2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation

Image-guided surgery (IGS) can reduce the risk of tissue damage and improve the accuracy and targeting of lesions by increasing the surgery’s visual field. Three-dimensional (3D) medical images can provide spatial location information to determine the location of lesions and plan the operation process. For real-time tracking and adjusting the spatial position of surgical instruments, two-dimensional (2D) images provide real-time intraoperative information. In this experiment, 2D/3D medical image registration algorithm based on the gray level is studied, and the registration based on normalized cross-correlation is realized. The Gaussian Laplacian second-order differential operator is introduced as a new similarity measure to increase edge information and internal detail information to solve single information and small convergence regions of the normalized cross-correlation algorithm. The multiresolution strategy improves the registration accuracy and efficiency to solve the low efficiency of the normalized cross-correlation algorithm.

The Dog Model in the Spotlight: Legacy of a Trustful Cooperation

Dogs have long been used as a biomedical model system and in particular as a preclinical proof of concept for innovative therapies before translation to humans. A recent example of the utility of this animal model is the promising myotubularin gene delivery in boys affected by X-linked centronuclear myopathy after successful systemic, long-term efficient gene therapy in Labrador retrievers. Mostly, this is due to unique features that make dogs an optimal system. The continuous emergence of spontaneous inherited disorders enables the identification of reliable complementary molecular models for human neuromuscular disorders (NMDs). Dogs’ characteristics including size, lifespan and unprecedented medical care level allow a comprehensive longitudinal description of diseases. Moreover, the highly similar pathogenic mechanisms with human patients yield to translational robustness. Finally, interindividual phenotypic heterogeneity between dogs helps identifying modifiers and anticipates precision medicine issues.

This review article summarizes the present list of molecularly characterized dog models for NMDs and provides an exhaustive list of the clinical and paraclinical assays that have been developed. This toolbox offers scientists a sensitive and reliable system to thoroughly evaluate neuromuscular function, as well as efficiency and safety of innovative therapies targeting these NMDs. This review also contextualizes the model by highlighting its unique genetic value, shaped by the long-term coevolution of humans and domesticated dogs. Because the dog is one of the most protected research animal models, there is considerable opposition to include it in preclinical projects, posing a threat to the use of this model. We thus discuss ethical issues, emphasizing that unlike many other models, the dog also benefits from its contribution to comparative biomedical research with a drastic reduction in the prevalence of morbid alleles in the breeding stock and an improvement in medical care.

Muscle percentage index as a marker of disease severity in golden retriever muscular dystrophy

INTRODUCTION

Golden retriever muscular dystrophy (GRMD) is a spontaneous X-linked canine model of Duchenne muscular dystrophy that resembles the human condition. Muscle percentage index (MPI) is proposed as an imaging biomarker of disease severity in GRMD.

METHODS

To assess MPI, we used MRI data acquired from nine GRMD samples using a 4.7 T small-bore scanner. A machine learning approach was used with eight raw quantitative mapping of MRI data images (T1m, T2m, two Dixon maps, and four diffusion tensor imaging maps), three types of texture descriptors (local binary pattern, gray-level co-occurrence matrix, gray-level run-length matrix), and a gradient descriptor (histogram of oriented gradients).

RESULTS

The confusion matrix, averaged over all samples, showed 93.5% of muscle pixels classified correctly. The classification, optimized in a leave-one-out cross-validation, provided an average accuracy of 80% with a discrepancy in overestimation for young (8%) and old (20%) dogs.

DISCUSSION

MPI could be useful for quantifying GRMD severity, but careful interpretation is needed for severe cases.

Texture as an imaging biomarker for disease severity in golden retriever muscular dystrophy

INTRODUCTION

Golden retriever muscular dystrophy (GRMD), an X-linked recessive disorder, causes similar phenotypic features to Duchenne muscular dystrophy (DMD). There is currently a need for a quantitative and reproducible monitoring of disease progression for GRMD and DMD.

METHODS

To assess severity in the GRMD, we analyzed texture features extracted from multi-parametric MRI (T1w, T2w, T1m, T2m, and Dixon images) using 5 feature extraction methods and classified using support vector machines.

RESULTS

A single feature from qualitative images can provide 89% maximal accuracy. Furthermore, 2 features from T1w, T2m, or Dixon images provided highest accuracy. When considering a tradeoff between scan-time and computational complexity, T2m images provided good accuracy at a lower acquisition and processing time and effort.

CONCLUSIONS

The combination of MRI texture features improved the classification accuracy for assessment of disease progression in GRMD with evaluation of the heterogenous nature of skeletal muscles as reflection of the histopathological changes. Muscle Nerve 59:380-386, 2019.