Detection of joint inflammation in rheumatoid arthritis using multispectral diffuse optical imaging

Rheumatoid arthritis is a chronic autoimmune disease, characterised by joint inflammation, which if untreated causes disability. A clinical need exists for novel, low-cost and noninvasive imaging tools capable of detecting inflammation in the joints for the diagnosis and monitoring of patients with rheumatoid arthritis. Diffuse optical imaging provides information about the underlying functional properties of biological tissue and previous studies have reported an optical contrast between inflamed and non-inflamed joints, with former displaying localised increases in absorption and scattering attributed to underlying pathophysiological changes. In this work, a novel, multispectral diffuse optical imaging system for imaging human hand joints was presented, which combined surface imaging and optical transmission imaging in a single work-flow to reconstruct maps of clinically relevant parameters such as oxygen saturation, total haemoglobin, water and scattering amplitude in three dimensions. The system was designed to provide accurate, robust and rapid data acquisition, particularly through the novel application to joint imaging of a galvanometer-based unit for fast source repositioning allowing full datasets to be acquired in 2mins per joint, such as to be sufficient for implementation in a clinical setting. This clinical prototype system was then comprehensively studied through experiments involving biological tissue mimicking optical phantoms, to assess performance against a ground truth set of known parameters. Preliminary studies involving healthy volunteers gave useful insight into the systems in vivo performance and provided a good understanding of baseline values in healthy subjects, with significantly greater variability observed between subjects than when comparing joints within the same subject. A pilot clinical study was then carried out, involving 144 joints from 21 rheumatology patients with ultrasound imaging and clinical examination as reference comparisons, to assess the systems diagnostic accuracy capabilities. A degree of sensitivity was observed from three dimensional maps of total haemoglobin and scattering amplitude to pathophysiological changes in the joint during longitudinal monitoring of either recovery from acute injury in a single healthy subject or the response to therapy in rheumatoid arthritis patients. From single time-point examination data, classification accuracies when considering the entire cohort were limited, with areas under the receiver operator curve of up to 0.657 achieved, with similar conclusions reached to those in comparable single-wavelength, continuous-wave studies previously reported despite the multiple wavelength acquisition. A normalised Fourier transform methodology was then presented, engineered to extract features related to the spatial signature of the transmitted light through the joint that were less sensitive to inter-subject variability in total flux for the assessment of optical transmission images. For the first time within the academic community, to the authors knowledge, the impact on diffuse optical imaging signals of the spatially asymmetrical prevalence of inflammation in hand joints of rheumatoid arthritis patients was addressed. In distinction from previous work, optical images were acquired from the dorsal side with illumination on the palmar side and results when using the proposed normalised fast Fourier transform methodology demonstrated accurate detection of inflamed joints from single time-point examinations, with of area under the receiver operator curve values up to 0.888 together with sensitivities and specificities of up to 77.9% and 90.9% respectively achieved for this specific dataset. This work-flow may enable future development of clinically viable, low-cost devices for assessing inflammation in arthritis patients, without the need for cuff occlusion or comparison to baseline. It will be important to assess the generalisation of these accuracies in future work, using a larger patient cohort and testing different machine learning classification schemes

L1-norm Based Nonlinear Reconstruction Improves Quantitative Accuracy of Spectral Diffuse Optical Tomography

Spectrally constrained diffuse optical tomography (SCDOT) is known to improvereconstruction in diffuse optical imaging: constraining the reconstruction by coupling the optical properties across multiple wavelengths suppresses artefacts in the resulting reconstructed images. In other work, L1-norm regularization has been shown to improve certain types of image reconstruction problem as its sparsity-promoting properties render it robust against noise and enable preservation of edges in images, but because the L1-norm is non-differentiable, it is not always simple to implement. In this work, we show how to incorporate L1 regularization into SCDOT. Three popular algorithms for L1 regularization are assessed for application in SCDOT: iteratively reweighted least square algorithm (IRLS), alternating directional method of multipliers (ADMM) and fast iterative shrinkage-thresholding algorithm (FISTA).We introduce an objective procedure for determining the regularization parameter in these algorithms and compare their performance in simulated experiments, and in real data acquired from a tissue phantom. Our results show that L1 regularization consistently outperforms Tikhonov regularization in this application, particularly in the presence of noise

Multispectral diffuse optical tomography of finger joints

Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by synovial inflammation. The current treatment paradigm for earlier, more aggressive therapy places importance on development of functional imaging modalities, capable of quantifying joint changes at the earliest stages. Diffuse optical tomography (DOT) has shown great promise in this regard, due to its cheap, non-invasive, non-ionizing and high contrast nature. Underlying pathological activity in afflicted joints leads to altered optical properties of the synovial region, with absorption and scattering increasing. Previous studies have used these optical changes as features for classifying diseased joints from healthy. Non-tomographic, single wavelength, continuous wave (CW) measurements of trans-illuminated joints have previously reported achieving this with specificity and sensitivity in the range 80 - 90% [1]. A single wavelength, frequency domain DOT system, combined with machine learning techniques, has been shown to achieve sensitivity and specificity in the range of 93.8 - 100% [2]. A CW system is presented here which collects data at 5 wavelengths, enabling reconstruction of pathophysiological parameters such as oxygenation and total hemoglobin, with the aim of identifying localized hypoxia and angiogenesis associated with inflammation in RA joints. These initial studies focus on establishing levels of variation in recovered parameters from images of healthy controls.</p

Development of a cost effective optical imaging system for monitoring of Rheumatoid Arthritis

Rheumatoid Arthritis (RA) affects a significant portion of the population, but early treatment can improve patient outcomes. A low-cost, streamlined system has been developed for monitoring the progression of RA and inform treatment during this critical early window.</jats:p

Development of a cost effective optical imaging system for monitoring of Rheumatoid Arthritis

Rheumatoid Arthritis (RA) affects a significant portion of the population, but early treatment can improve patient outcomes. A low-cost, streamlined system has been developed for monitoring the progression of RA and inform treatment during this critical early window.</p