Pixel-based approach to assess contrast-enhanced ultrasound kinetics parameters for differential diagnosis of rheumatoid arthritis

Abstract

Inflammatory rheumatic diseases are the leading causes of disability and constitute a frequent medical disorder, leading to inability to work, high comorbidity, and increased mortality. The standard for diagnosing and differentiating arthritis is based on clinical examination, laboratory exams, and imaging findings, such as synovitis, bone edema, or joint erosions. Contrast-enhanced ultrasound (CEUS) examination of the small joints is emerging as a sensitive tool for assessing vascularization and disease activity. Quantitative assessment is mostly performed at the region of interest level, where the mean intensity curve is fitted with an exponential function. We showed that using a more physiologically motivated perfusion curve, and by estimating the kinetic parameters separately pixel by pixel, the quantitative information gathered is able to more effectively characterize the different perfusion patterns. In particular, we demonstrated that a random forest classifier based on pixelwise quantification of the kinetic contrast agent perfusion features can discriminate rheumatoid arthritis from different arthritis forms (psoriatic arthritis, spondyloarthritis, and arthritis in connective tissue disease) with an average accuracy of 97%. On the contrary, clinical evaluation (DAS28), semiquantitative CEUS assessment, serological markers, or region-based parameters do not allow such a high diagnostic accuracy.

Keywords: arthritis; contrast enhanced ultrasound; kinetics analysis; parameter estimation; perfusion analysis.

Fig. 1

Contrast-enhanced ultrasound (CEUS) data of rheumatoid arthritis (RA) subjects. The B-mode ultrasound image used as anatomical reference is shown (top panel), together with a CEUS image from an early (middle panel) and late (bottom panel) perfusion phase. Each column represents an RA patient with a different grading (from 0 to 2, left to right).

Fig. 2

Profile of a gamma-variate curve. An example of gamma-variate (blue) with the main parameters of interest ($t_0$, ${\text{time}}_{\text{raise}}$, ${\text{time}}_{\text{peak}}$, ${\text{time}}_{\text{wash}}$, peak value, MTT, blood volume index, blood flow index).

Fig. 3

Data analysis workflow. The first block consists of the acquisition of the B-mode image and of the CEUS video. In the preprocessing phase, the synovia is manually drawn on the B-mode image and the motion correction is performed on the CEUS data, which are then coregistered. In the last block (quantification), the synovial time-activity curves are extracted and the kinetic parameters are estimated both at region and pixel level.

Fig. 4

CEUS semiquantitative analysis and serological values. Correlations of the serological values (anti-cyclic citrullinated peptides, RF, DAS28, C-reactive protein, and ERS) and of the semiquantitative CEUS analysis (grade and presumptive diagnosis) across all subjects are reported in terms of Pearson’s correlation ($R$).

Fig. 5

Parametric maps of quantitative kinetic estimates in a representative RA subject. (a) The mean transit time and (b) the time to peak are reported overlayed on the $I_{\mathrm{gs}}$ gray-scaled image. A Gaussian filter (FWHM 2.35 pixels) was used to smooth the images before visualization. Note the heterogeneous distribution of the kinetic parameters within the synovia and the presence of areas in the region of interest with no signal, highlighting the presence or absence of vascularization.

Fig. 6

Map of pixel failures of two RA patients. Parametric maps of pixel failures, which have been eliminated because nonphysiological and/or unreliable (in red) in two different disease conditions overlaid to the correspondent B-mode image. (a) CEUS image of a patient with IACUS grade 2 (35.9% of failures) and (b) CEUS image of a patient with IACUS grade 0 (2.4% of failures).

Fig. 7

Heterogeneity of kinetic patterns in a representative RA-like psoriatic arthritis (PSA) subject. (a) The cluster results show the presence of heterogeneous perfusion kinetics within the same synovial area in a RA-like PSA subject. Cluster kinetics were derived with $k$-means partitioning method (Euclidean distance, six clusters). (b) The corresponding time activity curves are reported for each cluster. All the centroids are significantly different (two-sided rank sum test, $p<0.05$).