Rheumatoid Arthritis: Kubassova O

Kubassova O, Boesen M, Peloschek P, Langs G, Cimmino MA, Bliddal H, Torp-Pedersen S. · Image Analysis Ltd., University of Leeds, 5-7 Cromer Terrace, Leeds, West Yorkshire, UK. · Ann N Y Acad Sci. · Pubmed #19250239 No free full text.

Abstract: Traditional imaging, represented by radiographs, provides a very concise description of anatomical pathology of bony structures. Both degenerative and inflammatory joint diseases are characterized by progressive joint destruction, and valid, reproducible measures of disease impact are available. Much effort has been expended to develop scoring systems for joint destruction in both osteoarthritis and rheumatoid arthritis, and the most common internationally accepted semiobjective scores are presented. The anatomical pathology mirrors the past activity of the disease, and advanced imaging gives an impression of the actual disease processes, which subsequently lead to the damage. Such information is required to facilitate the development of efficient therapy against arthritis. Newer technology, exemplified by MRI and ultrasound Doppler, supplements images of structural change with functional data of ongoing disease activity. This chapter focuses on the possibilities for quantification of images in MRI and ultrasound, in which postcontrast enhancement and Doppler information, respectively, are of special interest for the evaluation of the inflammatory changes of arthritis. To save time and eliminate human bias, automation is mandatory. In ultrasound, semiautomatic evaluations are coming that allow for a real-time, reproducible estimate of disease activity. With MRI fully automated algorithms have been developed for processing of data of bony structures, cartilage, and soft tissue, and are currently being implemented into everyday clinical practice.

Bliddal H, Boesen M, Christensen R, Kubassova O, Torp-Pedersen S. · The Parker Institute, Frederiksberg Hospital, 2000 Frederiksberg, Denmark. · Best Pract Res Clin Rheumatol. · Pubmed #19041080 No free full text.

Abstract: Imaging is key to the objective analysis of status in joint diseases. X-ray is the mainstay of imaging in both osteoarthritis (OA) and rheumatoid arthritis (RA) due to its accessibility, low cost and very good reproducibility. Also, considerable experience has been gathered in the evaluation of X-rays with the Sharp score in RA and the Kellgren-Lawrence score in OA. X-rays only show structural changes and, in comparison with magnetic resonance imaging (MRI), the detection of erosions on X-ray is delayed for more than 1 year. More advanced imaging by both MRI and ultrasound (US) may add to clinical examinations by showing signs of RA activity. US is by far the easiest modality to apply in a rheumatology outpatient setting, and is becoming an everyday diagnostic tool in many clinics. The definitions and standards for US are still being tested and need further work before application in longitudinal settings is possible. Reproducibility is better with MRI, but this examination is time-consuming, both in the acquisition phase with the patient and also for interpretation and scoring by the examiner. The latter issue seems to be overcome by computer-assisted diagnostics using algorithms for automatic evaluation. With technical developments and increasing knowledge regarding both MRI and US, both of these modalities may be of value in the evaluation of rheumatology patients.

Kubassova O. · School of Computing, University of Leeds, UK. · Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. · Pubmed #18051107 No free full text.

Abstract: In this paper we present an approach for blood vessel segmentation from dynamic contrast-enhanced MRI datasets of the hand joints acquired from patients with active rheumatoid arthritis. Exclusion of the blood vessels is needed for accurate visualisation of the activation events and objective evaluation of the degree of inflammation. The segmentation technique is based on statistical modelling motivated by the physiological properties of the individual tissues, such as speed of uptake and concentration of the contrast agent; it incorporates Markov random field probabilistic framework and principal component analysis. The algorithm was tested on 60 temporal slices and has shown promising results.

Kubassova O, Boesen M, Boyle RD, Cimmino MA, Jensen KE, Bliddal H, Radjenovic A. · School of Computing, University of Leeds, UK. · Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. · Pubmed #18044577 No free full text.

Abstract: A fully automated method for quantitative analysis of dynamic contrast-enhanced MRI data acquired with low and high field scanners, using spin echo and gradient echo sequences, depicting various joints is presented. The method incorporates efficient pre-processing techniques and a robust algorithm for quantitative assessment of dynamic signal intensity vs. time curves. It provides differentiated information to the reader regarding areas with the most active perfusion and permits depiction of different disease activity in separate compartments of a joint. Additionally, it provides information on the speed of contrast agent uptake by various tissues. The method delivers objective and easily reproducible results, which have been favourably viewed by a number of medical experts.