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.

Sodium MRI has the potential to depict cartilage health accurately, but synovial fluid can influence the estimation of sodium parameters of cartilage. Therefore, this study aimed to reduce the impact of synovial fluid to render the quantitative compositional analyses of cartilage tissue technically more robust. Two dedicated protocols were applied for determining sodium ${\mathrm{T}}_1$ and ${\mathrm{T}}_2^{*}$ relaxation times. For each protocol, data were acquired from 10 healthy volunteers and one patient with patellar cartilage damage. Data recorded with multiple repetition times for ${\mathrm{T}}_1$ measurement and multi-echo data acquired with an additional inversion recovery pulse for ${\mathrm{T}}_2^{*}$ measurement were analysed using biexponential models to differentiate longitudinal relaxation components of cartilage (${\mathrm{T}}_{1,\mathrm{car}}$) and synovial fluid (${\mathrm{T}}_{1,\mathrm{syn}}$), and short (${\mathrm{T}}_{2\mathrm{s}}^{*}$) from long (${\mathrm{T}}_{2\mathrm{l}}^{*}$) transversal relaxation components. Sodium relaxation times and concentration estimates in patellar cartilage were successfully determined: ${\mathrm{T}}_{1,\mathrm{car}}$ = 14.5 ± 0.7 ms; ${\mathrm{T}}_{1,\mathrm{syn}}$ = 37.9 ± 2.9 ms; c(${\mathrm{T}}_1$-protocol) = 200 ± 48 mmol/L; ${\mathrm{T}}_{2\mathrm{s}}^{*}$ = 0.4 ± 0.1 ms; ${\mathrm{T}}_{2\mathrm{l}}^{*}$ = 12.6 ± 0.7 ms; c(${\mathrm{T}}_2^{*}$-protocol) = 215 ± 44 mmol/L for healthy volunteers. In conclusion, a robust determination of sodium relaxation times is possible at a clinical field strength of 3T to quantify sodium concentrations, which might be a valuable tool to determine cartilage health.

Background: Clinical-standard morphologic magnetic resonance imaging (MRI) is limited in the refined diagnosis of posterior cruciate ligament (PCL) injuries. Quantitative MRI sequences such as ultrashort echo-time (UTE)-T2* mapping or conventional T2* mapping have been theorized to quantify ligament (ultra-) structure and integrity beyond morphology. This study evaluates their diagnostic potential in identifying and differentiating partial and complete PCL injuries in a standardized graded injury model. Methods: Ten human cadaveric knee joint specimens were imaged on a clinical 3.0 T MRI scanner using morphologic, conventional T2* mapping, and UTE-T2* mapping sequences before and after standardized arthroscopic partial and complete PCL transection. Following manual segmentation, quantitative T2* and underlying texture features (i.e., energy, homogeneity, and variance) were analyzed for each specimen and PCL condition, both for the entire PCL and its subregions. For statistical analysis, Friedman’s test followed by Dunn’s multiple comparison test was used against the level of significance of P≤0.01. Results: For the entire PCL, T2* was significantly increased as a function of injury when acquired with the UTE-T2* sequence [entire PCL: 11.1±3.1 ms (intact); 10.9±4.6 ms (partial); 14.3±4.9 ms (complete); P<0.001], but not when acquired with the conventional T2* sequence [entire PCL: 10.0±3.2 ms (intact); 11.4±6.2 ms (partial); 15.5±7.8 ms (complete); P=0.046]. The PCL subregions and texture variables showed variable changes indicative of injury-associated disorganization. Conclusions: In contrast to the conventional T2* mapping, UTE-T2* mapping is more receptive in the detection of structural damage of the PCL and allows quantitative assessment of ligament (ultra-)structure and integrity that may help to improve diagnostic differentiation of distinct injury states. Once further substantiated beyond the in-situ setting, UTE-T2* mapping may refine diagnostic evaluation of PCL injuries and -possibly- monitor ligament healing, ageing, degeneration, and inflammation.

Currently, clinical indications for the application of gadolinium-based contrast agents (GBCA) in magnetic resonance imaging (MRI) are increasingly being questioned. Consequently, this study aimed to evaluate the additional diagnostic value of contrast enhancement in MRI of the hand in patients with rheumatoid arthritis (RA). Thirty-one patients with RA (mean age, 50 ± 14 years (range, 18–72 years)) underwent morphologic MRI scans on a clinical 3 T scanner. MRI studies were analyzed based on (1) the Rheumatoid Arthritis Magnetic Resonance Imaging Score (RAMRIS) and (2) the GBCA-free RAMRIS version, termed RAMRIS Sine-Gadolinium-For-Experts (RAMRIS-SAFE), in which synovitis and tenosynovitis were assessed using the short-tau inversion-recovery sequence instead of the post-contrast T1-weighted sequence. The synovitis subscores in terms of Spearman’s ρ, as based on RAMRIS and RAMRIS-SAFE, were almost perfect (ρ = 0.937; p < 0.001), while the tenosynovitis subscores were less strongly correlated (ρ = 0.380 p = 0.035). Correlation between the total RAMRIS and RAMRIS-SAFE was also almost perfect (ρ = 0.976; p < 0.001). Inter-rater reliability in terms of Cohen’s κ was high (0.963 ≤ κ ≤ 0.925). In conclusion, RAMRIS-SAFE as the GBCA-free version of the well-established RAMRIS is a patient-friendly and resource-efficient alternative for assessing disease-related joint changes in RA. As patients with RA are subject to repetitive GBCA applications, non-contrast imaging protocols should be considered.

Using glycosaminoglycan Chemical Exchange Saturation Transfer (gagCEST) magnetic resonance imaging (MRI), this study comparatively evaluated the GAG contents of lumbar intervertebral disks (IVDs) of patients with non-specific low back pain (nsLBP), radiculopathy, and asymptomatic volunteers to elucidate the association of clinical manifestation and compositional correlate. A total of 18 patients (mean age 57.5 ± 22.5 years) with radiculopathy, 16 age-matched patients with chronic nsLBP and 20 age-matched volunteers underwent standard morphologic and compositional gagCEST MRI on a 3T scanner. In all cohorts, GAG contents of lumbar IVDs were determined using gagCEST MRI. An assessment of morphologic IVD degeneration based on the Pfirrmann classification and T2-weighted sequences served as a reference. A linear mixed model adjusted for multiple confounders was used for statistical evaluation. IVDs of patients with nsLBP showed lower gagCEST values than those of volunteers (nsLBP: 1.3% [99% confidence intervals (CI): 1.0; 1.6] vs. volunteers: 1.9% [99% CI: 1.6; 2.2]). Yet, IVDs of patients with radiculopathy (1.8% [99% CI: 1.4; 2.1]) were not different from patients with nsLBP or volunteers. In patients with radiculopathy, IVDs directly adjacent to IVD extrusions demonstrated lower gagCEST values than distant IVDs (adjacent: 0.9% [99% CI: 0.3; 1.5], distant: 2.1% [99% CI: 1.7; 2.5]). Advanced GAG depletion in nsLBP and directly adjacent to IVD extrusions in radiculopathy indicates close interrelatedness of clinical pathology and compositional degeneration.