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Background: Morphology and glenoid involvement determine the necessity of surgical management in scapula fractures. While being present in only a small share of patients with shoulder trauma, numerous classification systems have been in use over the years for categorization of scapula fractures. The purpose of this study was to evaluate the established AO/OTA classification in comparison to the classification system of Euler and Rüedi (ER) with regard to interobserver reliability and confidence in clinical practice., Methods: Based on CT imaging, 149 patients with scapula fractures were retrospectively categorized by two trauma surgeons and two radiologists using the classification systems of ER and AO/OTA. To measure the interrater reliability, Fleiss kappa (κ) was calculated independently for both fracture classifications. Rater confidence was stated subjectively on a five-point scale and compared with Wilcoxon signed rank tests. Additionally, we computed the intraclass correlation coefficient (ICC) based on absolute agreement in a two-way random effects model to assess the diagnostic confidence agreement between observers., Results: In scapula fractures involving the glenoid fossa, interrater reliability was substantial (κ = 0.722; 95% confidence interval [CI] 0.676-0.769) for the AO/OTA classification in contrast to moderate agreement (κ = 0.579; 95% CI 0.525-0.634) for the ER classification system. Diagnostic confidence for intra-articular fracture patterns was superior using the AO/OTA classification compared to ER (p < 0.001) with higher confidence agreement (ICC: 0.882 versus 0.831). For extra-articular fractures, ER (κ = 0.817; 95% CI 0.771-0.863) provided better interrater reliability compared to AO/OTA (κ = 0.734; 95% CI 0.692-0.776) with higher diagnostic confidence (p < 0.001) and superior agreement between confidence ratings (ICC: 0.881 versus 0.912)., Conclusions: The AO/OTA classification is most suitable to categorize intra-articular scapula fractures with glenoid involvement, whereas the classification system of Euler and Rüedi appears to be superior in extra-articular injury patterns with fractures involving only the scapula body, spine, acromion and coracoid process., (© 2022. The Author(s).)

Background: Functional lung MRI techniques are usually associated with time-consuming post-processing, where manual lung segmentation represents the most cumbersome part. The aim of this study was to investigate whether deep learning-based segmentation of lung images which were scanned by a fast UTE sequence exploiting the stack-of-spirals trajectory can provide sufficiently good accuracy for the calculation of functional parameters., Methods: In this study, lung images were acquired in 20 patients suffering from cystic fibrosis (CF) and 33 healthy volunteers, by a fast UTE sequence with a stack-of-spirals trajectory and a minimum echo-time of 0.05 ms. A convolutional neural network was then trained for semantic lung segmentation using 17,713 2D coronal slices, each paired with a label obtained from manual segmentation. Subsequently, the network was applied to 4920 independent 2D test images and results were compared to a manual segmentation using the Sørensen-Dice similarity coefficient (DSC) and the Hausdorff distance (HD). Obtained lung volumes and fractional ventilation values calculated from both segmentations were compared using Pearson's correlation coefficient and Bland Altman analysis. To investigate generalizability to patients outside the CF collective, in particular to those exhibiting larger consolidations inside the lung, the network was additionally applied to UTE images from four patients with pneumonia and one with lung cancer., Results: The overall DSC for lung tissue was 0.967 ± 0.076 (mean ± standard deviation) and HD was 4.1 ± 4.4 mm. Lung volumes derived from manual and deep learning based segmentations as well as values for fractional ventilation exhibited a high overall correlation (Pearson's correlation coefficent = 0.99 and 1.00). For the additional cohort with unseen pathologies / consolidations, mean DSC was 0.930 ± 0.083, HD = 12.9 ± 16.2 mm and the mean difference in lung volume was 0.032 ± 0.048 L., Conclusions: Deep learning-based image segmentation in stack-of-spirals based lung MRI allows for accurate estimation of lung volumes and fractional ventilation values and promises to replace the time-consuming step of manual image segmentation in the future.

Background: To increase the image quality of end-expiratory and end-inspiratory phases of retrospective respiratory self-gated 4D MRI data sets using non-rigid image registration for improved target delineation of moving tumors., Methods: End-expiratory and end-inspiratory phases of volunteer and patient 4D MRI data sets are used as targets for non-rigid image registration of all other phases using two different registration schemes: In the first, all phases are registered directly (dir-Reg) while next neighbors are successively registered until the target is reached in the second (nn-Reg). Resulting data sets are quantitatively compared using diaphragm and tumor sharpness and the coefficient of variation of regions of interest in the lung, liver, and heart. Qualitative assessment of the patient data regarding noise level, tumor delineation, and overall image quality was performed by blinded reading based on a 4 point Likert scale., Results: The median coefficient of variation was lower for both registration schemes compared to the target. Median dir-Reg coefficient of variation of all ROIs was 5.6% lower for expiration and 7.0% lower for inspiration compared with nn-Reg. Statistical significant differences between the two schemes were found in all comparisons. Median sharpness in inspiration is lower compared to expiration sharpness in all cases. Registered data sets were rated better compared to the targets in all categories. Over all categories, mean expiration scores were 2.92 ± 0.18 for the target, 3.19 ± 0.22 for nn-Reg and 3.56 ± 0.14 for dir-Reg and mean inspiration scores 2.25 ± 0.12 for the target, 2.72 ± 215 0.04 for nn-Reg and 3.78 ± 0.04 for dir-Reg., Conclusions: In this work, end-expiratory and inspiratory phases of a 4D MRI data sets are used as targets for non-rigid image registration of all other phases. It is qualitatively and quantitatively shown that image quality of the targets can be significantly enhanced leading to improved target delineation of moving tumors.

Background: Contrast-enhanced cardiovascular magnetic resonance angiography (CE-CMRA) is the established imaging modality for patients with Marfan syndrome requiring life-long annual aortic imaging before and after aortic root replacement. Contrast-free CMRA techniques avoiding side-effects of contrast media are highly desirable for serial imaging but have not been evaluated in the postoperative setup of Marfan patients. The purpose of this study was to assess the feasibility of non-contrast balanced steady-state free precession (bSSFP) magnetic resonance imaging for aortic monitoring of postoperative patients with Marfan syndrome., Methods: Sixty-four adult Marfan patients after aortic root replacement were prospectively included. Fourteen patients (22%) had a residual aortic dissection after surgical treatment of type A dissection. bSSFP imaging and CE-CMRA were performed at 1.5 Tesla. Two radiologists evaluated the images regarding image quality (1 = poor, 4 = excellent), artifacts (1 = severe, 4 = none) and aortic pathologies. Readers measured the aortic diameters at defined levels in both techniques. Statistics included observer agreement for image scoring and diameter measurements and ROC analyses for comparison of the diagnostic performance of bSSFP and CE-CMRA., Results: Both readers observed no significant differences in image quality between bSSFP and CE-CMRA and found a median image quality score of 4 for both techniques (all p > .05). No significant differences were found regarding the frequency of image artifacts in both sequences (all p > .05). Sensitivity and specificity for detection of aortic dissections was 100% for both readers and techniques. Compared to bSSFP imaging, CE-CMRA resulted in higher diameters (mean bias, 0.9 mm; p < .05). The inter-observer biases of diameter measurements were not significantly different (all p > .05), except for the distal graft anastomosis (p = .001). Using both techniques, the readers correctly identified a graft suture dehiscence with aneurysm formation requiring surgery., Conclusion: Unenhanced bSSFP CMR imaging allows for riskless aortic monitoring with high diagnostic accuracy in Marfan patients after aortic root surgery.