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 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.

Correlating 2D histological slice with 3D MRI image volume using smart phone as an interactive tool for muscle study.

In muscle dystrophy studies, registration of histological image with MRI image volume enables cross validation of MRI biomarkers using pathological result. However, correlation of 2D histology slice with 3D MRI volume is technically challenging due to the potentially non-orthogonal slice plane and incomplete or distorted histological slice. This paper presents an efficient method to directly perform the 2D-3D registration. The method is unique in that it uses smart phone as a navigation tool for initial alignment followed by an overlap invariant mutual information-based refinement. Experimental results using animal muscle samples images from a 3T MRI and HE stained histological images show that the proposed method is capable of aligning the histological slice with an oblique slice in MR volume.

Parameter estimation in arterial spin labeling MRI: comparing the Four Phase model and the Buxton model with Fourier transform.

This paper presents a comparison between two algorithms that analyze and extract brain perfusion parameters from pulsed arterial spin labeling (ASL). One algorithm is based on the Four Phase Single Capillary Stepwise (FPSCS) model, which divides the time course of the signal difference between the control and labeled image into four phases. The other algorithm utilizes the Buxton model and Fourier transformation (FTB). Both algorithms are implemented on MATLAB to extract the bolus arrival time (BAT) and the cerebral blood flow (CBF). Current results show that the FTB algorithm has similar estimations of the BAT and CBF compared to the FPSCS model with generally faster processing speeds.

Further analysis of interpolation effects in mutual information-based image registration.

This paper presents an analysis of the mutual information (MI) metric in rigid-body registration of two digital images, in particular, local fluctuations of the MI value due to interpolation. In contrast to existing work in this area, this paper starts with two hypothetical continuous images, based on which both sampling and interpolation effects are analyzed. This analysis indicates that an "ideal" interpolator may not be able to completely suppress the undesirable local minima of the MI metric if the sampling effect is not negligible. Several preprocessing methods are discussed for reducing the interpolation effects.