Alessandro Coran

Improved detection of synovial boundaries in ultrasound examination by using a cascade of active-contours

Rheumatoid arthritis (RA) is a chronic multisystemic autoimmune disease, with an unclear etiopathogenesis. Its early diagnosis and activity assessment are essential to adjust the proper therapy. Among the different imaging techniques, ultrasonography (US) allows direct visualization of early inflammatory joint changes as synovitis, being also rapidly performed and easily accepted by patients. We propose an algorithm to semi-automatically detect synovial boundaries on US images, requiring minimal user interaction. In order to identify the synovia-bone and the synovia-soft tissues interfaces, and to tackle the morphological variability of diseased joints, a cascade of two different active contours is developed, whose composition corresponds to the whole synovial boundary. The algorithm was tested on US images acquired from proximal interphalangeal (PIP) and metacarpophalangeal (MCP) finger joints of 34 subjects. The results have been compared with a consensus manual segmentation. We obtained an overall mean sensitivity of 85±13%, and a mean Dices similarity index of 80±8%, with a mean Hausdorff distance from the manual segmentation of 28±10 pixels (approximately 1.4±0.5mm), that are a better performance than those obtained by the raters with respect to the consensus.

Evaluation of Finger Joint Synovial Vascularity in Patients with Rheumatoid Arthritis Using Contrast-Enhanced Ultrasound with Water Immersion and a Stabilized Probe

To assess synovial microvascularity in finger joints with rheumatoid arthritis (RA) by contrast‐enhanced ultrasound (CEUS), distinguishing between cases of active disease and those in remission; to standardize the technique for software analysis.

Imaging of soft-tissue tumors

The objective of this review is to highlight the major imaging characteristics of the main soft‐tissue sarcoma histotypes observed in the group “Sarcomi” of the Istituto Oncologico Veneto in the last 5 years. A literature review was performed using PubMed and textbooks. Radiological imaging can guide the diagnosis for the subset of lesions that have typical clinical and imaging features. Soft‐tissue tumors are common in clinical practice and a systematic clinical and imaging approach may guide the diagnosis. J. Magn. Reson. Imaging 2013;37:791–804.

Effects of mud-bath therapy in psoriatic arthritis patients treated with TNF inhibitors. Clinical evaluation and assessment of synovial inflammation by contrast-enhanced ultrasound (CEUS).

OBJECTIVES Despite the efficacy of TNF inhibitors, most patients with psoriatic arthritis maintain a residual synovial inflammation. The main aim of the study was to evaluate the effects of mud-bath therapy on clinical picture of PsA patients treated with TNF inhibitors. The secondary outcome was to assess synovial inflammation in hand joints detected by contrast-enhanced ultrasound. Other aims were to verify the risk of arthritis flare and to evaluate the effects of spa treatment on functional ability and on quality of life. METHODS Thirty-six patients with psoriatic arthritis, treated in the last 6 months with TNF inhibitors, were enrolled. After 1:1 randomisation, 18 patients (group A) underwent mud-bath therapy (12 mudpacks and 12 thermal baths), maintaining treatment with TNF inhibitors; 18 patients (group B) continued pharmacological therapy alone. CRP, PASI, DAS28, swollen and tender joint count, VAS pain, HAQ and SF-36 were evaluated at baseline (T0) and after 45 days (T1). Synovial inflammation detected by contrast-enhanced ultrasound, analysed by a software system, was also assessed. RESULTS A significant improvement in PASI (P<0.005), DAS28 (P<0.05), swollen joint count and tender joint count (P<0.001), and HAQ (P<0.001) between T0 and T1 was observed in group A. No patient underwent a flare-up of arthritis. Ultrasound videos demonstrated a significant appearance delay (P<0.05) and faster washout (P<0.02) of contrast dye in group A patients with respect to group B. CONCLUSIONS These data suggest a decrease of residual synovial inflammation and a beneficial clinical effect of spa therapy in psoriatic arthritis patients treated with TNF inhibitors.

Role of Radiologic Imaging in Genetic and Acquired Neuromuscular Disorders

Great technologic and clinical progress have been made in the last two decades in identifying genetic defects of several neuromuscular diseases, as Spinal Muscular Atrophy, genetic muscular dystrophies and other genetic myopathies. The diagnosis is usually challenging, due to great variability in genetic abnormalities and clinical phenotypes and the poor specificity of complementary analyses, i.e., serum creatine kinase (CK) and electrophysiology. Muscle biopsy represents the gold standard for the diagnosis of genetic neuromuscular diseases, but clinical imaging of muscle tissue is an important diagnostic tool to identify and quantifyies muscle damage. Radiologic imaging is, indeed, increasingly used as a diagnostic tool to describe patterns and the extent of muscle involvement, thanks to modern techniques that enable to definethe definition of degrees of muscle atrophy and changes in connective tissue. They usually grade the severity of the disease process with greater accuracy than clinical scores. Clinical imaging is more than complementary to perform muscle biopsy, especially as ultrasound scans are often mandatory to identify the muscle to be biopsied. We will here detail and provideWe will herein provide detailed examples of the radiologic methods that can be used in genetic and acquired neuromuscular disorders, stressing pros and cons. Key Words: Muscle Imaging, MRI, CT, genetic muscle disorders, myopathies, dystrophies

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.

Quantitative imaging by pixel-based contrast-enhanced ultrasound reveals a linear relationship between synovial vascular perfusion and the recruitment of pathogenic IL-17A-F + IL-23 + CD161 + CD4 + T helper cells in psoriatic arthritis joints

To develop quantitative imaging biomarkers of synovial tissue perfusion by pixel-based contrast-enhanced ultrasound (CEUS), we studied the relationship between CEUS synovial vascular perfusion and the frequencies of pathogenic T helper (Th)-17 cells in psoriatic arthritis (PsA) joints. Eight consecutive patients with PsA were enrolled in this study. Gray scale CEUS evaluation was performed on the same joint immediately after joint aspiration, by automatic assessment perfusion data, using a new quantification approach of pixel-based analysis and the gamma-variate model. The set of perfusional parameters considered by the time intensity curve includes the maximum value (peak) of the signal intensity curve, the blood volume index or area under the curve, (BVI, AUC) and the contrast mean transit time (MTT). The direct ex vivo analysis of the frequencies of SF IL17A-F+CD161+IL23+ CD4+ T cells subsets were quantified by fluorescence-activated cell sorter (FACS). In cross-sectional analyses, when tested for multiple comparison setting, a false discovery rate at 10%, a common pattern of correlations between CEUS Peak, AUC (BVI) and MTT parameters with the IL17A-F+IL23+ - IL17A-F+CD161+ - and IL17A-F+CD161+IL23+ CD4+ T cells subsets, as well as lack of correlation between both peak and AUC values and both CD4+T and CD4+IL23+ T cells, was observed. The pixel-based CEUS assessment is a truly measure synovial inflammation, as a useful tool to develop quantitative imaging biomarker for monitoring target therapeutics in PsA.

A comparison of region-based and pixel-based CEUS kinetics parameters in the assessment of arthritis

Inflammatory rheumatic diseases are leading causes of disability and constitute a frequent medical disorder, leading to inability to work, high comorbidity and increased mortality. The gold-standard for diagnosing and differentiating arthritis is based on patient conditions and radiographic findings, as joint erosions or decalcification. However, early signs of arthritis are joint effusion, hypervascularization and synovial hypertrophy. In particular, vascularization has been shown to correlate with arthritis’ destructive behavior, more than clinical assessment. Contrast Enhanced Ultrasound (CEUS) examination of the small joints is emerging as a sensitive tool for assessing vascularization and disease activity. The evaluation of perfusion pattern rely on subjective semi-quantitative scales, that are able to capture the macroscopic degree of vascularization, but are unable to detect the subtler differences in kinetics perfusion parameters that might lead to a deeper understanding of disease progression and a better management of patients. Quantitative assessment is mostly performed by means of the Qontrast software package, that requires the user to define a region of interest, whose mean intensity curve is fitted with an exponential function. We show that using a more physiologically motivated perfusion curve, and by estimating the kinetics parameters separately pixel per pixel, the quantitative information gathered is able to differentiate more effectively different perfusion patterns. In particular, we will show that a pixel-based analysis is able to provide significant markers differentiating rheumatoid arthritis from simil-rheumatoid psoriatic arthritis, that have non-significant differences in clinical evaluation (DAS28), serological markers, or region-based parameters.

Dynamic automated synovial imaging (DASI) for differential diagnosis of rheumatoid arthritis

Inflammatory rheumatic diseases are leading causes of disability and constitute a frequent medical disorder, leading to inability to work, high comorbidity and increased mortality. The gold-standard for diagnosing and differentiating arthritis is based on patient conditions and radiographic findings, as joint erosions or decalcification. However, early signs of arthritis are joint effusion, hypervascularization and synovial hypertrophy. In particular, vascularization has been shown to correlate with arthritis’ destructive behavior, more than clinical assessment. Contrast Enhanced Ultrasound (CEUS) examination of the small joints is emerging as a sensitive tool for assessing vascularization and disease activity. The evaluation of perfusion pattern rely on subjective semiquantitative scales, that are able to capture the macroscopic degree of vascularization, but are unable to detect the subtler differences in kinetics perfusion parameters that might lead to a deeper understanding of disease progression and a better management of patients. We show that after a kinetic analysis of contrast agent appearance, providing the quantitative features characterizing the perfusion pattern of the joint, it is possible to accurately discriminate RA from PSA by building a random forest classifier on the computed features. We compare its accuracy with the assessment performed by expert radiologist blinded of the diagnosis.

Data-Driven Learning to Detect Characteristic Kinetics in Ultrasound Images of Arthritis

Contrast Enhanced Ultrasound (CEUS) is a sensitive imaging technique to assess synovial vascularization and perfusion, allowing a pixel-wise perfusion quantification that can be used to distinguish different forms of disease and help their early detection. However, the high dimensionality of the perfusion parameter space prevents an easy understanding of the underlying pathological changes in the synovia. In order extract relevant clinical information, we present a data-driven method to identify the perfusions patterns characterizing the different types of arthritis, exploiting a sparse representation obtained from a dictionary of basis signals learned from the data.