To evaluate the impact of a novel, deep-learning-based image reconstruction (DLIR) algorithm on image quality in CT angiography of the aorta, we retrospectively analyzed 51 consecutive patients who underwent ECG-gated chest CT angiography and non-gated acquisition for the abdomen on a 256-dectector-row CT. Images were reconstructed with adaptive statistical iterative reconstruction (ASIR-V) and DLIR. Intravascular image noise, the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) were quantified for the ascending aorta, the descending thoracic aorta, the abdominal aorta and the iliac arteries. Two readers scored subjective image quality on a five-point scale. Compared to ASIR-V, DLIR reduced the median image noise by 51–54% for the ascending aorta and the descending thoracic aorta. Correspondingly, median CNR roughly doubled for the ascending aorta and descending thoracic aorta. There was a 38% reduction in image noise for the abdominal aorta and the iliac arteries, with a corresponding improvement in CNR. Median subjective image quality improved from good to excellent at all anatomical levels. In CT angiography of the aorta, DLIR substantially improved objective and subjective image quality beyond what can be achieved by state-of-the-art iterative reconstruction. This can pave the way for further radiation or contrast dose reductions.

With its standardized MRI datasets of the entire spine, the German National Cohort (GNC) has the potential to deliver standardized biometric reference values for intervertebral discs (VD), vertebral bodies (VB) and spinal canal (SC). To handle such large-scale big data, artificial intelligence (AI) tools are needed. In this manuscript, we will present an AI software tool to analyze spine MRI and generate normative standard values. 330 representative GNC MRI datasets were randomly selected in equal distribution regarding parameters of age, sex and height. By using a 3D U-Net, an AI algorithm was trained, validated and tested. Finally, the machine learning algorithm explored the full dataset (n = 10,215). VB, VD and SC were successfully segmented and analyzed by using an AI-based algorithm. A software tool was developed to analyze spine-MRI and provide age, sex, and height-matched comparative biometric data. Using an AI algorithm, the reliable segmentation of MRI datasets of the entire spine from the GNC was possible and achieved an excellent agreement with manually segmented datasets. With the analysis of the total GNC MRI dataset with almost 30,000 subjects, it will be possible to generate real normative standard values in the future.

Introduction: The chicken egg, with its in ovo compartments, is a widely used and popular animal model in experimental studies. This study aimed to quantify the volumes of the yolk/yolk sac, amniotic fluid, and chicken embryo in ovo using non-invasive ultra-high-field magnetic resonance imaging (UHF-MRI).Materials and methods: In total, 64 chicken eggs were examined using a 7 T UHF-MRI scanner, acquiring T2-weighted anatomical images of the entire egg from developmental day 1 to 16 (D1-D16). Four eggs were scanned each developmental day, and the volumes of the yolk/yolk sac, amniotic fluid, and embryo were quantitatively assessed.Results: UHF-MRI facilitated the in ovo quantitative assessment of the yolk/yolk sac starting from D1 and the embryo from D5 onward. The yolk/yolk sac volume increased from D1 to D6 before progressively decreasing until D14. The amniotic cavity could be detected on D6, with its fluid volume increasing steadily until D14. The embryo’s volume increased consistently throughout the developmental period, reaching its peak at D16.Discussion: UHF-MRI allows in vivo assessment of embryonic development, providing non-invasive, longitudinal insights into the volumes of the yolk/yolk sac, amniotic fluid, and chicken embryo. The investigation method described in this study may provide a standardized model for biomedical research in the developing chicken embryo, supporting various experimental applications.

Objectives: To evaluate the impact of a novel deep learning-based image reconstruction algorithm on the image quality in CT angiography of the aorta. Methods: We retrospectively analyzed 51 consecutive patients (median age 69 years, 19 women) who underwent ECG-gated chest CT angiography and non-gated acquisition for the abdomen on a 256-dectector-row CT. Images were reconstructed

Background/Objectives. Automated lateral ventricle volumetry is increasingly used in population-based neuroimaging, but correlation between methods does not establish agreement of absolute volumes. We quantified agreement and calibration between FreeSurfer and a visually quality-controlled FSL/FAST–ALVIN lateral ventricle workflow within the Study of Health in Pomerania (SHIP). Methods. This cross-sectional agreement-and-calibration study included 2988 SHIP participants with visually accepted FSL/FAST–ALVIN total lateral ventricle volumes; paired FreeSurfer data were available for 1913 participants. FSL/FAST–ALVIN was treated as the study reference scale rather than biological ground truth. Agreement was assessed using Pearson and Spearman correlations, Bland–Altman analysis, log-ratio agreement, Lin’s concordance correlation coefficient, and a two-way mixed-effects single-measure absolute agreement intraclass correlation coefficient. Directional calibration models predicted FSL/FAST–ALVIN volume from FreeSurfer volume and were internally validated using 2000 bootstrap resamples. Results. In the paired sample, volumes were almost perfectly associated (Pearson r = 0.9978; Spearman ρ = 0.9974), but FreeSurfer yielded systematically lower values (mean FreeSurfer-minus-FSL bias, −3.02 mL; 95% limits of agreement, −4.52 to −1.53 mL; geometric mean FreeSurfer/FSL ratio, 0.844). Lin’s concordance coefficient and the absolute agreement ICC were both 0.9598. Calibration was strong but workflow-specific: FSL/FAST–ALVIN volume = 2.611 + 1.0210 × FreeSurfer volume (R2 = 0.9955; optimism-corrected RMSE = 0.732 mL). Conclusions. FreeSurfer and visually quality-controlled FSL/FAST–ALVIN preserved participant ranking extremely well but were not directly interchangeable as absolute measurements. Cross-workflow comparisons require explicit method reporting, formal agreement analysis, and calibration to the intended measurement scale; the equation should not be used as a universal conversion formula outside comparable acquisition, segmentation, QC and software settings.

Currently, the effect of prenatal ultrasound on foetal development is intensively discussed and the guidelines for prenatal diagnostics have been changed. However, data supporting these concerns are scarce. Therefore, we used an established in ovo model of the chicken embryo to investigate cell proliferation and apoptosis within the retina. A total of 21 chicken eggs were fenestrated on Day 5 and allocated to either the control (n = 8) or exposition group (n = 13). The exposition group was treated with pulsed‐wave Doppler ultrasound (PWD) for 10 min while controls remained without treatment. After subsequent incubation (6–48 h), the eggs were sacrificed, and chicken embryos were examined morphologically (HE‐staining) and immunohistochemically. Counting of apoptotic and proliferating cells per retina was performed using antibodies specific for phospho‐histone‐H3 and active caspase‐3 in combination with a biotin‐labelled secondary antibody and peroxidase conjugated avidin–biotin complex for chromogenic detection. Due to a rather low number of specimens at each time point after ultrasound exposition, we neglected the effects of incubation time and focused on treatment effects. This approach revealed that the median number of proliferating cells is reduced after 10 min of exposure to PWD (569 vs. 766), while the number of apoptotic cells is fairly comparable between groups (5 vs. 6). Our data contribute to a better understanding of prenatal US on foetal development by suggesting that PWD could have an impact on the number of proliferating cells in the developing chicken retina and therefore justify further investigations.