It is well known that the changes in tissue microstructure associated with certain pathophysiological conditions can influence its mechanical properties. Quantitatively relating the tissue microstructure to the macroscopic mechanical properties could lead to significant improvements in clinical diagnosis, especially when the mechanical properties of the tissue are used as diagnostic indices such as in digital rectal examination and elastography. In this study, a novel method of imposing periodic boundary conditions in non-periodic finite-element meshes is presented. This method is used to develop quantitative relationships between tissue microstructure and its apparent mechanical properties for benign and malignant tissue at various length scales. Finally, the inter-patient variation in the tissue properties is also investigated. Results show significant changes in the statistical distribution of the mechanical properties at different length scales. More importantly the loss of the normal differentiation of glandular structure of cancerous tissue has been demonstrated to lead to changes in mechanical properties and anisotropy. The proposed methodology is not limited to a particular tissue or material and the example used could help better understand how changes in the tissue microstructure caused by pathological conditions influence the mechanical properties, ultimately leading to more sensitive and accurate diagnostic technologies.
Biological tissues often experience drastic changes in their microstructure due to their pathophysiological conditions. Such microstructural changes could result in variations in mechanical properties, which can be used in diagnosing or monitoring a wide range of diseases, most notably cancer. This paves the avenue for non‐invasive diagnosis by instrumented palpation although challenges remain in quantitatively assessing the amount of diseased tissue by means of mechanical characterization. This paper presents a framework for tissue diagnosis using a quantitative and efficient estimation of the fractions of cancerous and non‐cancerous tissue without a priori knowledge of tissue microstructure. First, the sample is tested in a creep or stress relaxation experiment, and the behavior is characterized using a single term Prony series. A rule of mixtures, which relates tumor fraction to the apparent mechanical properties, is then obtained by minimizing the difference between strain energy of a heterogeneous system and an equivalent homogeneous one. Finally, the percentage of each tissue constituent is predicted by comparing the observed relaxation time with that calculated from the rule of mixtures. The proposed methodology is assessed using models reconstructed from histological samples and magnetic resonance imaging of prostate. Results show that estimation of cancerous tissue fraction can be obtained with a maximum error of 12% when samples of different sizes, geometries, and tumor fractions are presented. The proposed framework has the potential to be applied to a wide range of diseases such as rectal polyps, cirrhosis, or breast and prostate cancer whose current primary diagnosis remains qualitative.
Identification and characterization of nodules in soft tissue, including their size, shape, and location, provide a basis for tumor identification. This study proposes an inverse finite‐element (FE) based computational framework, for characterizing the size of examined tissue sample and detecting the presence of embedded tumor nodules using instrumented palpation, without a priori anatomical knowledge. The inverse analysis was applied to a model system, the human prostate, and was based on the reaction forces which can be obtained by trans‐rectal mechanical probing and those from an equivalent FE model, which was optimized iteratively, by minimizing an error function between the two cases, toward the target solution. The tumor nodule can be identified through its influence on the stress state of the prostate. The effectiveness of the proposed method was further verified using a realistic prostate model reconstructed from magnetic resonance (MR) images. The results show the proposed framework to be capable of characterizing the key geometrical indices of the prostate and identifying the presence of cancerous nodules. Therefore, it has potential, when combined with instrumented palpation, for primary diagnosis of prostate cancer, and, potentially, solid tumors in other types of soft tissue.
We theoretically analyse the spin-dependent tunnelling through an array of δ-magnetic barriers and give a set of general recursive relations. Using the recursive relations we analyse spin transmissions and polarizations under the constraint of counterbalancing the vector potentials with an electric potential controlled by gate voltages. After the theoretical and numerical analysis, we find that the polarization of the current vanishes when the magnetic barriers' orientations are in a particular pattern. Also we give a set of explicit expressions for the orientations. Then we demonstrate the physical properties of the orientations and their applications for the spin-filtering effect.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.