Literature DB >> 26359812

Hyperspectral imaging for detection of arthritis: feasibility and prospects.

Matija Milanic, Lukasz A Paluchowski, Lise L Randeberg.   

Abstract

Rheumatoid arthritis (RA) is a disease that frequently leads to joint destruction. It has a high incidence rate worldwide, and the disease significantly reduces patients’ quality of life. Detecting and treating inflammatory arthritis before structural damage to the joint has occurred is known to be essential for preventing patient disability and pain. Existing diagnostic technologies are expensive, time consuming, and require trained personnel to collect and interpret data. Optical techniques might be a fast, noninvasive alternative. Hyperspectral imaging (HSI) is a noncontact optical technique which provides both spectral and spatial information in one measurement. In this study, the feasibility of HSI in arthritis diagnostics was explored by numerical simulations and optimal imaging parameters were identified. Hyperspectral reflectance and transmission images of RA and normal human joint models were simulated using the Monte Carlo method. The spectral range was 600 to 1100 nm. Characteristic spatial patterns for RA joints and two spectral windows with transmission were identified. The study demonstrated that transmittance images of human joints could be used as one parameter for discrimination between arthritic and unaffected joints. The presented work shows that HSI is a promising imaging modality for the diagnostics and follow-up monitoring of arthritis in small joints.

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Year:  2015        PMID: 26359812     DOI: 10.1117/1.JBO.20.9.096011

Source DB:  PubMed          Journal:  J Biomed Opt        ISSN: 1083-3668            Impact factor:   3.170


  4 in total

1.  Assessing spectral imaging of the human finger for detection of arthritis.

Authors:  Rok Dolenec; Elmar Laistler; Matija Milanic
Journal:  Biomed Opt Express       Date:  2019-11-26       Impact factor: 3.732

2.  Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks.

Authors:  Martin Halicek; James V Little; Xu Wang; Amy Y Chen; Baowei Fei
Journal:  J Biomed Opt       Date:  2019-03       Impact factor: 3.170

3.  Hyperspectral autofluorescence characterization of drusen and sub-RPE deposits in age-related macular degeneration.

Authors:  Yuehong Tong; Thomas Ach; Christine A Curcio; R Theodore Smith
Journal:  Ann Eye Sci       Date:  2021-03-15

4.  Polarizer-Free AOTF-Based SWIR Hyperspectral Imaging for Biomedical Applications.

Authors:  Vladislav Batshev; Alexander Machikhin; Grigoriy Martynov; Vitold Pozhar; Sergey Boritko; Milana Sharikova; Vladimir Lomonov; Alexander Vinogradov
Journal:  Sensors (Basel)       Date:  2020-08-08       Impact factor: 3.576

  4 in total

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