A Nature-Inspired Search Space Reduction Technique for Spine Identification on Ultrasound Samples of Spina Bifida Cases.

Spina bifida is a birth defect caused by incomplete closing around the spinal cord. Spina bifida is diagnosed in a number of different ways. One approach involves searching for a deformity in the spinal axis via ultrasound. Although easy to apply, this approach requires a highly trained clinician to locate the abnormality due to the noise and distortion present in prenatal ultrasound images. Accordingly, visual examination of ultrasound images may be error prone and subjective. A computerized support system that would automatically detect the location of the spinal deformity may be helpful to the clinician in the diagnostic process. Such a software system first and foremost would require an algorithm for the identification of the entire (healthy or unhealthy) spine in the ultrasound image. This paper introduces a novel flocking dynamics based approach for reducing the size of the search space in the spine identification problem. Proposed approach accepts bone-like blobs on the ultrasound images as bird flocks and combine them into bone groups by calculating the migration path of each flock. Presented results reveal that the method is able to locate correct bones to be grouped together and reduce search space (i.e. number of bones) up to 68%.

Estimation of depth-dependent material properties of biphasic soft tissues through finite element optimization and sensitivity analysis.

A knowledge of material properties of soft tissue, such as articular cartilage, is essential to assess its mechanical function. It is also increasingly more evident that the inhomogeneity of the tissues plays a significant role in its in vivo functioning. Hence, efficient and reliable tools are needed to accurately characterize the inhomogeneity of the soft tissue mechanical properties. The objective of this research is to propose a finite element optimization procedure to determine depth-dependent material properties of articular cartilage by processing experimental data. Cartilage is modeled as a biphasic continuum with a linear elastic solid phase. The optimization method is based on a sensitivity analysis where the sensitivity of the finite element results to a variation in the material properties is analytically evaluated. The elastic modulus and permeability of the tissue are assumed to vary either linearly or quadratically through the thickness of the cartilage layer. After adopting some initial estimates, these material properties are updated iteratively based on their sensitivities to the current results, and the difference between the actual experimental data and computational experimental data. The optimization method has been tested in two common experimental configurations of cartilage and found to be efficient to estimate the material properties.

An analytical method to create patient-specific deformed bone models using X-ray images and a healthy bone model.

Generation of patient-specific bone models from X-ray images is useful for various medical applications such as total hip replacement, implant manufacturing, knee kinematic studies and deformity correction. These models may provide valuable information required for a more reliable operation. In this work, we propose a new algorithm for generating patient-specific 3D models of femur and tibia with deformity, using only a generic healthy bone model and some simple measurements taken on the X-ray images of the diseased bone. Using the X-ray measurements, an interpolation function (a polynomial or a cubic spline) is fit to the mid-diaphyseal curve of the actual bone and the generic bone model is deformed in the guidance of this function with free form deformation method. The created models are intended to be used mainly for the visualization of fixation procedure in software-supported external fixation systems. An error measure is defined to quantify the error in this matching procedure. The method is found to be capable of producing deformed tibia models that satisfactorily reflect the actual bones, as confirmed by two orthopaedic surgeons who use software-supported external fixation systems regularly.

Determination of Nerve Fiber Diameter Distribution From Compound Action Potential: A Continuous Approach.

When a signal is initiated in the nerve, it is transmitted along each nerve fiber via an action potential (called single fiber action potential (SFAP)) which travels with a velocity that is related with the diameter of the fiber. The additive superposition of SFAPs constitutes the compound action potential (CAP) of the nerve. The fiber diameter distribution (FDD) in the nerve can be computed from the CAP data by solving an inverse problem. This is usually achieved by dividing the fibers into a finite number of diameter groups and solve a corresponding linear system to optimize FDD. However, number of fibers in a nerve can be measured sometimes in thousands and it is possible to assume a continuous distribution for the fiber diameters which leads to a gradient optimization problem. In this paper, we have evaluated this continuous approach to the solution of the inverse problem. We have utilized an analytical function for SFAP and an assumed a polynomial form for FDD. The inverse problem involves the optimization of polynomial coefficients to obtain the best estimate for the FDD. We have observed that an eighth order polynomial for FDD can capture both unimodal and bimodal fiber distributions present in vivo, even in case of noisy CAP data. The assumed FDD distribution regularizes the ill-conditioned inverse problem and produces good results.