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

• Published in: Physics in medicine & biology 2024-06, Vol.69 (12)
• Main Authors: Ioussoufovitch, S, Diop, M
• Format: Article
• Language: English
• Subjects: Arthritis, Rheumatoid - diagnostic imaging; Computer Simulation; diffuse optical imaging; Humans; Image Processing, Computer-Assisted - methods; Monte Carlo Method; Monte Carlo simulation; near-infrared; Optical Imaging - methods; Phantoms, Imaging; rheumatoid arthritis; Time Factors; time-domain
• ISSN: 0031-9155; 1361-6560; 1361-6560
• DOI: 10.1088/1361-6560/ad539f

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. 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. 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 (⩽0.4 GHz) demonstrated greater sensitivity to small changes in disease activity than previously published spatial features extracted from the same images. 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.