Graphics processing unit-accelerated Monte Carlo simulation of polarized light in complex three-dimensional media

Significance: Monte Carlo (MC) methods have been applied for studying interactions between polarized light and biological tissues, but most existing MC codes supporting polarization modeling can only simulate homogeneous or multi-layered domains, resulting in approximations when handling realistic tissue structures. Aim: Over the past decade, the speed of MC simulations has seen dramatic improvement with massively-parallel computing techniques. Developing hardware-accelerated MC simulation algorithms that can accurately model polarized light inside 3-D heterogeneous tissues can greatly expand the utility of polarization in biophotonics applications. Approach: Here we report a highly efficient polarized MC algorithm capable of modeling arbitrarily complex media defined over a voxelated domain. Each voxel of the domain can be associated with spherical scatters of various radii and densities. The Stokes vector of each simulated photon packet is updated through photon propagation, creating spatially resolved polarization measurements over the detectors or domain surface. Results: We have implemented this algorithm in our widely disseminated MC simulator, Monte Carlo eXtreme (MCX). It is validated by comparing with a reference CPU-based simulator in both homogeneous and layered domains, showing excellent agreement and a 931-fold speedup. Conclusion: The polarization-enabled MCX (pMCX) offers biophotonics community an efficient tool to explore polarized light in bio-tissues, and is freely available at http://mcx.space/.

Ground Penetrating Radar Algorithm to Sense the Depth of Blood Clot in Microwave Head Imaging

Background: Microwave imaging is one of the emerging non-invasive portable imaging techniques, which uses nonionized radiations to take a detailed view of biological tissues in the microwave frequency range. Brain stroke is an emergency caused by the interruption of the blood supply into parts of brain, leading to the loss of millions of brain cells. Imaging plays a major role in stroke diagnosis for prompt treatment. Objective: This work proposes a computationally efficient algorithm called the GPR algorithm to locate the blood clot with a size of 10 mm in microwave images. Methods: The electromagnetic waves are radiated, and backscattered reflections are received by Antipodal Vivaldi antenna with the parasitic patch (48 mm*21 mm). The received signals are converted to a planar 2D image, and the depth of the blood clot is identified from the B-scan image. The novelty of this work lies in applying the GPR algorithm for the accurate positioning of a blood clot in a multilayered head tissue. Results: The proposed system is effectively demonstrated using a 3D EM simulator and simulated results are verified in a Vector network analyzer (E8363B) with an experimental setup. Conclusion: This an alternative safe imaging modality compared to present imaging systems(CT and MRI)

Mechanical Feedback Control for Multicellular Tissue Size Maintenance: A Minireview

All living tissues and organs have their respective sizes, critical to various biological functions, such as development, growth, and homeostasis. As tissues and organs generally converge to a certain size, intrinsic regulatory mechanisms may be involved in the maintenance of size regulation. In recent years, important findings regarding size regulation have been obtained from diverse disciplines at the molecular and cellular levels. Here, I briefly review the size regulation of biological tissues from the perspective of control systems. This minireview focuses on how feedback systems engage in tissue size maintenance through the mechanical interactions of constituent cell collectives through intracellular signaling. I introduce a general framework of a feedback control system for tissue size regulation, followed by two examples: maintenance of epithelial tissue volume and epithelial tube diameter. The examples deliver the idea of how cellular mechano-response works for maintaining tissue size.

Electrical impedance tomography: a potential tool for intraoperative imaging of the tongue base

The presence of a tumor in the tongue is a pathology that requires surgical intervention from a certain stage. This type of surgery is difficult to perform because of the limited space available around the base of the tongue for the insertion of surgical tools. During the procedure, the surgeon has to stretch and then fix the tongue firmly in order to optimize the available space and prevent tissue movement. As a result, the preoperative images of the inside of the tongue no longer give a reliable indication of the position and shape of the cancerous tissue due to the deformation of the overall tissue in the area. Thus, new images are needed during the operation, but are very difficult to obtain using conventional techniques due to the presence of surgical tools. Electrical Impedance Tomography (EIT) is an imaging technique that maps the resistivity or difference of resistivity of biological tissues from electrical signals. The small size of the electrodes makes it a potentially interesting tool to obtain intraoperative images of the inside of the tongue. In this paper, the possibility of using EIT for this purpose is investigated. A detection method is proposed, including an original configuration of the electrodes, consistent with the anatomical specificities of the tongue. The proposed method is studied in simulation and then a proof of concept is obtained experimentally on a 3D printed test tank filled with saline solution and plant fibres.

A review on magnetic and spintronic neurostimulation: challenges and prospects

In the treatment of neurodegenerative, sensory and cardiovascular diseases, electrical probes and arrays have shown quite a promising success rate. However, despite the outstanding clinical outcomes, their operation is significantly hindered by non-selective control of electric fields. A promising alternative is micromagnetic stimulation (μMS) due to the high permeability of magnetic field through biological tissues. The induced electric field from the time-varying magnetic field generated by magnetic neurostimulators is used to remotely stimulate neighboring neurons. Due to the spatial asymmetry of the induced electric field, high spatial selectivity of neurostimulation has been realized. Herein, some popular choices of magnetic neurostimulators such as microcoils (μcoils) and spintronic nanodevices are reviewed. The neurostimulator features such as power consumption and resolution (aiming at cellular level) are discussed. In addition, the chronic stability and biocompatibility of these implantable neurostimulator are commented in favor of further translation to clinical settings. Furthermore, magnetic nanoparticles (MNPs), as another invaluable neurostimulation material, has emerged in recent years. Thus, in this review we have also included MNPs as a remote neurostimulation solution that overcomes physical limitations of invasive implants. Overall, this review provides peers with the recent development of ultra-low power, cellular-level, spatially selective magnetic neurostimulators of dimensions within micro- to nano-range for treating chronic neurological disorders. At the end of this review, some potential applications of next generation neuro-devices have also been discussed.

Fractal Dimension Analysis to Detect the Progress of Cancer Using Transmission Optical Microscopy

Fractal dimension, a measure of self-similarity in a structure, is a powerful physical parameter for the characterization of structural property of many partially filled disordered materials. Biological tissues are fractal in nature and reports show a change in self-similarity associated with the progress of cancer, resulting in changes in their fractal dimensions. Here, we report that fractal dimension measurement is a potential technique for the detection of different stages of cancer using transmission optical microscopy. Transmission optical microscopy of a thin tissue sample produces intensity distribution patterns proportional to its refractive index pattern, representing its mass density distribution. We measure fractal dimension detection of different cancer stages and find its universal feature. Many deadly cancers are difficult to detect in their early to different stages due to the hard-to-reach location of the organ and/or lack of symptoms until very late stages. To study these deadly cancers, tissue microarray (TMA) samples containing different stages of cancers are analyzed for pancreatic, breast, colon, and prostate cancers. The fractal dimension method correctly differentiates cancer stages in progressive cancer, raising possibilities for a physics-based accurate diagnosis method for cancer detection.

Postbuckling analysis of ultra-low rigidity serpentine structures

Serpentine structures are of growing interest due to its unique mechanical and physical properties for applications in stretchable electronics, mechanical sensing, biomedical devices. Mechanics-guided, deterministic three-dimensional (3D) assembly provide routes to form remarkable 3D structures, which in turn significantly improve its potential for applications. Therefore, an accurate postbuckling analysis is essential to the complex 3D serpentine structures with arbitrary geometry/material parameters. Here, simple, analytical expressions are obtained for the displacement and effective rigidity of serpentine structures during postbuckling. By tuning geometry parameters, the amplitude of assembled 3D serpentine structures can span a very broad range from zero to that of a straight ribbon. The analytical model can be used in design, fabrication, and application of versatile 3D serpentine structures to ensure their compatibility with the ultra-low rigidity biological tissues. A hierarchical 3D serpentine structure with ultra-low rigidity is presented to demonstrate the application of the analytical model.

Light-driven biological actuators to probe the rheology of 3D microtissues

The mechanical properties of biological tissues are key to the regulation of their physical integrity and function. Although the application of external loading or biochemical treatments allows to estimate these properties globally, it remains problematic to assess how such external stimuli compare with internal, cell-generated contractions. Here we engineered 3D microtissues composed of optogenetically-modified fibroblasts encapsulated within collagen. Using light to control the activity of RhoA, a major regulator of cellular contractility, we induced local mechanical perturbation within 3D fibrous microtissues, while tracking in real time microtissue stress and strain. We thus investigated the dynamic regulation of light-induced, local contractions and their spatio-temporal propagation in microtissues. By comparing the evolution of stresses and strains upon stimulation, we demonstrated the potential of our technique for quantifying tissue elasticity and viscosity, before examining the possibility of using light to map local anisotropies in mechanically heterogeneous microtissues. Altogether, our results open an avenue to non-destructively chart the rheology of 3D tissues in real time, using their own constituting cells as internal actuators.

A Real-time Breast Hyperthermia Monitoring Scheme Based on Processing of Microwave Scattering Parameters with Deep Learning

This study proposes a new method based on deep learning to determine whether the temperature values ​​are at an appropriate level during the use of microwave hyperthermia method in the treatment of breast cancer. To implement our method, we utilize the temperature dependent dielectric properties of biological tissues to generate the heating scenarios that simulates the thermal behavior of biological tissue during the breast cancer hyperthermia treatment. Using the temperature-dependent dielectric properties we designated corresponding temperature thresholds, next, we labeled the malignant tumor region and the healthy tissue region in accordance with the pre-determined thresholds. In addition, scattering problems are solved based on treatment (hot or heated) and pre-treatment (cool) scenarios. Using the difference between hot and cool states, we train, test, and validate the CNN. Our main purpose in the project is to determine whether the tissue is heated in the desired temperature region using only the single frequency differential scattered electric field data.

A Real-time Breast Hyperthermia Monitoring Scheme Based on Processing of Microwave Scattering Parameters with Deep Learning

This study proposes a new method based on deep learning to determine whether the temperature values ​​are at an appropriate level during the use of microwave hyperthermia method in the treatment of breast cancer. To implement our method, we utilize the temperature dependent dielectric properties of biological tissues to generate the heating scenarios that simulates the thermal behavior of biological tissue during the breast cancer hyperthermia treatment. Using the temperature-dependent dielectric properties we designated corresponding temperature thresholds, next, we labeled the malignant tumor region and the healthy tissue region in accordance with the pre-determined thresholds. In addition, scattering problems are solved based on treatment (hot or heated) and pre-treatment (cool) scenarios. Using the difference between hot and cool states, we train, test, and validate the CNN. Our main purpose in the project is to determine whether the tissue is heated in the desired temperature region using only the single frequency differential scattered electric field data.