New Similarity Metric for Registration of MRI to Histology: Golden Retriever Muscular Dystrophy Imaging Academic Article uri icon

abstract

  • OBJECTIVE: Histology is often used as a gold standard to evaluate noninvasive imaging modalities such as a magnetic resonance imaging (MRI). Spatial correspondence between histology and MRI is a critical step in quantitative evaluation of skeletal muscle in golden retriever muscular dystrophy (GRMD). Registration becomes technically challenging due to nonorthogonal histology section orientation, section distortion, and the different image contrast and resolution. METHODS: This study describes a three-step procedure to register histology images with multiparametric MRI, i.e., interactive slice localization controlled by a three-dimensional mouse, followed by an affine transformation refinement, and a B-spline deformable registration using a new similarity metric. This metric combines mutual information and gradient information. RESULTS: The methodology was verified using ex vivo high-resolution multiparametric MRI with a resolution of 117.19 μm (i.e., T1-weighted and T2-weighted MRI images) and trichrome stained histology images acquired from the pectineus muscles of ten dogs (nine GRMD and one healthy control). The proposed registration method yielded a root mean squares (RMS) error of 148.83 ± 34.96 μm averaged for ten muscle samples based on landmark points validated by five observers. The best RMS error averaged for ten muscles, was 128.48 ± 25.39 μm. CONCLUSION: The established correspondence between histology and in vivo MRI enables accurate extraction of MRI characteristics for histologically confirmed regions (e.g., muscle, fibrosis, and fat). SIGNIFICANCE: The proposed methodology allows creation of a database of spatially registered multiparametric MRI and histology. This database will facilitate accurate monitoring of disease progression and assess treatment effects noninvasively.

altmetric score

  • 1

author list (cited authors)

  • Eresen, A., Birch, S. M., Alic, L., Griffin, J. F., Kornegay, J. N., & Ji, J. X.

citation count

  • 4

publication date

  • September 2018