Dr. Elmar Laistler Profile

Dr. Elmar Laistler

at Medizinische Univ Wien

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Publications (2)

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Proceedings Article | 7 March 2019 Presentation

Mobile phone camera-based SpO2 measurements using broadband light and colored paper filters (Conference Presentation)

Morris Vanegas, Anh Phong Tran, Elmar Laistler, Qianqian Fang

Proceedings Volume 10869, 1086914 (2019) https://doi.org/10.1117/12.2510896

KEYWORDS: Optical filters, Cell phones, Optical testing, Monte Carlo methods, Cameras, Point-of-care devices, 3D modeling, Electronic filtering, Diagnostics, Oximetry

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The mobile health field has given rise to a surge of point-of-care diagnostic attachments for mobile phones. These attachments, however, are limited in adoption in low-resource settings due to initial acquisition and subsequent maintenance cost challenges. Point-of-care devices that require no or minimum attachment can make a great impact to the accessibility of such devices in resource-poor regions. In this abstract, we report a simulation study to demonstrate the feasibility of using an ultra-low-cost color-paper filter and a mobile phone to perform broadband pulse oximetry. We run a series of GPU-based Monte Carlo simulations using a previously segmented 7T MRI scan of a finger 3D model. We sweep the optical properties of the finger tissues between the wavelengh band of 400-800 nm with a 1 nm increment, with intensity based on the measured spectrum of an iPhone 8’s LED. We also measured the transmission spectra from paper filters of various colors, which we used to further alter the light source spectrum. Using a discretized photoplethysmogram (PPG) signal, we simulate a 60 bpm oscillation optical measurements due to an up to 15% volume changes of the finger arterioles. Simulations were repeated for various peripheral blood oxygen levels (SpO2). Finally, we estimate the SpO2 using the simulated PPG signals using the Ratio of Ratios (RR) method. We evaluate the performance of different color paper filters by comparing 1) total optical signal intensity, 2) maximum magnitude of the RR signal variations and 3) the correlation of the computed and assumed SpO2 values. We found that the purple-colored filter produced the highest RR signal variations and the cyan-colored paper resulted in the largest SpO2 changes in the tested range.

Proceedings Article | 12 February 2018 Presentation + Paper

Selection of optimal multispectral imaging system parameters for small joint arthritis detection

Rok Dolenec, Elmar Laistler, Jost Stergar, Matija Milanic

Proceedings Volume 10484, 1048413 (2018) https://doi.org/10.1117/12.2288192

KEYWORDS: Transmittance, Tissues, Monte Carlo methods, Multispectral imaging, Reflectivity, Tissue optics, Bone, Magnetic resonance imaging, Imaging systems

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Early detection and treatment of arthritis is essential for a successful outcome of the treatment, but it has proven to be very challenging with existing diagnostic methods. Novel methods based on the optical imaging of the affected joints are becoming an attractive alternative. A non-contact multispectral imaging (MSI) system for imaging of small joints of human hands and feet is being developed. In this work, a numerical simulation of the MSI system is presented. The purpose of the simulation is to determine the optimal design parameters. Inflamed and unaffected human joint models were constructed with a realistic geometry and tissue distributions, based on a MRI scan of a human finger with a spatial resolution of 0.2 mm. The light transport simulation is based on a weighted-photon 3D Monte Carlo method utilizing CUDA GPU acceleration. An uniform illumination of the finger within the 400-1100 nm spectral range was simulated and the photons exiting the joint were recorded using different acceptance angles. From the obtained reflectance and transmittance images the spectral and spatial features most indicative of inflammation were identified. Optimal acceptance angle and spectral bands were determined. This study demonstrates that proper selection of MSI system parameters critically affects ability of a MSI system to discriminate the unaffected and inflamed joints. The presented system design optimization approach could be applied to other pathologies.

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