New Similarity Metric for Registration of MRI to Histology: Golden Retriever Muscular Dystrophy Imaging.

OBJECTIVE: Histology is often used as a gold standard to evaluate non-invasive imaging modalities such as 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 non-orthogonal histology section orientation, section distortion, and the different image contrast and resolution.

METHODS: This study describes a 3-step procedure to register histology images with multi-parametric MRI: i.e., interactive slice localization controlled by a 3D 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 multi-parametric 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 an RMS error of 148.83 ± 34.96 μm averaged for 10 muscle samples based on landmark points validated by 5 observers. The best RMS error averaged for 10 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, fat).

SIGNIFICANCE: The proposed methodology allows creation of a database of spatially registered multi-parametric MRI and histology. This database will felicitate accurate monitoring of disease progression and assess treatment effects non-invasively.