Viscoelastic modulus reconstruction using time harmonic vibrations

This paper presents a new iterative reconstruction method to provide high-resolution images of shear modulus and viscosity via the internal measurement of displacement fields in tissues. To solve the inverse problem, we compute the Fr\'echet derivatives of the least-squares discrepancy functional with respect to the shear modulus and shear viscosity. The proposed iterative reconstruction method using this Fr\'echet derivative does not require any differentiation of the displacement data for the full isotropic linearly viscoelastic model, whereas the standard reconstruction methods require at least double differentiation. Because the minimization problem is ill-posed and highly nonlinear, this adjoint-based optimization method needs a very well-matched initial guess. We find a good initial guess. For a well-matched initial guess, numerical experiments show that the proposed method considerably improves the quality of the reconstructed viscoelastic images.Comment: 15 page

The human CHRNA7 and CHRFAM7A genes: A review of the genetics, regulation, and function

The human α7 neuronal nicotinic acetylcholine receptor gene (CHRNA7) is ubiquitously expressed in both the central nervous system and in the periphery. CHRNA7 is genetically linked to multiple disorders with cognitive deficits, including schizophrenia, bipolar disorder, ADHD, epilepsy, Alzheimer's disease, and Rett syndrome. The regulation of CHRNA7 is complex; more than a dozen mechanisms are known, one of which is a partial duplication of the parent gene. Exons 5–10 of CHRNA7 on chromosome 15 were duplicated and inserted 1.6 Mb upstream of CHRNA7, interrupting an earlier partial duplication of two other genes. The chimeric CHRFAM7A gene product, dupα7, assembles with α7 subunits, resulting in a dominant negative regulation of function. The duplication is human specific, occurring neither in primates nor in rodents. The duplicated α7 sequence in exons 5–10 of CHRFAM7A is almost identical to CHRNA7, and thus is not completely queried in high throughput genetic studies (GWAS). Further, pre-clinical animal models of the α7nAChR utilized in drug development research do not have CHRFAM7A (dupα7) and cannot fully model human drug responses. The wide expression of CHRNA7, its multiple functions and modes of regulation present challenges for study of this gene in disease

Spring-damper equivalents of the fractional, poroelastic, and poroviscoelastic models for elastography

In MR elastography it is common to use an elastic model for the tissue's response in order to properly interpret the results. More complex models such as viscoelastic, fractional viscoelastic, poroelastic, or poroviscoelastic ones are also used. These models appear at first sight to be very different, but here it is shown that they all may be expressed in terms of elementary viscoelastic models. For a medium expressed with fractional models, many elementary spring-damper combinations are added, each of them weighted according to a long-tailed distribution, hinting at a fractional distribution of time constants or relaxation frequencies. This may open up for a more physical interpretation of the fractional models. The shear wave component of the poroelastic model is shown to be modeled exactly by a three-component Zener model. The extended poroviscoelastic model is found to be equivalent to what is called a non-standard four-parameter model. Accordingly, the large number of parameters in the porous models can be reduced to the same number as in their viscoelastic equivalents. As long as the individual displacements from the solid and fluid parts cannot be measured individually the main use of the poro(visco)elastic models is therefore as a physics based method for determining parameters in a viscoelastic model.Comment: 11 pages, 7 figures. Changed inconsistent notation in Eqs 1, 5, 8, 10 and corrected mistakes in Eqs 2, 4, 12, 3

A novel approach to error function minimization for feedforward neural networks

Feedforward neural networks with error backpropagation (FFBP) are widely applied to pattern recognition. One general problem encountered with this type of neural networks is the uncertainty, whether the minimization procedure has converged to a global minimum of the cost function. To overcome this problem a novel approach to minimize the error function is presented. It allows to monitor the approach to the global minimum and as an outcome several ambiguities related to the choice of free parameters of the minimization procedure are removed.Comment: 11 pages, latex, 3 figures appended as uuencoded fil

Magnetic resonance elastography in nonlinear viscoelastic materials under load.

Characterisation of soft tissue mechanical properties is a topic of increasing interest in translational and clinical research. Magnetic resonance elastography (MRE) has been used in this context to assess the mechanical properties of tissues in vivo noninvasively. Typically, these analyses rely on linear viscoelastic wave equations to assess material properties from measured wave dynamics. However, deformations that occur in some tissues (e.g. liver during respiration, heart during the cardiac cycle, or external compression during a breast exam) can yield loading bias, complicating the interpretation of tissue stiffness from MRE measurements. In this paper, it is shown how combined knowledge of a material's rheology and loading state can be used to eliminate loading bias and enable interpretation of intrinsic (unloaded) stiffness properties. Equations are derived utilising perturbation theory and Cauchy's equations of motion to demonstrate the impact of loading state on periodic steady-state wave behaviour in nonlinear viscoelastic materials. These equations demonstrate how loading bias yields apparent material stiffening, softening and anisotropy. MRE sensitivity to deformation is demonstrated in an experimental phantom, showing a loading bias of up to twofold. From an unbiased stiffness of [Formula: see text] Pa in unloaded state, the biased stiffness increases to 9767.5 [Formula: see text]1949.9 Pa under a load of [Formula: see text] 34% uniaxial compression. Integrating knowledge of phantom loading and rheology into a novel MRE reconstruction, it is shown that it is possible to characterise intrinsic material characteristics, eliminating the loading bias from MRE data. The framework introduced and demonstrated in phantoms illustrates a pathway that can be translated and applied to MRE in complex deforming tissues. This would contribute to a better assessment of material properties in soft tissues employing elastography

Biomechanical properties of breast tissue, a state-of-the-art review

This paper reviews the existing literature on the tests used to determine the mechanical properties of women breast tissues (fat, glandular and tumour tissue) as well as the different values of these properties. The knowledge of the mechanical properties of breast tissue is important for cancer detection, study and planning of surgical procedures such as surgical breast reconstruction using pre-surgical methods and improving the interpretation of clinical tests. Based on the data collected from the analysed studies, some important conclusions were achieved: (1) the Young’s modulus of breast tissues is highly dependent on the tissue preload compression level, and (2) the results of these studies clearly indicate a wide variation in moduli not only among different types of tissue but also within each type of tissue. These differences were most evident in normal fat and fibroglandular tissues