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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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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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