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

Sviluppo di un tool automatico per l\u2019individuazione con risonanza magnetica del livello di attivit\ue0 di malattia nei pazienti affetti da artrite idiopatica giovanile in remissione clinica

INTRODUZIONE Il principale obiettivo del trattamento dei pazienti affetti da Artrite Idiopatica Giovanile (AIG) \ue8 quello di indurre la remissione clinica della malattia, fondamentale per prevenire la progressione del danno articolare e la conseguente disabilit\ue0 funzionale. Lo stato di remissione clinica viene valutato dal medico sulla base dell\u2019esame obiettivo (sinovite) del paziente e degli indici di infiammazione (VES o PCR). \uc8 noto, tuttavia, che l\u2019esame clinico possa non essere sufficientemente sensibile per l\u2019identificazione della presenza di sinovite sub clinica ossia non evidenziabile con l\u2019esame obiettivo del paziente [23]. La RM viene considerata la metodica di riferimento per l\u2019individuazione del processo infiammatorio a carico della membrana sinoviale. In un recente studio condotto presso il nostro istituto in una coorte di 90 pazienti affetti da AIG in remissione clinica, la RM ha dimostrato la presenza di sinovite subclinica nel 63% dei pazienti. \uc8\u2019 stato inoltre dimostrato che la persistenza del processo infiammatorio a livello della membrana sinoviale era significativamente correlato alla ricaduta della malattia e ad una progressione del danno strutturale a livello articolare. I metodi qualitativi e manuali ad oggi adoperati per la lettura delle RM articolari richiedono un notevole impegno temporale e presentano una soggettivit\ue0 intrinseca. Per supportare e superare i limiti dovuti alla valutazione qualitativa, operatore dipendente, sarebbe utile sviluppare dei metodi di segmentazione automatica delle immagini in grado di valutare la presenza di un processo infiammatorio a livello articolare in maniera obiettiva e riproducibile. OBIETTIVI Sviluppo di un metodo di segmentazione automatica delle immagini da RM che individui la presenza di sinovite subclinica in una coorte di pazienti con AIG in remissione clinica. METODI Il progetto ha previsto una revisione della letteratura sui metodi ad oggi proposti per l\u2019analisi automatizzata delle RM articolari nei pazienti affetti da artrite infiammatoria cronica. Per l\u2019analisi delle immagini mediche sono state adoperate le librerie \u201cInsight Toolkit\u201d (ITK), ed il tool ITK-SNAP rispettivamente per la segmentazione automatica e semi-automatica. ITK \ue8 una libreria open-source ampiamente adoperata per lo sviluppo di software di segmentazione e registrazione di immagini. ITK-SNAP \ue8 un\u2019applicazione software anch\u2019essa open-source usata per segmentare le strutture nelle immagini 3D e fornisce una segmentazione semi-automatica adoperando metodi di \u201cactive-countour\u201d (contorni attivi). Lo scopo dell\u2019utilizzo di questo tool \ue8 quello di estendere una segmentazione in modalit\ue0 singola ad una pipeline che combina la preelaborazione multimodale guidata dall\u2019utente e la segmentazione di oggetti di set di livelli (level-set) in cui si combinano in maniera congiunta tutte le informazioni provenienti da pi\uf9 canali. Le sequenze 3D-SPIR ottenute dopo la somministrazione del mezzo di contrasto, in 15 pazienti affetti da AIG con diversi gradi di severit\ue0 della malattia sono state valutate dapprima con una segmentazione semi-automatica e successivamente validate da una segmentazione manuale effettuata da un Radiologo Pediatra. Le stesse sequenze sono state elaborate mediante un metodo completamente automatizzato. Dapprima una pipeline di segmentazione basata su atlante \ue8 stata sviluppata allo scopo di registrare la sequenza di immagini da elaborare con delle immagini di riferimento (atlanti) opportunamente selezionate, l\u2019obiettivo di questa prima segmentazione \ue8 stato quello di individuare la regione di interesse e definire i markers; questi ultimi costituiscono l\u2019input della seconda segmentazione basata sul metodo dei contorni attivi dei nuovi markers ottenuti dalla precedente segmentazione. Il metodo automatico \ue8 stato anch\u2019esso confrontato con i risultati ottenuti dalla segmentazione manuale e semi-automatico e successivamente testato su un dataset pi\uf9 ampio di 30 pazienti con AIG in remissione clinica. RISULTATI Le RM (10 polsi e 5 articolazioni coxo-femorali) ottenute da 15 pazienti con AIG (M:5, 33%; F:10, 67%) sono state utilizzate per lo sviluppo di un metodo di segmentazione automatica per l\u2019identificazione e quantificazione della sinovite. Inizialmente \ue8 stato implementato un approccio di analisi semi automatica che rappresenta un buon compromesso tra affidabilit\ue0 e velocit\ue0 di individuazione dell\u2019area interessata dall\u2019attivit\ue0 della malattia; questo studio ha consentito di testare due differenti approcci algoritmici sullo stesso set di immagini ed individuare i parametri di segmentazione che pi\uf9 si avvicinano alla segmentazione manuale effettuata dal pediatra radiologo. La segmentazione \ue8 stata giudicata soddisfacente in 11 su 15 RM (73%) con il metodo della segmentazione di Threshold, soddisfacente in 10 su 15 RM (67%) con l\u2019utilizzo del metodo dei Cluster, operate entrambe dal tool ITK-SNAP. L\u2019identificazione dei parametri pi\uf9 significativi per eseguire la segmentazione semiautomatica \ue8 stata fondamentale per implementare il tool per automatizzare il processo di segmentazione dell\u2019articolazione. La soggettivit\ue0 dell\u2019operatore, con la selezione dell\u2019area di interesse, dei punti di markers e con la regolazione dei parametri della segmentazione, incide notevolmente sulla qualit\ue0 dell\u2019elaborazione eseguita. A tale scopo si \ue8 sviluppato un metodo completamente automatizzato, quindi intrinsecamente pi\uf9 riproducibile perch\ue9 eseguito esclusivamente dal calcolatore. Il tool per la identificazione automatica della sinovia infiammata \ue8 stato sottoposto ad una validazione preliminare su una corte di 30 RM (20 polsi e 10 bacini) ottenute da pazienti con AIG in remissione clinica. Di questi pazienti 5/30 (17%) erano maschi e 10/30 (33%) erano femmine. La durata mediana di malattia al momento di inclusione dello studio era di 8.5 anni; l\u2019et\ue0 mediana dei pazienti alla visita basale era di 13.8 anni. Di questi 30 pazienti sono state elaborate le articolazioni: 20/30 (67%) di polso e 10/30 (33%) coxo-femorali. La sinovite subclinica \ue8 stata identificata in 15/30 (50%) dei pazienti esaminati. La concordanza tra la lettura del radiologo pediatra e il metodo semi-automatico \ue8 stata valutata essere 86.66% (Cohen K=0.728). La concordanza tra la lettura del radiologo pediatra ed il metodo automatico \ue8 risultata essere dell\u201983.33% (Cohen K=0.663). CONCLUSIONI I risultati di questo studio suggeriscono che il metodo completamente automatico per l\u2019individuazione della sinovite, basato sulla registrazione delle immagini di risonanza \u201catlas-based\u201d, \ue8 affidabile per individuare la persistenza di un processo infiammatorio a livello articolare in pazienti con AIG in remissione clinica. In questo studio \ue8 stata adoperata la piattaforma ITK per le sue caratteristiche \u2019aperte\u2019 che la rendono un ottimo strumento di ricerche; la piattaforma ITK, infatti, fornisce un gran numero di procedure di registrazione/segmentazione adattabili alle caratteristiche del dato in esame. La valutazione automatica \ue8 significativamente pi\uf9 rapida rispetto a quella manuale e pi\uf9 obiettiva (meno operatore dipendente). Per queste caratteristiche appare un promettente strumento da impiegare nelle sperimentazioni cliniche atte a valutare l\u2019efficacia dei nuovi farmaci antireumatici nell\u2019indurre la remissione della malattia. Sono necessarie ulteriori indagini di approfondimento e di test allo scopo di ampliare il database di immagini di registrazione ed estendere l\u2019analisi ad altre articolazioni come ginocchio e caviglia. La segmentazione automatica dei tessuti articolari e muscolo-scheletrici \ue8 ancora una sfida importante per l'elaborazione delle immagini mediche. Con la standardizzazione nell'acquisizione RM e nell'identificazione dei biomarcatori, la segmentazione automatica \ue8 un passo inevitabile per passare dall'analisi di piccoli set di dati in cui la segmentazione manuale \ue8 una soluzione fattibile, a set di dati pi\uf9 grandi e studi multicentrici, ottenendo misure pi\uf9 standardizzate e affidabili. Inoltre, in presenza di un metodo validato di segmentazione automatico sarebbe possibile effettuare diverse analisi quantitative promettenti in modalit\ue0 pi\uf9 agevole, da applicare anche nella comune pratica clinica. La segmentazione automatica del tessuto articolare e muscolo-scheletrico pu\uf2 essere applicata a nuove analisi statistiche di "big data", come l'analisi dei dati topologici o l\u2019utilizzo di pattern di deep learning, che aiuterebbe i medici a comprendere meglio la fisiopatologia e la fenotipizzazione della malattia.INTRODUCTION The main goal of the treatment of patients with Juvenile Idiopathic Arthritis (JIA) is to induce the clinical remission of the disease, which is essential to prevent the progression of joint damage and the consequent functional disability. Clinical remission status is assessed by the physician based on the patient's physical examination (synovitis) and inflammation indices (ESR or CRP). It is known that the clinical examination may not be sensitive for the identification of the presence of subclinical synovitis, that is, not detectable with the patient's physical examination [23]. MRI is considered the reference method for identifying the inflammatory process affecting the synovial membrane. In a recent study conducted at our institute in a cohort of 90 JIA patients in clinical remission, MRI demonstrated the presence of subclinical synovitis in 63% of patients. It was also shown that the persistence of the inflammatory process at the level of the synovial membrane was related to the relapse of the disease and to a progression of structural damage at the joint level. The qualitative and manual methods used to date for the reading of joint MRIs require a considerable time commitment and present an intrinsic subjectivity. To support and overcome the limitations due to qualitative and operator dependent evaluation, it would be useful to develop methods of automatic image segmentation capable of evaluating the presence of an inflammatory process at the joint level in an objective and reproducible way. OBJECTIVES Development of an automatic MR image segmentation method that detects the presence of subclinical synovitis in a cohort of patients with JIA in clinical remission. METHODS The project involved a review of the literature on the methods currently proposed for the automated analysis of joint MRI in patients with chronic inflammatory arthritis. For the analysis of medical images, the "Insight Toolkit" (ITK) libraries and the ITK-SNAP tool were used for automatic and semi-automatic segmentation, respectively. ITK is an open-source library widely used for image segmentation and recording software development. ITK-SNAP is also an open-source software application used to segment structures in 3D images and provides semi-automatic segmentation using "active-countour" methods. The purpose of using this tool is to extend a single-mode segmentation to a pipeline that combines user-driven multimodal preprocessing and the segmentation of level-set objects in which they are combined in a joint manner all information from multiple channels. The 3D-SPIR sequences obtained after administration of the contrast medium, in 15 patients affected by JIA with different degrees of severity of the disease were first evaluated with a semi-automatic segmentation and subsequently validated by a manual segmentation performed by a pediatric radiologist. The sequences themselves were processed using a fully automated method. First, an atlas-based segmentation pipeline was developed in order to record the sequence of images to be processed with appropriately selected reference images (atlases), the goal of this first segmentation was to identify the region of interest and define the markers; the latter constitute the input of the second segmentation based on the method of active contours of the new markers obtained from the previous segmentation. The automatic method was also compared with the results obtained from manual and semi-automatic segmentation and subsequently tested on a larger dataset of 30 patients with JIA in clinical remission. RESULTS MRIs (10 wrists and 5 coxo-femoral joints) obtained from 15 patients with JIA (M: 5.33%; F: 10.67%) were used to develop an automated segmentation method for identification and quantification of synovitis. A semi-automatic analysis approach was initially implemented which represents a good compromise between reliability and speed of identification of the area affected by the activity of the disease; this study made it possible to test two different algorithmic approaches on the same set of images and to identify the segmentation parameters that are closest to the manual segmentation performed by the radiologist pediatrician. The segmentation was judged to be satisfactory in 11 / 15 RM (73%) with the Threshold segmentation method, in 10 / 15 RM (67%) with the use of the Cluster method, both operated by the ITK-SNAP tool. The identification of the most significant parameters to perform the semi-automatic segmentation was essential to implement the tool to automate the joint segmentation process. The subjectivity of the operator, with the selection of the area of interest, of the markers points and with the adjustment of the segmentation parameters, greatly affects the quality of the processing performed. For this purpose, a completely automated method has been developed, therefore intrinsically more reproducible being explicitly performed by the computer. The tool for automatic identification of inflamed synovium was subjected to a preliminary validation on a court of 30 MRIs (20 wrists and 10 coxo-femoral joints) obtained from patients with JIA in clinical remission. Of these patients 5/30 (17%) were male and 10/30 (33%) were female. The median duration of illness at the time of study inclusion was 8.5 years; the median age of the patients at the baseline visit was 13.8 years. Of these 30 patients, the joints were worked out: 20/30 (67%) wrist and 10/30 (33%) coxo-femoral. Subclinical synovitis was identified in 15/30 (50%) of the patients examined. The concordance between the pediatric radiologist's reading and the semi-automatic method was evaluated to be 86.66% (Cohen K = 0.728). The agreement between the reading by the pediatrician radiologist and the automatic method was found to be 83.33% (Cohen K = 0.663). CONCLUSIONS The results of this study suggest that the fully automatic method for the detection of synovitis, based on the recording of "atlas-based" resonance images, is reliable for detecting the persistence of an inflammatory process at the joint level in patients with JIA in remission. clinic. In this study, the ITK platform was used for its 'open' features that make it an excellent research tool; the ITK platform provides a large number of registration / segmentation procedures adaptable to the characteristics of the data in question. Automatic assessment is significantly faster than manual and more objective (less operator dependent). Due to these characteristics, it appears to be a promising tool to be used in clinical trials aimed at evaluating the efficacy of new antirheumatic drugs in inducing remission of the disease. Further in-depth investigations and tests are needed in order to expand the database of registration images and extend the analysis to other joints such as the knee and ankle. Automatic segmentation of joint and musculoskeletal tissues is still a major challenge for medical image processing. With standardization in MR acquisition and biomarker identification, automatic segmentation is an inevitable step in moving from small dataset analysis where manual segmentation is a feasible solution, to larger datasets and multi-center studies, obtaining more standardized and reliable measurements. Moreover, having a validated method of automatic segmentation would allow more easily to carry out several promising quantitative analyses, also applicable in common clinical practice. Automatic segmentation of joint and musculoskeletal tissue can be applied to new "big data" statistical analyzes, such as topological data analysis or the use of deep learning patterns, which would help physicians better understand pathophysiology and the phenotyping of the disease

Cellular Imaging in Regenerative Medicine, Cancer and Osteoarthritis

In this thesis we showed different methods to label cells for cellular imaging to determine their role in diagnosis and therapy of various diseases