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At least 217 records · Page 12Linked to original sources
arXiv2014

Improvement in quantification of urine components: Alternate technique

Urea and creatinine are two important diagnostic components of urine. The study of creatinine in liquid phase is difficult due to its feeble concentration in urine. To bring down the detection limit, Raman spectroscopy of dried urine samples was employed. Raman studies in association with partial least square algorithm of artificial urine samples gave improved results in dried phase as compared to liquid phase. These findings were further validated on real urine samples.

physics.med-ph↗
arXiv2014

Holographic imaging of unlabelled sperm cells for semen analysis: a review

Male reproductive health in both humans and animals is an important research field in biological study. In order to characterize the morphology, the motility and the concentration of the sperm cells, which are the most important parameters to feature them, digital holography demonstrated to be an attractive technique. Indeed, it is a labelfree, non-invasive and high-resolution method that enables the characterization of live specimen. The review is intended both for summarize the state-of-art on the semen analysis and recent achievement obtained by means of digital holography and for exploring new possible applications of digital holography in this field.

physics.bio-ph↗
arXiv2015

Statistical models and regularization strategies in statistical image reconstruction of low-dose X-ray CT: a survey

Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose X-ray computed tomography (CT) as compared to the conventional filtered back-projection (FBP) method for various clinical tasks. According to the maximum a posterior (MAP) estimation, the SIR methods can be typically formulated by an objective function consisting of two terms: (1) data-fidelity (or equivalently, data-fitting or data-mismatch) term modeling the statistics of projection measurements, and (2) regularization (or equivalently, prior or penalty) term reflecting prior knowledge or expectation on the characteristics of the image to be reconstructed. Existing SIR methods for low-dose CT can be divided into two groups: (1) those that use calibrated transmitted photon counts (before log-transform) with penalized maximum likelihood (pML) criterion, and (2) those that use calibrated line-integrals (after log-transform) with penalized weighted least-squares (PWLS) criterion. Accurate statistical modeling of the projection measurements is a prerequisite for SIR, while the regularization term in the objective function also plays a critical role for successful image reconstruction. This paper reviews several statistical models on CT projection measurements and various regularization strategies incorporating prior knowledge or expected properties of the image to be reconstructed, which together formulate the objective function of the SIR methods for low-dose X-ray CT.

physics.med-ph↗
arXiv2015

On using the Microsoft Kinect$^{\rm TM}$ sensors in the analysis of human motion

The present paper aims at providing the theoretical background required for investigating the use of the Microsoft Kinect$^{\rm TM}$ (`Kinect', for short) sensors (original and upgraded) in the analysis of human motion. Our methodology is developed in such a way that its application be easily adaptable to comparative studies of other systems used in capturing human-motion data. Our future plans include the application of this methodology to two situations: first, in a comparative study of the performance of the two Kinect sensors; second, in pursuing their validation on the basis of comparisons with a marker-based system (MBS). One important feature in our approach is the transformation of the MBS output into Kinect-output format, thus enabling the analysis of the measurements, obtained from different systems, with the same software application, i.e., the one we use in the analysis of Kinect-captured data; one example of such a transformation, for one popular marker-placement scheme (`Plug-in Gait'), is detailed. We propose that the similarity of the output, obtained from the different systems, be assessed on the basis of the comparison of a number of waveforms, representing the variation within the gait cycle of quantities which are commonly used in the modelling of the human motion. The data acquisition may involve commercially-available treadmills and a number of velocity settings: for instance, walking-motion data may be acquired at $5$ km/h, running-motion data at $8$ and $11$ km/h. We recommend that particular attention be called to systematic effects associated with the subject's knee and lower leg, as well as to the ability of the Kinect sensors in reliably capturing the details in the asymmetry of the motion for the left and right parts of the human body. The previous versions of the study have been withdrawn due to the use of a non-representative database.

physics.med-ph↗
arXiv2014

Alternative statistical methods for cytogenetic radiation biological dosimetry

The paper presents alternative statistical methods for biological dosimetry, such as the Bayesian and Monte Carlo method. The classical Gaussian and robust Bayesian fit algorithms for the linear, linear-quadratic as well as saturated and critical calibration curves are described. The Bayesian model selection algorithm for those curves is also presented. In addition, five methods of dose estimation for a mixed neutron and gamma irradiation field were described: two classical methods, two Bayesian methods and one Monte Carlo method. Bayesian methods were also enhanced and generalized for situations with many types of mixed radiation. All algorithms were presented in easy-to-use form, which can be applied to any computational programming language. The presented algorithm is universal, although it was originally dedicated to cytogenetic biological dosimetry of victims of a nuclear reactor accident.

stat.ME↗
arXiv2014

Evaluation of the productivity of Brazilian hospitals by the methodology of diagnosis related group (DRG)

The management requires a hospital organization to provision their costs/expenses with tools that approximate reality. The task of measuring productivity can be complex and uncertain, several methods are tested and the use of the DRG has been efficient, being used to assess the productivity through clinical outcomes. Cross-sectional study evaluated 145.710 hospitalizations in the period 2012-2014, using the DRG methodology for measuring productivity from the median length of hospitalization. When we group all hospitalizations in clinical (37.6%) and surgical (62.4%), multiple analyzes could be made according to this criterion. The DRG as a tool for prediction of hospital days is an effective alternative, thereby contributing to the control of productivity that directly influences the costs of hospital expenses and product and service quality.

physics.med-ph↗
arXiv2014

Physical Foundation for General Interior Tomography

Gauge invariability guarantees the same form of the Maxwell equations in different coordinate systems, and is instrumental for electromagnetic cloaking to hide a region of interest (ROI) perfectly. On the other hand, interior tomography is to reconstruct an ROI exactly. In this article, the recent results in these two disconnected areas are brought together to justify the general interior tomography principle. Several opportunities are suggested for tomographic research.

physics.med-ph↗
arXiv2014

X-optogenetics and U-optogenetics: Feasibility and Possibilities

To address these limitations of optogenetics, here we propose two new methods for optogenetic stimulation. The first is x-optogenetics, which uses visible light-emitting nanophosphors stimulated by focused x-rays. This idea is not new but the application to optogenetics is novel. X-rays can penetrate much more deeply than infrared light and could allow for nerve cell stimulation in any part of the brain. In this paper, we discuss the feasibility and possibilities of such a method by describing the advances in nanomaterials, x-ray focusing, and x-ray sources. Also, we discuss concerns when dealing with x-rays such as radiation dosage. Through the use of quantities and assumptions backed by recent literature, manufacturer specifications, and personal correspondence, a full feasibility analysis of x-optogenetics is completed. The second proposed method we explore is u-optogenetics, which is the application of sonoluminescence to optogenetics. Such a technique uses ultrasound waves instead of x-rays to induce light emission, so there would be no introduction of radiation. However, the penetration depth of ultrasound is less than that of x-ray. The key issues affecting feasibility are laid out for further investigation into both x-optogenetics and u-optogenetics.

physics.bio-ph↗
arXiv2014

Predictive Modelling of Toxicity Resulting from Radiotherapy Treatments of Head and Neck Cancer

In radiotherapy for head and neck cancer, the radiation dose delivered to the pharyngeal mucosa (mucosal lining of the throat) is thought to be a major contributing factor to dysphagia (swallowing dysfunction), the most commonly reported severe toxicity. There is a variation in the severity of dysphagia experienced by patients. Understanding the role of the dose distribution in dysphagia would allow improvements in the radiotherapy technique to be explored. The 3D dose distributions delivered to the pharyngeal mucosa of 249 patients treated as part of clinical trials were reconstructed. Pydicom was used to extract DICOM (digital imaging and communications in medicine) data (the standard file formats for medical imaging and radiotherapy data). NumPy and SciPy were used to manipulate the data to generate 3D maps of the dose distribution delivered to the pharyngeal mucosa and calculate metrics describing the dose distribution. Multivariate predictive modelling of severe dysphagia, including descriptions of the dose distribution and relevant clinical factors, was performed using Pandas and SciKit-Learn. Matplotlib and Mayavi were used for 2D and 3D data visualisation. A support vector classification model, with feature selection using randomised logistic regression, to predict radiation-induced severe dysphagia, was trained. When this model was independently validated, the area under the receiver operating characteristic curve was 0.54. The model has poor predictive power and work is ongoing to improve the model through alternative feature engineering and statistical modelling approaches.

physics.med-ph↗
arXiv2014

Spherical-Wave Far-Field Interferometer for Hard X-Ray Phase Contrast Imaging

Low dose, high contrast x-ray imaging is of general interest in medical diagnostic applications. X-ray Mach-Zehnder interferometers using collimated synchrotron beams demonstrate the highest levels of phase contrast under a given exposure dose. However, common x-ray sources emit divergent cone beams. Here, we developed a spherical-wave inline Mach-Zehnder interferometer for phase contrast imaging over an extended area with a broadband and divergent source. The first tabletop system was tested in imaging experiments of a mammographic accreditation phantom and various biological specimens. The noise level of the phase contrast images at a clinical radiation dose corresponded to a 6 nano radian bending of the x-ray wavefront. Un-resolved structures with conventional radiography and near-field interferometer techniques became visible at a fraction of the radiation dose.

physics.med-ph↗
arXiv2014

Stochastic biophysical modeling of irradiated cells

The paper presents a computational stochastic model of virtual cells irradiation, based on Quasi-Markov Chain Monte Carlo method and using biophysical input. The model is based on a stochastic tree of probabilities for each cell of the entire colony. Biophysics of the cells is described by probabilities and probability distributions provided as the input. The adaptation of nucleation and catastrophe theories, well known in physics, yields sigmoidal relationships for carcinogenic risk as a function of the irradiation. Adaptive response and bystander effect, incorporated into the model, improves its application. The results show that behavior of virtual cells can be successfully modeled, e.g. cancer transformation, creation of mutations, radioadaptation or radiotherapy. The used methodology makes the model universal and practical for simulations of general processes. Potential biophysical curves and relationships are also widely discussed in the paper. However, the presented theoretical model does not describe the real cells and tissues. Also the exposure geometry (e.g., uniform or non-uniform exposure), type of radiation (e.g., X-rays, gamma rays, neutrons, heavy ions, etc.) as well as microdosimetry are not presently addressed. The model is focused mainly on creation of general and maximal wide mathematical description of irradiated hypothetical cells treated as complex physical systems.

physics.bio-ph↗
arXiv2014

A Pilot Study on Coupling CT and MRI through Use of Semiconductor Nanoparticles

CT and MRI are the two most widely used imaging modalities in healthcare, each with its own merits and drawbacks. Combining these techniques in one machine could provide unprecedented resolution and sensitivity in a single scan, and serve as an ideal platform to explore physical coupling of x-ray excitation and magnetic resonance. Molecular probes such as functionalized nanophosphors present an opportunity to demonstrate a synergy between these modalities. However, a simultaneous CT-MRI scanner does not exist at this moment. As a pilot study, here we propose a mechanism in which water solutions containing LiGa5O8:Cr3+ nanophosphors can be excited with x-rays to store energy, and these excited particles may subsequently influence the T2 relaxation times of the solutions so that a difference in T2 can be measured by MRI before and after x-ray excitation. The trends seen in our study suggest that a measurable effect may exist from x-ray excitation of the nanophosphors. However, there are several experimental conditions that hinder the clarity of the results to be statistically significant up to a commonly accepted level (p=0.05), including insoluble nanoparticles and inter-scan variability. Nevertheless, the initial results from our experiments seem a consistent and inspiring story that x-rays modify MRI T2 values around nanophosphors. Upon availability of soluble nanophosphors, we will repeat our experiments to confirm these observations.

physics.med-ph↗
arXiv2014

Waveform Inversion with Source Encoding for Breast Sound Speed Reconstruction in Ultrasound Computed Tomography

Ultrasound computed tomography (USCT) holds great promise for improving the detection and management of breast cancer. Because they are based on the acoustic wave equation, waveform inversion-based reconstruction methods can produce images that possess improved spatial resolution properties over those produced by ray-based methods. However, waveform inversion methods are computationally demanding and have not been applied widely in USCT breast imaging. In this work, source encoding concepts are employed to develop an accelerated USCT reconstruction method that circumvents the large computational burden of conventional waveform inversion methods. This method, referred to as the waveform inversion with source encoding (WISE) method, encodes the measurement data using a random encoding vector and determines an estimate of the sound speed distribution by solving a stochastic optimization problem by use of a stochastic gradient descent algorithm. Both computer-simulation and experimental phantom studies are conducted to demonstrate the use of the WISE method. The results suggest that the WISE method maintains the high spatial resolution of waveform inversion methods while significantly reducing the computational burden.

physics.med-ph↗
arXiv2014

Comb models for transport along spiny dendrites

This chapter is a contribution in the "Handbook of Applications of Chaos Theory" ed. by Prof. Christos H Skiadas. The chapter is organized as follows. First we study the statistical properties of combs and explain how to reduce the effect of teeth on the movement along the backbone as a waiting time distribution between consecutive jumps. Second, we justify an employment of a comb-like structure as a paradigm for further exploration of a spiny dendrite. In particular, we show how a comb-like structure can sustain the phenomenon of the anomalous diffusion, reaction-diffusion and Lévy walks. Finally, we illustrate how the same models can be also useful to deal with the mechanism of ta translocation wave / translocation waves of CaMKII and its propagation failure. We also present a brief introduction to the fractional integro-differentiation in appendix at the end of the chapter.

q-bio.NC↗
arXiv2015

Spaser as Novel Versatile Biomedical Tool

Fluorescence imaging and spectroscopy remain the most powerful tools for visualization with chemical and immunological specificity of labeled biomolecules, viruses, cellular organelles, and living cells in complex biological backgrounds. However, a common drawback of fluorescence labels is that their brightness is limited by optical saturation and photobleaching. As an alternative, plasmonic metal nanoparticles are very promising as optical labels with no photobleaching and low optical saturation at realistic exciting intensities as was demonstrated in photoacoustic and photothermal sensing, imaging, and theranostics. However, plasmonic nanoparticles have wide absorption spectra and are not fluorescent, which limits their spectral selectivity and multimodal functionality, respectively. Here we demonstrate experimentally, both in vitro and in vivo, that spaser (surface plasmon amplification by stimulated emission of radiation) provides unprecedented efficiency as a versatile tool in biomedical research and applications. This is due to the unique combination of intense near-monochromatic stimulated emission and strongly enhanced absorption, free of optical saturation. Using soluble and biocompatible uranine dye as a gain medium surrounding the gold nanocore as a plasmonic resonator, we demonstrate unprecedented spaser stimulated emission intensity ("giant spasing") and a narrow spectral width (0.8 nm), which are more than ~380-fold and ~30-fold, respectively, better than in quantum dots as the best conventional fluorescent nanoprobes. At the same time, the plasmonic spaser nanocore served as excellent photoacoustic and photothermal contrast agents for imaging and nanobubble-based theranostics of cancer cells. This makes the spasers, arguably, the best multifunctional, super-contrast, low-toxicity optical probes in biomedical research, especially with single-pulse excitation.

cond-mat.mes-hall↗
arXiv2016

Grading of Mammalian Cumulus Oocyte Complexes using Machine Learning for in Vitro Embryo Culture

Visual observation of Cumulus Oocyte Complexes provides only limited information about its functional competence, whereas the molecular evaluations methods are cumbersome or costly. Image analysis of mammalian oocytes can provide attractive alternative to address this challenge. However, it is complex, given the huge number of oocytes under inspection and the subjective nature of the features inspected for identification. Supervised machine learning methods like random forest with annotations from expert biologists can make the analysis task standardized and reduces inter-subject variability. We present a semi-automatic framework for predicting the class an oocyte belongs to, based on multi-object parametric segmentation on the acquired microscopic image followed by a feature based classification using random forests.

cs.CV↗
arXiv2017

Fast non-coplanar beam orientation optimization based on group sparsity

The selection of beam orientations, which is a key step in radiation treatment planning, is particularly challenging for non-coplanar radiotherapy systems due to the large number of candidate beams. In this paper, we report progress on the group sparsity approach to beam orientation optimization, wherein beam angles are selected by solving a large scale fluence map optimization problem with an additional group sparsity penalty term that encourages most candidate beams to be inactive. The optimization problem is solved using an accelerated proximal gradient method, the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). We derive a closed-form expression for a relevant proximal operator which enables the application of FISTA. The proposed algorithm is used to create non-coplanar treatment plans for four cases (including head and neck, lung, and prostate cases), and the resulting plans are compared with clinical plans. The dosimetric quality of the group sparsity treatment plans is superior to that of the clinical plans. Moreover, the runtime for the group sparsity approach is typically about 5 minutes. Problems of this size could not be handled using the previous group sparsity method for beam orientation optimization, which was slow to solve much smaller coplanar cases. This work demonstrates for the first time that the group sparsity approach, when combined with an accelerated proximal gradient method such as FISTA, works effectively for non-coplanar cases with 500-800 candidate beams.

physics.med-ph↗