Imaging of Synovitis: Techniques & Applications
John Charles Waterton1,2
1Bioxydyn Ltd, MANCHESTER, United Kingdom, 2Centre for Imaging Sciences, Division of Informatics Imaging & Data Sciences, School of Health Sciences, Faculty of Biology Medicine & Health, University of Manchester, MANCHESTER, United Kingdom

Synopsis

Keywords: Musculoskeletal: Joints, Musculoskeletal: Tendons, Contrast mechanisms: Contrast agents

Synovitis – inflammation of the synovial membrane – is important in several conditions including osteo- and rheumatoid arthritis. Gadolinium contrast-enhanced (CE) and dynamic contrast-enhanced (DCE) techniques are technically preferable, although non-contrast-enhanced techniques are emerging. Scoring systems provide ordered categorical biomarkers of extent of synovitis, and volume of enhancement also provides an extensive-variable biomarker. If intensity of inflammation is of interest, DCEMRI provides intensive-variable biomarkers based either on heuristics or on compartmental modelling. Choice of synovitis biomarker depends on context of use: prognostic or predictive biomarkers demand reproducibility while monitoring and response biomarkers demand repeatability and sensitivity to change.

What is synovitis and why measure it by MRI?

Synovial membranes[1-4] surround fluid-filled cavities that require lubrication, notably diarthrodial joints (such as the hip and knee, and the joints of the fingers and hands), tendon sheaths, and bursae. The normal synovial membrane is typically comprised of three layers: intima (an inner surface layer 1-2 cells thick that communicates with its cavity); sub-intima (≤5mm thick, somewhat acellular, and comprised of extracellular matrix, blood vessels, lymphatics, nerves, fibroblasts, adipocytes, and immune cells); and an outer fibrous structural layer. Its functions include: lubrication with stretching, rolling, and folding of the membrane; production of viscous synovial fluid; and immune surveillance, oxygenation and nutrition for the joint or tendon.
Synovitis is inflammation of the synovial membrane, in e.g. trauma or infection. Synovitis typically accompanies arthritis (literally “inflammation of the joint”), notably in rheumatoid arthritis (RA) where very large deposits of proliferative synovitis (“pannus”) can occur, often exceeding 100 ml in large joints [5,6]. Other joint diseases also exhibit synovitis to some degree, notably osteoarthritis (OA), psoriatic arthritis, juvenile idiopathic arthritis, pigmented villonodular synovitis, haemophiliac arthropathy, gout, and others. Tenosynovitis is inflammation of the synovial membrane in the tendon sheath.
A special case is Hoffa’s fat pad [7-9], which may contain small patches of synovial tissue, being of particular importance in knee pain in osteoarthritis, where increased volume of non-fat signal area in Hoffa’s fat pad has been used as a proxy for synovitis.
Synovitis [2-4] is associated with vascular angiogenesis, infiltration of immune cells, and pain. In joints, synovitis erodes adjacent cartilage and bone: presence of synovitis forecasts structural damage. Synovitis can be investigated by palpation, by synovial biopsy, and/or by imaging (e.g. ultrasound, MRI). Following the NIH/FDA BEST [10] terminology, MR measurements of synovitis are commonly classed as “biomarkers”. Biomarkers can be used in different ways for different purposes, i.e. they have different “Contexts of Use (CoU)”. Typical CoU for MR biomarkers of synovitis include:

  • prognostic biomarker: forecasts the patient’s outcome, how rapidly their disease will progress;
  • predictive biomarker: indicates which treatment will be most efficacious and least harmful for each particular patient;
  • monitoring biomarker: assesses disease progression or maybe flags when to change therapy;
  • pharmacodynamic/response biomarker, particularly in drug development: indicates that an investigational drug has elicited the desired biological change and at what dose.
Of note, with a prognostic or predictive biomarker, a patient will typically be measured once, and the care pathway then chosen. Such biomarkers need to be “reproducible” [11], ideally providing comparable data in any hospital worldwide, to facilitate evidence-based medicine. In contradistinction, monitoring or pharmacodynamic/response biomarkers are measured on multiple occasions, usually with the same hardware and software, in the same patient. In that case, measurements need to be “repeatable” [11], so that small changes can be detected with confidence. The design of different MR biomarkers of synovitis in part reflects the constraints of the differing CoU.

MR biomarkers of synovitis

Synovitis is best assessed using contrast agent - based (Gd) techniques, which show good correlation with underlying histopathology [12]. Non-contrast agent based (non-Gd) techniques tend to be technically inferior to Gd techniques, both because Gd techniques are fairly sensitive and specific for inflammation, and also because the complex 3D structures of joints make it difficult to draw accurate regions of interest without Gd. However, some investigators seek to develop non-Gd approaches because of the costs, logistics and potential risks associated with Gd.

Gd contrast agent based biomarkers

The OMERACT (Outcome Measures in Rheumatology) collaboration [13] has devised and validated several scoring systems, notably the hand RA system RAMRIS [14], also systems for different arthritides in different locations e.g. PsAMRIS (psoriatic arthritis), TOMS (thumb OA), HIMRISS (hip OA) and so on. Each has subscores for different disease features. The most recent update [14] of the RAMRIS system includes ordered categorical variable subscores for synovitis (scoring 0-3 in each of 8 regions giving a range 0..24) and for tenosynovitis (scoring 0-3 in each of 14 regions giving a range 0..42), based in post-Gd signal enhancement in T1-weighted (T1w) MRI in a single hand. OMERACT strives to ensure reproducibility through the publication of atlases and score-sheets [15], and also assesses repeatability and responsiveness to change. RAMRIS is widely used and has become standard in clinical trials of investigational therapies [14]. A potential limitation of these synovitis subscores is that the gradations 0-3 are fairly coarse, so might not be particularly sensitive to small early changes. Additionally they assess extent but not intensity of inflammation. Scoring systems have not yet been developed for all joints which might need assessment. Investigators who require an extensive continuous variable can measure volume (ml) of enhancing pannus (VEP) in any location (and indeed in any species). VEP is derived from a summation of voxels which enhance significantly post-Gd in T1w MRI. Within-subject repeatability of 22% has been reported [16]. However between-centre reproducibility will need careful attention to methodological consistency, since detecting “significant” voxelwise enhancement must depend to some degree on sequence, signal-to-noise ratio, time of post-Gd observation and thresholding method.
Investigators who are interested in intensity as well as extent of inflammation can employ DCEMRI, yielding voxelwise biomarkers which can be mean- or median- averaged over a region of interest (RoI) for each joint or tendon. Some investigators [17,18] prefer to analyse signal intensity (SI) – time curves, reporting the intensive-variable heuristics initial rate of enhancement (IRE, %/s) and maximal enhancement (maximal SI / baseline SI), or classifying patterns of contrast uptake such as ‘persistent’, ‘plateau’, or ‘wash-out’. Such heuristics are straightforward to employ, and have been widely used. A potential limitation is the dependence on arbitrary signal units so that numerical values might not be reproducible between different sequences in different centres with different makes and models of scanner. An alternative intensive-variable heuristic [16,19] is the area under the dynamic curve over the initial 60 or 120 s. In this case, measurements use mM rather than arbitrary units, with Gd concentrations obtained from an initial mapping of the longitudinal relaxation rate (R1), dynamic ∆R1, and the known relaxivity of the contrast agent used. In principle the use of mM rather than arbitrary units should reduce the risk of between-centre irreproducibility. Finally, the dynamic curves can be analysed with a compartmental model, typically the extended Tofts model, providing intensive-variable biomarkers Ktrans (dimensions min-1) [16, 20, 21] the transfer constant for Gd from the vasculature, and vp the factional vascular plasma volume, range 0..1. For Ktrans, within-subject repeatability of 27%-30% has been reported [16, 20]. An advantage of compartmental modelling is that the resulting biomarkers admit a pathophysiologic interpretation which might illustrate the mode of action of an investigational drug. Such compartmental modelling can however be challenging, demanding accurate baseline R1 and dynamic ∆R1 in 3D with high spatial and temporal resolution as well as an accurate arterial input function. In particular R1 from variable flip angle 3D gradient echo sequences can be wildly inaccurate if not carefully calibrated.
All intensive-variable biomarkers are critically dependent on how their RoI is defined. If the same RoI is used pre- and post- treatment, then effective treatment would reduce both VEP and the voxelwise average of the intensive-variable biomarker such as ME, IAUC60, or Ktrans. Unbiased pre-post RoI definition can be derived from statistical shape modelling [16]. Alternatively if enhancement is used to define the RoIs, then effective treatment would reduce VEP and the size of the RoI, but might not affect the intensive-variable biomarker averaged over the smaller RoI.

Acknowledgements

No acknowledgement found.

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Proc. Intl. Soc. Mag. Reson. Med. 32 (2024)