New Similarity Metric for Registration of MRI to Histology

Golden Retriever Muscular Dystrophy Imaging

Aydin Eresen, Sharla M. Birch, Lejla Alic, Jay F. Griffin IV, Joe N. Kornegay, Jim Ji

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
JournalIEEE Transactions on Biomedical Engineering
DOIs
Publication statusAccepted/In press - 15 Sep 2018

Fingerprint

Histology
Magnetic resonance imaging
Imaging techniques
Muscle
Oils and fats
Splines
Monitoring

Keywords

  • Golden retriever muscular dystrophy
  • histology
  • image registration
  • magnetic resonance imaging
  • similarity metric

ASJC Scopus subject areas

  • Biomedical Engineering

Cite this

New Similarity Metric for Registration of MRI to Histology : Golden Retriever Muscular Dystrophy Imaging. / Eresen, Aydin; Birch, Sharla M.; Alic, Lejla; Griffin IV, Jay F.; Kornegay, Joe N.; Ji, Jim.

In: IEEE Transactions on Biomedical Engineering, 15.09.2018.

Research output: Contribution to journalArticle

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N2 - 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.

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