Rationale and objectives: Zero echo time (ZTE) is an advanced MRI technique providing CT-like images of mineralized tissues. This study evaluates the impact of deep learning (DL)–based reconstruction with chemical shift correction (DLCSC) on osseous depiction in ZTE MRI of the lumbar spine, compared to standard DL and non-DL reconstruction. Methods: This retrospective, single-center study included 38 patients undergoing 3 T ZTE MRI of the lumbar spine. K-space data were reconstructed using three methods: non-DL, standard DL, and a prototype DLCSC algorithm. Quantitative image sharpness, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were analyzed using repeated-measures ANOVA. Two radiologists independently rated pathology-related criteria ( n = 22) and bone depiction quality ( n = 38) on a 4-point scale. Ordinal data were analyzed using the Friedman test, and inter-reader agreement was assessed with weighted Cohen’s kappa. Results: DLCSC images yielded quantitatively sharper images compared to non-DL ( p = 0.010) and standard DL ( p < 0.001). SNR and CNR were significantly higher in DLCSC and DL than in non-DL ( p < 0.001). In the qualitative assessment, mean scores for all criteria of pathologies and bone depiction quality improved significantly from non-DL and DL to DLCSC ( p < 0.001). There was no evidence of differences in classification of pathologies ( p > 0.05). Inter-reader agreement ranged from substantial to almost perfect ( κ = 0.867–0.901). Conclusion: DLCSC image reconstruction of ZTE MRI can improve bone depiction of the lumbar spine compared to standard DL and non-DL reconstruction.

To assess differential MRI characteristics of cortical lesions in patients with and without inflammatory arthritis, distinguishing erosions from non-inflammatory lesions. Lesions were classified into those associated with inflammatory arthritis (IA) (n = 177) and without (n = 148). Two radiologists measured lesion diameter and cortical defect width. Linear mixed models were used for continuous outcome variables and generalized estimating equations for binary outcomes and all predictive modeling to estimate odds ratios and predicted probabilities. This retrospective study included hand MRI exams of 161 patients (83 female, median age (IQR) 54 (39-69)), with 325 cortical lesions. Mean (±standard deviation) cortical defect width (2.74 ± 1.69 mm vs. 1.81 ± 1.20 mm; p < 0.001) and ratio of cortical defect width to lesion diameter (0.61 ± 0.36 vs. 0.37 ± 0.21; p < 0.001) was significantly greater in IA. Blurred delineation (42% vs. 18%; OR = 3.42, p < 0.001), bone marrow edema (BME) (31% vs. 11%; OR = 3.45, p < 0.001) and signal heterogeneity (51% vs. 36%, OR = 1.80, p < 0.001) were significantly more common in IA. Using the ratio and BME to predict IA yielded a cross-validated AUC of 0.721 (95% CI: 0.535-0.788), with a sensitivity of 58% and a specificity of 85% at an optimal threshold. These results suggest that using a quantitative morphological approach can aid differentiation of erosions from non-inflammatory lesions in hand MRI.

Objectives: The transition from in-person to online formats for clinical case discussions has transformed the way medical professionals collaborate and share knowledge. The current pilot study investigates the potential impact of this shift by comparing the experiences of specialists and residents who had practiced both online and in-person pituitary tumor boards compared to the experiences of those who only experienced the online format. Methods: A cohort of 15 participants, including 10 specialists and 5 residents, provided insights through structured surveys and free-text responses. Results: The results indicate differences in the perception of the duration and participation dynamics in online pituitary tumor boards between both groups, with online boards facilitating the discussion of more cases. Overall participant engagement was mostly perceived as the same; a subset reported reduced perceived engagement, particularly among senior professionals. Conclusions: These findings highlight how prior experience can affect the perceived effectiveness, level of engagement and duration of online formats, providing valuable hypothesis-generating input for optimizing future medical board meetings.

Background/Objectives: This study assesses the outcomes of integrating deep learning-augmented zero echo time (ZTE DL) MRI sequences into standard MRI protocols for assessment of the hands and feet. Methods: In this single-center, retrospective study, a standard MRI protocol of hands and feet at 1.5 T was compared with the same protocol with an added ZTE DL sequence. Pathological changes in the bone, including subcortical sclerosis, osteophytes, joint space narrowing and fractures, were rated as present or absent. Indeterminate intraosseous lesions (erosions/ganglia) and indeterminate extraosseous lesions (ossicles/soft tissue calcifications) were additionally assessed on a semi-quantitative 4-point Likert scale. Diagnostic confidence was rated as low, moderate, or high. Standard MRI vs. ZTE-DL-augmented MRI comparisons were evaluated using a mixed-effects model with reader as a random effect, and X-ray vs. ZTE-DL-augmented MRI comparisons using a paired Wilcoxon signed-rank test. Interreader agreement (two readers) was assessed using Kappa statistics. Results: The cohort encompassed 59 datasets (feet = 22, hands = 37) of 40 patients with an average age of 51.79 (SD ± 11.84) years (58% women (n = 34), 42% men (n = 25)). Additional ZTE DL sequences resulted in similar findings, but with higher diagnostic confidence for assessment of bone changes when compared to conventional MR sequences (p-values < 0.05) and, overall, when compared to radiographs in a subgroup (p < 0.05 for five of six pathologic bone changes). Interreader agreement of diagnostic confidence was moderate to substantial (kappa 0.59–0.71). Conclusions: Addition of ZTE DL sequences to standard MRI protocols of hands and feet at 1.5 T demonstrated similar findings in the assessment of pathological bone changes, but with higher diagnostic confidence compared to conventional MR sequences and radiographs. However, further validation against a reference standard is required to determine diagnostic accuracy.