While morphologic magnetic resonance imaging (MRI) is the imaging modality of choice for the evaluation of ligamentous wrist injuries, it is merely static and incapable of diagnosing dynamic wrist instability. Based on real-time MRI and algorithm-based image post-processing in terms of convolutional neural networks (CNNs), this study aims to develop and validate an automatic technique to quantify wrist movement. A total of 56 bilateral wrists (28 healthy volunteers) were imaged during continuous and alternating maximum ulnar and radial abduction. Following CNN-based automatic segmentations of carpal bone contours, scapholunate and lunotriquetral gap widths were quantified based on dedicated algorithms and as a function of wrist position. Automatic segmentations were in excellent agreement with manual reference segmentations performed by two radiologists as indicated by Dice similarity coefficients of 0.96 ± 0.02 and consistent and unskewed Bland–Altman plots. Clinical applicability of the framework was assessed in a patient with diagnosed scapholunate ligament injury. Considerable increases in scapholunate gap widths across the range-of-motion were found. In conclusion, the combination of real-time wrist MRI and the present framework provides a powerful diagnostic tool for dynamic assessment of wrist function and, if confirmed in clinical trials, dynamic carpal instability that may elude static assessment using clinical-standard imaging modalities.

In recent years, much research evaluating the radiographic destruction of finger joints in patients with rheumatoid arthritis (RA) using deep learning models was conducted. Unfortunately, most previous models were not clinically applicable due to the small object regions as well as the close spatial relationship. In recent years, a new network structure called RetinaNets, in combination with the focal loss function, proved reliable for detecting even small objects. Therefore, the study aimed to increase the recognition performance to a clinically valuable level by proposing an innovative approach with adaptive changes in intersection over union (IoU) values during training of Retina Networks using the focal loss error function. To this end, the erosion score was determined using the Sharp van der Heijde (SvH) metric on 300 conventional radiographs from 119 patients with RA. Subsequently, a standard RetinaNet with different IoU values as well as adaptively modified IoU values were trained and compared in terms of accuracy, mean average accuracy (mAP), and IoU. With the proposed approach of adaptive IoU values during training, erosion detection accuracy could be improved to 94% and an mAP of 0.81 ± 0.18. In contrast Retina networks with static IoU values achieved only an accuracy of 80% and an mAP of 0.43 ± 0.24. Thus, adaptive adjustment of IoU values during training is a simple and effective method to increase the recognition accuracy of small objects such as finger and wrist joints.

Objective: To establish and optimize a stable 3 Tesla (T) glycosaminoglycan chemical exchange saturation transfer (gagCEST) imaging protocol for assessing the articular cartilage of the tibiotalar joint in healthy volunteers and patients after a sustained injury to the ankle. Methods: Using Bloch–McConnell simulations, we optimized the sequence protocol for a 3 T MRI scanner for maximum gagCEST effect size within a clinically feasible time frame of less than 07:30 min. This protocol was then used to analyze the gagCEST effect of the articular cartilage of the tibiotalar joint of 17 healthy volunteers and five patients with osteochondral lesions of the talus following ankle trauma. Reproducibility was tested with the intraclass correlation coefficient. Results: The mean magnetization transfer ratio asymmetry (MTR_asym), i.e., the gagCEST effect size, was significantly lower in patients than in healthy volunteers (0.34 ± 1.9% vs. 1.49 ± 0.11%; p < 0.001 [linear mixed model]). Intra- and inter-rater reproducibility was excellent with an average measure intraclass correlation coefficient (ICC) of 0.97 and a single measure ICC of 0.91 (p < 0.01). Discussion: In this feasibility study, pre-morphological tibiotalar joint cartilage damage was quantitatively assessable on the basis of the optimized 3 T gagCEST imaging protocol that allowed stable quantification gagCEST effect sizes across a wide range of health and disease in clinically feasible acquisition times.

Background: Because of overlapping phenotypical presentations, the diagnostic differentiation of rheumatoid arthritis (RA) and psoriatic arthritis (PsA) remains challenging. Thus, this study aimed to examine the diagnostic value of distinct imaging features obtained by high-resolution 3-T MRI for the diagnostic differentiation. Materials and methods: Seventeen patients with PsA and 28 patients with RA were imaged at high resolution using 3-T MRI scanners and a dedicated 16-channel hand coil. All images were analyzed according to the outcome measures in rheumatology clinical trials’ (OMERACT) RAMRIS (Rheumatoid Arthritis Magnetic Resonance Imaging Score) and PsAMRIS (Psoriatic Arthritis Magnetic Resonance Imaging Score) for the presence and intensity of synovitis, flexor tenosynovitis, bone edema, bone erosion, periarticular inflammation, bone proliferation, and joint space narrowing. Next, odds ratios (OR) were calculated to determine the strength of the associations between these imaging features, demographic characteristics, and the outcome RA vs. PsA. Results: PsA could be differentiated from RA by extracapsular inflammatory changes (PsAMRIS sub-score “periarticular inflammation”), with low odds for the presence of RA (OR of 0.06, p < 0.01) at all metacarpophalangeal (MCP) joints. A prediction model informed by the items that were strongest associated with the presence of RA or PsA demonstrated excellent differentiating capability with an area under the curve of 98.1%. Conclusion: High-resolution imaging is beneficial for the identification of relevant imaging features that may assist the clinical differentiation of inflammatory conditions of the hand. At the MCP level, extracapsular inflammatory changes were strongly associated with PsA and may consequently allow the imaging differentiation of PsA and RA.

The aim of the study was to evaluate a simplified version of the Rheumatoid Arthritis Magnetic Resonance Imaging Score (RAMRIS) for five joints of the hand (RAMRIS-5) in patients with early rheumatoid arthritis (RA) before and after the initiation of methotrexate (MTX) therapy using high-resolution, 3-T magnetic resonance imaging (MRI). Twenty-eight patients with a seropositive, early RA (disease duration of less than 6 months (range 2–23 weeks)) according to 2010 American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) criteria (mean age 56.8 years, range 39–74) were prospectively assessed with a baseline investigation including clinical assessment (disease activity score of 28 joints (DAS-28) and C-reactive protein (CRP)) and 3-T MRI of the clinically dominant hand. Follow-up visits were performed 3 and 6 months after initiation of a MTX therapy at baseline. MRI scans were analyzed in accordance with RAMRIS and the simplified RAMRIS-5. DAS-28 and CRP decreased significantly after initiation of MTX therapy. Even though erosion scores increased over time, RAMRIS and RAMRIS-5 also decreased significantly after the start of therapy. There was a strong correlation between the total RAMRIS-5 and RAMRIS at baseline (r = 0.838; P <0.001) and follow-up (3 months: r = 0.876; P <0.001; 6 months: r = 0.897; P <0.001). In the short term (3-month follow-up), RAMRIS and RAMRIS-5 demonstrated similar ability to detect changes for all subgroups (bone edema, erosion, and synovitis). In the long-term comparison (6-month follow-up), RAMRIS-5 also showed similar effectiveness when detecting changes in bone edema and erosion compared with RAMRIS. Deviations occurred regarding only synovitis, where change was slightly higher in RAMRIS-5: SRM (RAMRIS) = 0.07 ± 0.14; SRM (RAMRIS-5) = 0.34 ± 0.06. Three-Tesla MRI-based RAMRIS-5 is a simplified and resource-saving RAMRIS score which compares favorably with the RAMRIS when detecting changes in early RA. Even though there is a slight abbreviation between RAMRIS-5 and the original score regarding the change of synovitis, it may be used for diagnosis and therapy monitoring in follow-up evaluations.

Background: Even though cartilage loss is a known feature of psoriatic arthritis (PsA), little is known about its role in the pathogenesis of PsA. Using delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC) as a non-invasive marker of the tissue’s proteoglycan content, such early (i.e., pre-morphological) changes have been associated with inflammation in rheumatoid arthritis (RA). Yet, this association has not been studied before in PsA. Methods: The metacarpophalangeal (MCP), proximal interphalangeal (PIP), and distal interphalangeal (DIP) joints of 17 patients with active PsA were evaluated by high-resolution clinical standard morphological and dGEMRIC sequences using a 3T MRI scanner (Magnetom Skyra, Siemens) and a dedicated 16-channel hand coil. Images were analyzed by two independent raters for dGEMRIC indices, PsA MRI scores (PsAMRIS), and total cartilage thickness (TCT). Kendall tau correlation coefficients (τ) were calculated. Results: We found significant negative correlations between dGEMRIC indices and total PsAMRIS (τ = − 0.5, p = 0.012), synovitis (τ = − 0.56, p = 0.006), flexor tenosynovitis (τ = − 0.4, p = 0.049), and periarticular inflammation (τ = − 0.72, p < 0.001). Significant positive correlations were found between TCT and dGEMRIC indices at all joint levels (τ = 0.43, p < 0.001). No significant correlations were determined between dGEMRIC indices and bone erosion, bone edema, or bone proliferation. Conclusion: In PsA, proteoglycan loss as assessed by dGEMRIC is associated with periarticular inflammation, synovitis, and flexor tenosynovitis, but not with bone erosion or proliferation. Thereby, these findings contribute to in vivo concepts of the disease’s pathophysiology. Beyond morphology, advanced MRI techniques may be used to assess cartilage composition in PsA and to identify early changes in the cartilage as an imaging biomarker with potential application in detection, monitoring, and prediction of outcomes of PsA. Trial registration: 2014123117, December 2014.