In silico experiments of time-resolved near-infrared light transport through human fingers with simulated rheumatoid arthritis

Ioussoufovitch, Seva; Diop, Mamadou

doi:10.1117/12.2576899

Cited by 4 publications

(14 citation statements)

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“…To simulate TD-DOI data, virtual phantoms were created using a realistic tissue geometry derived from an MRI segmentation of a healthy human finger. 26 Following segmentation, both the geometry and optical properties of the phantoms were altered to simulate tissue-specific changes in 5 physiological parameters associated with RA disease activity (Table 1). The range of possible values for each physiological parameter was bounded by previously described models of "None" and "Severe" RA disease activity.…”

Section: Methodsmentioning

confidence: 99%

“…The range of possible values for each physiological parameter was bounded by previously described models of "None" and "Severe" RA disease activity. 26 Two additional sets of values were obtained by linearly interpolating between these two endpoints: "Mild" and "Moderate" RA disease activity were defined as 33 and 66%, respectively, within the 0-100% disease activity range defined by the "None" and "Severe" models. Multiple combinations of parameter values were then assembled by successively varying the value of each parameter in Table 1 between the aforementioned 4 disease activity levels while fixing all remaining parameters to their Mild values.…”

Section: Methodsmentioning

confidence: 99%

“…Virtual phantoms were then created by converting parameter values into tissue geometries and optical properties as described in our previous work. 26 Following virtual phantom creation, MCXLAB was used to simulate the propagation of 800 nm time-domain light through the phantoms' proximal interphalangeal (PIP) joints. 27 To mimic TD-DOI, an 8 × 8 grid of light source positions was defined on the dorsal side of each phantom's PIP joint over an area (≈ 12 × 24 mm 2 ; L × W ) that encompassed, and was centered around, the joint cavity.…”

Section: Methodsmentioning

confidence: 99%

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Detection of physiological changes in rheumatoid arthritis using time-domain near-infrared imaging: an in silico phantom study

Ioussoufovitch¹,

Diop²

2023

Optical Tomography and Spectroscopy of Tissue XV

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Early treatment improves rheumatoid arthritis (RA) prognosis; however, 30% of patients still fail their first treatment and current methods can take as long as 3-6 months to detect treatment failure. Time-domain nearinfrared diffuse optical imaging (TD-DOI) has the potential to track gradual changes in RA disease activity and could be a more sensitive technique for RA treatment monitoring. Nevertheless, there has been very little investigation into the relationships between TD-DOI and the specific physiological changes that occur in RA. To this end, this work's objective was to investigate the effects of RA-associated physiological changes on TD-DOI images in silico. Virtual finger phantoms-derived from an MRI segmentation of a healthy human finger-were used to simulate changes in 5 physiological parameters that are typically affected in RA. Four levels of disease activity were considered for each parameter and each parameter was altered individually; parameter alterations were then translated into changes in phantom geometry and optical properties. MCXLAB was used to simulate the propagation of time-domain light (800 nm) through phantoms' proximal interphalangeal joints and Poisson noise was added to the TD-DOI data to simulate experimental conditions. Spatiotemporal Fourier decomposition was applied to the TD-DOI data to extract image components, which were grouped into datasets based on the physiological parameter that was altered. Component sensitivity to each physiological parameter was assessed using the ratio between a component's range and standard deviation within each dataset (range-to-error ratio; RER). Mean RER was highest for changes in synovial membrane and fluid volume, suggesting that this parameter may be a primary source of contrast for monitoring RA treatment response with TD-DOI.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Detection of physiological changes in rheumatoid arthritis using time-domain near-infrared imaging: an in silico phantom study

Ioussoufovitch¹,

Diop²

2023

Optical Tomography and Spectroscopy of Tissue XV

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“…For reference, tissue-specific changes in physiological parameters associated with RA disease activity are summarized in table 1. Simulations were performed using virtual phantoms based on a realistic tissue geometry derived from a segmented MRI image of a healthy adult finger (Wang et al 2020)-details of the segmentation have been previously described (Ioussoufovitch and Diop 2021). The segmented finger was composed of 7 tissue types (skin, subcutis, tendon, synovial membrane, synovial fluid, cartilage, and bone) and was used as the base tissue geometry for a model with no RA disease activity (i.e.…”

Section: Simulationsmentioning

confidence: 99%

“…'None'). Additional tissue geometries for 'Mild' , 'Moderate' , and 'Severe' models of disease activity were created by linearly transforming transverse slices of the None geometry to incrementally increase the volume of the synovial fluid and synovial membrane (Ioussoufovitch and Diop 2021). This approach was adopted from the work of Dolenec et al (2019), and is a straightforward method of simulating controlled expansion of the synovial fluid and synovial membrane within a desired region of an underlying tissue geometry.…”

Section: Simulationsmentioning

confidence: 99%

Time-domain diffuse optical imaging technique for monitoring rheumatoid arthritis disease activity: theoretical development and in silico validation

Ioussoufovitch,

Diop

2024

Phys. Med. Biol.

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Objective: Effective early treatment---within 3 - 5 months of disease onset---significantly improves rheumatoid arthritis (RA) prognosis. Nevertheless, 1 in 3 patients experiences treatment failure which takes 3 - 6 months to detect with current monitoring techniques. The aim of this work is to develop a method for extracting quantitative features from data obtained with time-domain diffuse optical imaging (TD-DOI), and demonstrate their sensitivity to RA disease activity.Approach: 80 virtual phantoms of the proximal interphalangeal joint---obtained from a realistic tissue distribution derived from magnetic resonance imaging---were created to simulate RA-induced alterations in 5 physiological parameters. TD-DOI images were generated using Monte Carlo simulations, and Poisson noise was added to each image. Subsequently, each image was convolved with an instrument response function (IRF) to mimic experimental measurements. Various spatiotemporal features were extracted from the images (i.e., statistical moments, temporal Fourier components), corrected for IRF effects, and correlated with the disease index (DI) of each phantom.Main results: Spatiotemporal Fourier components of TD-DOI images were highly correlated with DI despite the confounding effects of noise and the IRF. Moreover, lower temporal frequency components demonstrated greater sensitivity to small changes in disease activity than previously published spatial features extracted from the same images.Significance: Spatiotemporal components of TD-DOI images may be more sensitive to small changes in RA disease activity than previously reported DOI features. TD-DOI may enable earlier detection of RA treatment failure, which would reduce the time needed to identify treatment failure and improve patient prognosis.

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Time-domain diffuse optical imaging technique for monitoring rheumatoid arthritis disease activity: experimental validation in tissue-mimicking finger phantoms

Ioussoufovitch,

Diop

2024

Phys. Med. Biol.

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Objective: Effective treatment within 3 - 5 months of disease onset significantly improves rheumatoid arthritis (RA) prognosis. Nevertheless, 30% of RA patients fail their first treatment, and it takes 3 - 6 months to identify failure with current monitoring techniques. Time-domain diffuse optical imaging (TD-DOI) may be more sensitive to RA disease activity and could be used to detect treatment failure. In this report, we present the development of a TD-DOI hand imaging system and validate its ability to measure simulated changes in RA disease activity using tissue-mimicking finger phantoms.Approach: A TD-DOI system was built, based on a single-pixel camera architecture, and used to image solid phantoms which mimicked a proximal interphalangeal finger joint. For reference, in silico images of virtual models of the solid phantoms were also generated using Monte Carlo simulations. Spatiotemporal Fourier components were extracted from both simulated and experimental images, and their ability to distinguish between phantoms representing different RA disease activity was quantified.Main results: Many spatiotemporal Fourier components extracted from TD-DOI images could clearly distinguish between phantoms representing different states of RA disease activity. Significance: A TD-DOI system was built and validated using finger-mimicking solid phantoms. The findings suggest that the system could be used to monitor RA disease activity. This single-pixel TD-DOI system could be used to acquire longitudinal measures of RA disease activity to detect early treatment failure.

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In silico experiments of time-resolved near-infrared light transport through human fingers with simulated rheumatoid arthritis

Cited by 4 publications

References 0 publications

Detection of physiological changes in rheumatoid arthritis using time-domain near-infrared imaging: an in silico phantom study

Detection of physiological changes in rheumatoid arthritis using time-domain near-infrared imaging: an in silico phantom study

Time-domain diffuse optical imaging technique for monitoring rheumatoid arthritis disease activity: theoretical development and in silico validation

Time-domain diffuse optical imaging technique for monitoring rheumatoid arthritis disease activity: experimental validation in tissue-mimicking finger phantoms

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