article  PubMed  CAS  Google Scholar 

  18. Mathur S, Lott DJ, Senesac C (2010) Age-related differences in lower-limb muscle cross-sectional area and torque production in boys with Duchenne muscular dystrophy. Arch Phys Med Rehabil 91:1051–1058

    Article  PubMed  Google Scholar 

  19. Kornegay JN, Cundiff DD, Bogan DJ, Bogan JR, Okamura CS (2003) The cranial sartorius muscle undergoes true hypertrophy in dogs with golden retriever muscular dystrophy. Neuromuscul Disord 13:493–500

    Article  PubMed  Google Scholar 

  20. Childers MK, Okamura CS, Bogan DJ, Bogan JR, Sullivan MJ, Kornegay JN (2001) Myofiber injury and regeneration in a canine homologue of Duchenne muscular dystrophy. Am J Phys Med Rehabil 80:175–181

    Google Scholar 

  21. Wang J, Fan Z, Kornegay J, Styner M (2011) MRI-based quantification of Duchenne muscular dystrophy in a canine model. Proc SPIE Med Imaging 7965:79650G1–79650G9

    Google Scholar 

  22. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31:1116–1128

    Article  PubMed  Google Scholar 

  23. Hou Z (2006) A review on MR image intensity inhomogeneity correction. Proc Int J Biomed Imaging 2006:49515-1–49515-11

    Google Scholar 

  24. Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320

    Article  PubMed  Google Scholar 

  25. Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17:87–97

    Article  PubMed  CAS  Google Scholar 

  26. Kim HK, Laor T, Horn PS, Racadio JM, Wong B, Dardzinski BJ (2010) T2 mapping in Duchenne muscular dystrophy: distribution of disease activity and correlation with clinical assessments. Radiology 255:899–908

    Google Scholar 

  27. Pluim JP, Maintz JB, Viergever MA (2003) Mutual-information-based registration of medical images: a survey. IEEE Trans Med Imaging 22:986–1004

    Article  PubMed  Google Scholar 

  28. Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12: 629–639

    Google Scholar 

  29. Chuah TK, Poh CL, Sheah K (2011) Quantitative texture analysis of MRI images for detection of cartilage-related bone marrow edema. Proc IEEE Eng Med Biol Soc 2011:5112–5115

    Google Scholar 

  30. Mayerhoefer ME, Schima W, Trattnig S, Pinker K, Berger-Kulemann V, Ba-Ssalamah A (2010) Texture-based classification of focal liver lesions on MRI at 3.0 Tesla: a feasibility study in cysts and hemangiomas. J Magn Reson Imaging 32:352–359

    Article  PubMed  Google Scholar 

  31. Holli K, Laaperi AL, Harrison L, Luukkaala T, Toivonen T, Ryymin P, Dastidar P, Soimakallio S, Eskola H (2010) Characterization of breast cancer types by texture analysis of magnetic resonance images. Acad Radiol 17:135–141

    Google Scholar 

  32. Zhang J, Yu C, Jiang G, Liu W, Tong L (2012) 3D texture analysis on MRI images of Alzheimer’s disease. Brain Imaging Behav 6:61–69

    Article  PubMed  Google Scholar 

  33. Herlidou S, Rolland Y, Bansard JY, Le Rumeur E, De Certaines JD (1999) Comparison of automated and visual texture analysis in MRI: characterization of normal and diseased skeletal muscle. Magn Reson Imaging 17:1393–1397

    Google Scholar 

  34. Mahmoud-Ghoneim D, Cherel Y, Lemaire L, De Certaines JD, Maniere A (2006) Texture analysis of magnetic resonance images of rat muscles during atrophy and regeneration. Magn Reson Imaging 24:167–171

    Google Scholar 

  35. Galloway M (1975) Texture analysis using gray level run lengths. Comput Graph Imaging Proc 4:172–179

    Google Scholar 

  36. Bryk AS, Raudenbush SW (1992) Hierarchical linear models: applications and data analysis methods. Sage, Newbury Park

    Google Scholar 

  37. Tegeler C, Grange R, Bogan D, Markert C, Case D, Kornegay J, Childers M (2010) Eccentric contractions induce rapid isometric torque drop in dystrophic-deficient dogs. Muscle Nerve 42:130–132

    Google Scholar 

  38. Childers M, Bogan J, Bogan D, Greiner H, Holder M, Grange R, Kornegay J (2011) Chronic administration of a leupeptin-derived calpain inhibitor fails to ameliorate severe muscle pathology in a canine model of Duchenne muscular dystrophy. Front Pharmacol 2:89

    Google Scholar 

  39. Abramoff M, Magalhaes P, Ram S (2004) Image processing with image J. Biophoton Intern 11:36–42

    Google Scholar 

Download references

Acknowledgments

This work was supported by National Institutes of Health Grant Nos. R42 NS059095-03 (NINDS) (Styner), P30-HD003110-41 (NICHD) (Styner) and 1U24NS059696-01A1 (NINDS) (Kornegay), the Muscular Dystrophy Association (Kornegay), Wellstone center for Muscular Dystrophy Research [USPHS (NIAMS) 1U54AR056953-01], The North Carolina Translational and Clinical Sciences (NC TraCS) Institute [Tracs50K (50KR71104)] and UNC Intellectual and Developmental Disabilities Research Center. The authors thank Weili Lin and Kathleen Wilber for their help in acquisition of MRI scans and their helpful discussions, Hongtu Zhu and Mihye Ahn for their helpful discussions on the statistical analysis, and Janet and Dan Bogan, and Jennifer Dow for technical assistance in managing the dogs. Conflict of interest The authors declare that they have no conflict of interest.

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Author notes

  1. Joe N. Kornegay

    Present address: Department of Veterinary Integrative Biosciences, Texas A and M University, College Station, TX, 77843, USA

Authors and Affiliations

  1. Department of Psychiatry, University of North Carolina, Chapel Hill, NC, 27599, USA

    Jiahui Wang & Martin A. Styner

  2. Department of Neurology, University of North Carolina, Chapel Hill, NC, 27599, USA

    Zheng Fan, Yael Shiloh-Malawsky & Joe N. Kornegay

  3. Department of Physical Therapy, University of Florida, Gainesville, FL, 32610, USA

    Krista Vandenborne

  4. Department of Physiology and Functional Genomics, University of Florida, Gainesville, FL, 32610, USA

    Glenn Walter

  5. Department of Radiology, University of North Carolina, Chapel Hill, NC, 27599, USA

    Hongyu An

  6. Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, 27599, USA

    Joe N. Kornegay

  7. Department of Computer Science, University of North Carolina, Chapel Hill, NC, 27599, USA

    Martin A. Styner

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  1. Jiahui Wang
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Correspondence to Jiahui Wang.

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Wang, J., Fan, Z., Vandenborne, K. et al. A computerized MRI biomarker quantification scheme for a canine model of Duchenne muscular dystrophy. Int J CARS 8, 763–774 (2013). https://doi.org/10.1007/s11548-012-0810-6

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  • DOI: https://doi.org/10.1007/s11548-012-0810-6

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