Open-source smartphone platform for quantitative imaging and analysis in point-of-care applications

2021 ◽  
Author(s):  
Alberto J. Ruiz ◽  
Richard Allen ◽  
Ethan P. M. LaRochelle ◽  
Kimberley S. Samkoe ◽  
Brian W. Pogue
Keyword(s):  
Open Source ◽  
Point Of Care ◽  
Quantitative Imaging

scholarly journals DCEMRI.jl: A fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis

2014 ◽  
Author(s):  
David S Smith ◽  
Xia Li ◽  
Lori R Arlinghaus ◽  
Thomas E Yankeelov ◽  
E. Brian Welch
Keyword(s):  
Open Source ◽  
Tissue Concentration ◽  
Quantitative Imaging ◽  
Imaging Biomarkers ◽  
Problem Size ◽  
Data Set ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Run Time

We present a fast, validated, open-source toolkit for processing dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data. We validate it against the Quantitative Imaging Biomarkers Alliance (QIBA) Standard and Extended Tofts-Kety phantoms and find near perfect recovery in the absence of noise, with an estimated 10-20x speedup in run time compared to existing tools. To explain the observed trends in the fitting errors, we present an argument about the conditioning of the Jacobian in the limit of small and large parameter values. We also demonstrate its use on an in vivo data set to measure performance on a realistic application. For a 192 x 192 breast image, we achieved run times of < 1 s. Finally, we analyze run times scaling with problem size and find that the run time per voxel scales as O(N1.9), where N is the number of time points in the tissue concentration curve. DCEMRI.jl was much faster than any other analysis package tested and produced comparable accuracy, even in the presence of noise.

scholarly journals Open-source, 3D-printed Peristaltic Pumps for Small Volume Point-of-Care Liquid Handling

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Michael R. Behrens ◽  
Haley C. Fuller ◽  
Emily R. Swist ◽  
Jingwen Wu ◽  
Md. Mydul Islam ◽  
...  
Keyword(s):  
Open Source ◽  
Small Volume ◽  
Point Of Care ◽  
Liquid Handling ◽  
Peristaltic Pumps ◽  
3D Printed

scholarly journals Omics Meeting Onics: Towards the Next Generation of Spectroscopic-Based Technologies in Personalized Medicine

2019 ◽  
Vol 9 (3) ◽  
pp. 39 ◽  
Author(s):  
Weng Peng ◽  
Daniele Paesani
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Personalized Medicine ◽  
Open Source ◽  
Point Of Care ◽  
Next Generation ◽  
Omics Technologies ◽  
The Future ◽  
Monitoring Technologies ◽  
New Applications

This article aims to discuss the recent development of integrated point-of-care spectroscopic-based technologies that are paving the way for the next generation of diagnostic monitoring technologies in personalized medicine. Focusing on the nuclear magnetic resonance (NMR) technologies as the leading example, we discuss the emergence of -onics technologies (e.g., photonics and electronics) and how their coexistence with -omics technologies (e.g., genomics, proteomics, and metabolomics) can potentially change the future technological landscape of personalized medicine. The idea of an open-source (e.g., hardware and software) movement is discussed, and we argue that technology democratization will not only promote the dissemination of knowledge and inspire new applications, but it will also increase the speed of field implementation.

scholarly journals pyPOCQuant - A tool to automatically quantify Point-Of-Care Tests from images

2020 ◽  
Author(s):  
Andreas P. Cuny ◽  
Fabian Rudolf ◽  
Aaron Ponti
Keyword(s):  
Immune Response ◽  
Open Source ◽  
Laboratory Testing ◽  
Quantitative Measurement ◽  
Digital Images ◽  
Point Of Care ◽  
Lateral Flow ◽  
Vulnerable Groups ◽  
Visual Interpretation ◽  
Point Of Care Tests

AbstractLateral flow Point-Of-Care Tests (POCTs) are a valuable tool for rapidly detecting pathogens and the associated immune response in humans and animals. In the context of the SARS-CoV-2 pandemic, they offer rapid on-site diagnostics and can relieve centralized laboratory testing sites, thus freeing resources that can be focused on especially vulnerable groups. However, visual interpretation of the POCT test lines is subjective, error prone and only qualitative. Here we present pyPOCQuant, an open-source tool implemented in Python 3 that can robustly and reproducibly analyze POCTs from digital images and return an unbiased and quantitative measurement of the POCT test lines.

scholarly journals MoFlus: An Open-Source Android Software for Fluorescence-Based Point of Care

2022 ◽  
Vol 1 (2) ◽  
pp. 39-48
Author(s):  
Panji Wisnu Wirawan ◽  
Adi Wibowo
Keyword(s):  
Developing Countries ◽  
Open Source ◽  
Rural Areas ◽  
Point Of Care ◽  
Region Of Interest ◽  
High Sensitivity ◽  
Science Research ◽  
Dynamic Selection ◽  
Monitoring Tools ◽  
Color Data

High-sensitivity fluorescence-based tests are utilized to monitor various activities in life science research. These tests are specifically used as health monitoring tools to detect diseases. Fluorescence-based test facilities in rural areas and developing countries, however, remain limited. Point-of-care (POC) tests based on fluorescence detection have become a solution to the limitations of fluorescence-based tools in developing countries. POC software for smartphone cameras was generally developed for specific devices and tools, and it ability to select the desired region of interest (ROI) is limited. In this work, we developed Mobile Fluorescence Spectroscopy (MoFlus), an open-source Android software for camera-based POC. We mainly aimed to develop camera-based POC software that can be used for the dynamic selection of ROI; the number of samples; and the types of detection, color, data, and for communication with servers. MoFlus facilitated the use of touch screens and data given that it was developed on the basis of the SurfaceView library in Android and Javascript object notation applications. Moreover, the function and endurance of the app when used multiple times and with different numbers of images were tested.

scholarly journals DICOM for quantitative imaging biomarker development: A standards based approach to sharing of clinical data and structured PET/CT analysis results in head and neck cancer research

2015 ◽  
Author(s):  
Andriy Fedorov ◽  
David Clunie ◽  
Ethan Ulrich ◽  
Christian Bauer ◽  
Andreas Wahle ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Open Source ◽  
Open Source Software ◽  
Quantitative Imaging ◽  
Imaging Data ◽  
Dicom Standard ◽  
Free Open Source Software ◽  
Pet Ct ◽  
Free Open Source

Background. Imaging biomarkers hold tremendous promise in the precision medicine clinical applications. Development of such biomarkers relies heavily on image post-processing tools for automated image quantitation. Their deployment in the context of clinical research necessitates interoperability with the clinical systems. Comparison with the established outcomes and evaluation motivate integration of the clinical and imaging data, and the use of standardized approaches to sharing analysis results and semantics. We develop the methodology and supporting tools to perform these tasks in Positron Emission Tomography and Computed Tomography (PET/CT) quantitative imaging (QI) biomarker development applied to head and neck cancer (HNC) treatment response assessment, using the Digital Imaging Communication in Medicine (DICOM®) international standard and free open source software tools. Methods. Quantitative analysis of PET/CT imaging data collected on patients undergoing treatment for HNC was conducted. Processing steps included Standardized Uptake Value (SUV) normalization of the images, segmentation of the tumor and reference regions of interest (ROI) using manual and semi-automatic approaches, and extraction of the volumetric segmentation-based measurements. Suitable components of the DICOM standard were identified to model the various types of data produced by the analysis. A developer toolkit of conversion routines and an Application Programming Interface (API) were contributed and applied to create a standards-based representation of the data. Results. DICOM Real World Value Mapping, Segmentation and Structured Reporting objects were utilized for standards-compliant representation of the PET/CT QI analysis results. A number of correction proposals to the standard were developed. The open-source DICOM toolkit (DCMTK) was improved to simplify the task of encoding via new API abstractions. Conversion and visualization tools utilizing this toolkit were developed. The encoded objects were validated for consistency and interoperability. The resulting dataset was deposited to the QIN-HEADNECK collection of The Cancer Imaging Archive. Supporting tools for data analysis and DICOM conversion are available as free open source software. Discussion. We presented a detailed investigation of the development and application of the DICOM model, as well as the supporting open source tools and toolkits, to accommodate representation of the research data in QI biomarker development. We demonstrated that DICOM standard can be used to represent various types of the analysis results and encode their complex relationships. As a result, the data objects are interoperable with a variety of readily available tools and toolkits, as well as commercial clinical imaging and analysis systems that adopt the DICOM standard virtually universally.

scholarly journals Open-source, Adaptable, All-in-one Smartphone-based System for Quantitative Analysis of Point-of-care Diagnostics.

2021 ◽  
Author(s):  
Weronika Schary ◽  
Filip Paskali ◽  
Simone Rentschler ◽  
Christoph Ruppert ◽  
Gabriel Wagner ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Open Source ◽  
Low Income ◽  
Point Of Care ◽  
Low Cost ◽  
Data Extraction ◽  
Diagnostic Testing ◽  
Analysis Data ◽  
Great Opportunity ◽  
Lateral Flow Assays

Abstract Point-of-care (POC) diagnostics, in particular lateral flow assays (LFA), represent a great opportunity for rapid, precise, low-cost and accessible diagnosis of disease. Especially with the ongoing coronavirus disease 2019 (COVID-19) pandemic, rapid point-of-care tests are becoming everyday tools for identification and prevention. Using smartphones as biosensors can enhance POC devices as portable, low-cost POC platforms for healthcare and medicine, food and environmental monitoring, improving diagnosis and documentation in remote, low-income locations. We present an open-source, all-in-one smartphone-based system for quantitative analysis of LFAs. It consists of a 3D-printed photo box, a smartphone for image acquisition, and an R Shiny software package with modular, customizable analysis workflow for image editing, analysis, data extraction, calibration and quantification of the assays. This system is less expensive than commonly used hardware and software, so it could prove very beneficial for diagnostic testing in the context of pandemics, as well as in low-resource countries.

scholarly journals Open Health Imaging Foundation Viewer: An Extensible Open-Source Framework for Building Web-Based Imaging Applications to Support Cancer Research

2020 ◽  
pp. 336-345 ◽  
Author(s):  
Erik Ziegler ◽  
Trinity Urban ◽  
Danny Brown ◽  
James Petts ◽  
Steve D. Pieper ◽  
...  
Keyword(s):  
Cancer Research ◽  
Open Source ◽  
Web Application ◽  
Data Exchange ◽  
Quantitative Imaging ◽  
Third Party ◽  
Web Based ◽  
Web Architecture ◽  
Imaging Research ◽  
Commercial Applications

PURPOSE Zero-footprint Web architecture enables imaging applications to be deployed on premise or in the cloud without requiring installation of custom software on the user’s computer. Benefits include decreased costs and information technology support requirements, as well as improved accessibility across sites. The Open Health Imaging Foundation (OHIF) Viewer is an extensible platform developed to leverage these benefits and address the demand for open-source Web-based imaging applications. The platform can be modified to support site-specific workflows and accommodate evolving research requirements. MATERIALS AND METHODS The OHIF Viewer provides basic image review functionality (eg, image manipulation and measurement) as well as advanced visualization (eg, multiplanar reformatting). It is written as a client-only, single-page Web application that can easily be embedded into third-party applications or hosted as a standalone Web site. The platform provides extension points for software developers to include custom tools and adapt the system for their workflows. It is standards compliant and relies on DICOMweb for data exchange and OpenID Connect for authentication, but it can be configured to use any data source or authentication flow. Additionally, the user interface components are provided in a standalone component library so that developers can create custom extensions. RESULTS The OHIF Viewer and its underlying components have been widely adopted and integrated into multiple clinical research platforms (e,g Precision Imaging Metrics, XNAT, LabCAS, ISB-CGC) and commercial applications (eg, Osirix). It has also been used to build custom imaging applications (eg, ProstateCancer.ai, Crowds Cure Cancer [presented as a case study]). CONCLUSION The OHIF Viewer provides a flexible framework for building applications to support imaging research. Its adoption could reduce redundancies in software development for National Cancer Institute–funded projects, including Informatics Technology for Cancer Research and the Quantitative Imaging Network.

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