Concept: The MathWorks
BACKGROUND: There is an increasing need for processing and understanding relevant information generated by the systematic collection of public health data over time. However, the analysis of those time series usually requires advanced modeling techniques, which are not necessarily mastered by staff, technicians and researchers working on public health and epidemiology. Here a user-friendly tool, EPIPOI, is presented that facilitates the exploration and extraction of parameters describing trends, seasonality and anomalies that characterize epidemiological processes. It also enables the inspection of those parameters across geographic regions. Although the visual exploration and extraction of relevant parameters from time series data is crucial in epidemiological research, until now it had been largely restricted to specialists. METHODS: EPIPOI is freely available software developed in Matlab (The Mathworks Inc) that runs both on PC and Mac computers. Its friendly interface guides users intuitively through useful comparative analyses including the comparison of spatial patterns in temporal parameters. RESULTS: EPIPOI is able to handle complex analyses in an accessible way. A prototype has already been used to assist researchers in a variety of contexts from didactic use in public health workshops to the main analytical tool in published research. CONCLUSIONS: EPIPOI can assist public health officials and students to explore time series data using a broad range of sophisticated analytical and visualization tools. It also provides an analytical environment where even advanced users can benefit by enabling a higher degree of control over model assumptions, such as those associated with detecting disease outbreaks and pandemics.
This paper tackles the design of a graphical user interface (GUI) based on Matlab (MathWorks Inc., MA), a worldwide standard in the processing of biosignals, which allows the acquisition of muscular force signals and images from a ultrasound scanner simultaneously. Thus, it is possible to unify two key magnitudes for analyzing the evolution of muscular injuries: the force exerted by the muscle and section/length of the muscle when such force is exerted. This paper describes the modules developed to finally show its applicability with a case study to analyze the functioning capacity of the shoulder rotator cuff.
A memory-efficient algorithm for the computation of Principal Component Analysis (PCA) of large mass spectrometry imaging data sets is presented. Mass Spectrometry Imaging (MSI) enables two- and three- dimensional overviews of hundreds of unlabeled molecular species in complex samples such as intact tissue. PCA, in combination with data binning or other reduction algorithms, has been widely used in the unsupervised processing of MSI data and as a dimentionality reduction method prior to clustering and spatial segmentation. Standard implementations of PCA require the data to be stored in random access memory. This imposes an upper limit on the amount of data that can be processed, necessitating a compromise between the number of pixels and the number of peaks to include. With increasing interest in multivariate analysis of large 3D multi-slice datasets and ongoing improvements in instrumentation, the ability to retain all pixels and many more peaks is increasingly important. We present a new method which has no limitation on the number of pixels and allows an increased number of peaks to be retained. The new technique was validated against the MATLAB (The MathWorks Inc., Natick, Massachusetts) implementation of PCA (princomp) and then used to reduce, without discarding peaks or pixels, multiple serial sections acquired from a single mouse brain which was too large to be analysed with princomp. k-means clustering was then performed on the reduced dataset. We further demonstrate with simulated data of 83 slices, comprising 20535 pixels per slice and equalling 44 GB of data, that the new method can be used in combination with existing tools to process an entire organ. MATLAB code implementing the memory efficient PCA algorithm is provided.
We present here a toolbox for the real-time motion capture of biological movements that runs in the cross-platform MATLAB environment (The MathWorks, Inc., Natick, MA). It provides instantaneous processing of the 3-D movement coordinates of up to 20 markers at a single instant. Available functions include (1) the setting of reference positions, areas, and trajectories of interest; (2) recording of the 3-D coordinates for each marker over the trial duration; and (3) the detection of events to use as triggers for external reinforcers (e.g., lights, sounds, or odors). Through fast online communication between the hardware controller and RTMocap, automatic trial selection is possible by means of either a preset or an adaptive criterion. Rapid preprocessing of signals is also provided, which includes artifact rejection, filtering, spline interpolation, and averaging. A key example is detailed, and three typical variations are developed (1) to provide a clear understanding of the importance of real-time control for 3-D motion in cognitive sciences and (2) to present users with simple lines of code that can be used as starting points for customizing experiments using the simple MATLAB syntax. RTMocap is freely available ( http://sites.google.com/site/RTMocap/ ) under the GNU public license for noncommercial use and open-source development, together with sample data and extensive documentation.
A computational toolkit (spektr 3.0) has been developed to calculate x-ray spectra based on the tungsten anode spectral model using interpolating cubic splines (TASMICS) algorithm, updating previous work based on the tungsten anode spectral model using interpolating polynomials (TASMIP) spectral model. The toolkit includes a matlab (The Mathworks, Natick, MA) function library and improved user interface (UI) along with an optimization algorithm to match calculated beam quality with measurements.
Background and purpose The purpose of this article is to estimate the distribution of superselective intra-arterial chemotherapy (IAC) delivery to ocular target tissue using quantitative digital subtraction angiography (qDSA). Materials and methods From March 2010 to January 2016, 50 ophthalmic artery contrast DSAs obtained immediately prior to IAC infusions in 22 patients were analyzed. This study was conducted under a retrospective review IRB (no. 10-01862). Parametric color-coded DSAs (iFlow, Siemens Medical) were post-processed (MATLAB, The Mathworks Inc.) using two methods: two box regions of interest (pre-retina and globe) and four custom regions of interest (ROIs-ophthalmic artery, choroid, supraclinoid internal carotid artery (ICA), cavernous ICA). Mean interobserver reliability of custom ROI selection is presented as a 95% confidence interval of interclass correlation, and fractional chemotherapy delivery to selected ROIs as means ± standard deviation in this study. Results The estimated fraction of chemotherapy delivered to the globe with the first method was 79.5%. Percentage regional delivery using the second method was as follows: ophthalmic artery, 85.8%; choroid, 60.5%; supraclinoid ICA, 14.2%. The cavernous ICA ROI (encompassing distal catheter and potential reflux) gave a signal equivalent to 9.3% of total delivery. Conclusion Parametric color-coded qDSA can estimate the fraction of IAC delivered to the retina and other orbital structures in ocular retinoblastoma patients. This information can inform delivery location and dosing strategies on a patient-specific basis.
Dual-energy computed tomography (DECT) uses two different x-ray energy spectra in order to differentiate between tissues, materials or elements in a single sample or patient. DECT is becoming increasingly popular in clinical imaging and preclinical in vivo imaging of small animal models, but there have been only very few reports on ex vivo DECT of biological samples at microscopic resolutions. The present study has three main aims. First, we explore the potential of microscopic DECT (microDECT) for delivering isotropic multichannel 3D images of fixed biological samples with standard commercial laboratory-based microCT setups at spatial resolutions reaching below 10 μm. Second, we aim for retaining the maximum image resolution and quality during the material decomposition. Third, we want to test the suitability for microDECT imaging of different contrast agents currently used for ex vivo staining of biological samples. To address these aims, we used microCT scans of four different samples stained with x-ray dense contrast agents. MicroDECT scans were acquired with five different commercial microCT scanners from four companies. We present a detailed description of the microDECT workflow, including sample preparation, image acquisition, image processing and postreconstruction material decomposition, which may serve as practical guide for applying microDECT. The MATLAB script (The Mathworks Inc., Natick, MA, USA) used for material decomposition (including a graphical user interface) is provided as a supplement to this paper (https://github.com/microDECT/DECTDec). In general, the presented microDECT workflow yielded satisfactory results for all tested specimens. Original scan resolutions have been mostly retained in the separate material fractions after basis material decomposition. In addition to decomposition of mineralized tissues (inherent sample contrast) and stained soft tissues, we present a case of double labelling of different soft tissues with subsequent material decomposition. We conclude that, in contrast to in vivo DECT examinations, small ex vivo specimens offer some clear advantages regarding technical parameters of the microCT setup and the use of contrast agents. These include a higher flexibility in source peak voltages and x-ray filters, a lower degree of beam hardening due to small sample size, the lack of restriction to nontoxic contrast agents and the lack of a limit in exposure time and radiation dose. We argue that microDECT, because of its flexibility combined with already established contrast agents and the vast number of currently unexploited stains, will in future represent an important technique for various applications in biological research.
It is common for biomechanics data sets to contain numerous dependent variables recorded over time, for many subjects, groups, and/or conditions. These data often require standard sorting, processing, and analysis operations to be performed in order to answer research questions. Visualization of these data is also crucial. This manuscript presents biomechZoo, an open-source toolbox that provides tools and graphical user interfaces to help users achieve these goals. The aims of this manuscript are to (1) introduce the main features of the toolbox, including a virtual three-dimensional environment to animate motion data (Director), a data plotting suite (Ensembler), and functions for the computation of three-dimensional lower-limb joint angles, moments, and power and (2) compare these computations to those of an existing validated system. To these ends, the steps required to process and analyze a sample data set via the toolbox are outlined. The data set comprises three-dimensional marker, ground reaction force (GRF), joint kinematic, and joint kinetic data of subjects performing straight walking and 90° turning manoeuvres. Joint kinematics and kinetics processed within the toolbox were found to be similar to outputs from a commercial system. The biomechZoo toolbox represents the work of several years and multiple contributors to provide a flexible platform to examine time-series data sets typical in the movement sciences. The toolbox has previously been used to process and analyse walking, running, and ice hockey data sets, and can integrate existing routines, such as the KineMat toolbox, for additional analyses. The toolbox can help researchers and clinicians new to programming or biomechanics to process and analyze their data through a customizable workflow, while advanced users are encouraged to contribute additional functionality to the project. Students may benefit from using biomechZoo as a learning and research tool. It is hoped that the toolbox can play a role in advancing research in the movement sciences. The biomechZoo m-files, sample data, and help repositories are available online (http://www.biomechzoo.com) under the Apache 2.0 License. The toolbox is supported for Matlab (r2014b or newer, The Mathworks Inc., Natick, USA) for Windows (Microsoft Corp., Redmond, USA) and Mac OS (Apple Inc., Cupertino, USA).
A MATLAB-based (The MathWorks, Inc, Natick, MA) computer program (the ganglion cell-inner plexiform layer [GCIPL] hemifield test) for automated detection of GCIPL thickness difference across the horizontal raphe was developed, and its glaucoma diagnostic performance was assessed.
This paper presents a computational study on contact characteristics of contact pressure and resultant deformation between an N95 filtering facepiece respirator and a newly developed digital headform. The geometry of the headform model is obtained based on computed tomography scanning of a volunteer. The segmentation and reconstruction of the headform model is performed by Mimics v16.0 (Materialise, Leuven, Belgium), which is a medical image processing software. The respirator model is obtained by scanning the surface of a 3M™ 8210 N95 respirator using a 3D digitizer and then the model is transformed by Geomagic Studio v12.0 (3D system, California, USA), a reverse engineering software. The headform model contains a soft tissue layer, a skull layer and a separate nose. The respirator model contains two layers (an inner face sealing layer and an outer layer) and a nose clip. Both the headform and respirator are modeled as solid elements and are deformable. The commercial software, LS-DYNA (LSTC, Livermore, California), is used to simulate the contact between the respirator and headform. Contact pressures and resultant deformation of the headform are investigated. Effects of respirator stiffness on contact characteristics are also studied. A Matlab (MathWorks, Natick, MA, USA) program is developed to calculate local gaps between the headform and respirator in the stable wearing state.