TY - JOUR
T1 - A computerized MRI biomarker quantification scheme for a canine model of Duchenne muscular dystrophy
AU - Wang, Jiahui
AU - Fan, Zheng
AU - Vandenborne, Krista
AU - Walter, Glenn
AU - Shiloh-Malawsky, Yael
AU - An, Hongyu
AU - Kornegay, Joe N.
AU - Styner, Martin A.
N1 - Funding Information:
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.
PY - 2013/9
Y1 - 2013/9
N2 - 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.
AB - 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.
KW - Canine model
KW - Duchenne muscular dystrophy
KW - Golden retriever muscular dystrophy
KW - MRI
KW - Segmentation
KW - Statistical texture analysis
UR - http://www.scopus.com/inward/record.url?scp=84884910403&partnerID=8YFLogxK
U2 - 10.1007/s11548-012-0810-6
DO - 10.1007/s11548-012-0810-6
M3 - Article
C2 - 23299128
AN - SCOPUS:84884910403
VL - 8
SP - 763
EP - 774
JO - International Journal of Computer Assisted Radiology and Surgery
JF - International Journal of Computer Assisted Radiology and Surgery
SN - 1861-6410
IS - 5
ER -