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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 near-infrared 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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