Obesity increases the risk of cardiovascular diseases (CVD), however, whether adipose tissue relates to dyslipidemia, and consequently to cardiovascular events remains unknown. Thus, we investigated the association of adipose tissue with circulating lipoproteins and triglycerides (TG) in subjects without CVD. 384 participants from the KORA-MRI study (mean age 56.2 ± 9.2 years; 41.9% female) underwent whole-body 3T-MRI. Visceral (VAT) and subcutaneous adipose tissue (SAT) derived from T1-DIXON-sequence using a semi-automatic algorithm. Total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), and TG were measured. Linear regression was applied to examine the relationships between adipose tissue, circulating lipoproteins, and TG, adjusting for risk factors. VAT was associated with total cholesterol (per SD increase) (ß = 0.39, p < 0.001). Total adipose tissue (TAT) and VAT were inversely associated with HDL (ß = −0.09, p = 0.009; ß = −0.14, p < 0.001), and positively associated with LDL (ß = 0.32, p < 0.001; ß = 0.37, p < 0.001). All adipose tissues were associated with TG (ß = 0.20, p < 0.001; ß = 0.27, p < 0.001; ß = 0.11, p = 0.004). Stratified analysis by sex and body mass index (BMI) was confirmatory in women and in individuals with BMI < 30. Our results suggest that adipose tissue plays an important role in increasing CVD risk independent of BMI, whereas gender imbalance may be explained by accurate characterization and quantification of adipose tissue.

Since the approval of the first dual-source photon-counting detector CT (PCD-CT) in the fall of 2021, significant insights have been gained in its application for cardiovascular imaging. This review aims to provide a comprehensive overview of the current state of knowledge and the growing body of research literature, illustrating innovative applications and perspectives through case examples.We conducted a structured literature review, identifying relevant studies via Google Scholar and PubMed, using the keywords “photon-counting detector”, “cardiovascular CT”, “cardiac CT”, and “ultra-high-resolution CT”. We analyzed studies published since January 2015. Additionally, we integrated our own clinical experiences and case examples.In addition to the well-known benefit of increased temporal resolution offered by dual-source scanners, dual-source PCD-CT provides three key advantages: 1) Optimized geometric dose efficiency with an improved contrast-to-noise ratio, 2) intrinsic spectral sensitivity, and 3) the ability for ultrahigh-resolution CT. This technology enables improved image quality or radiation dose reduction in established cardiovascular protocols. Its use in non-invasive cardiac diagnostics for obese patients, those with a high plaque burden, or after stent implantation appears technically feasible, potentially expanding the scope of CT. The spectral sensitivity also allows tailored image acquisition, reducing metallic artifacts and contrast agent doses in patients with renal impairment. Early studies and clinical experience support these potential applications of PCD-CT in cardiovascular diagnostics, suggesting workflow optimization and improved patient management.However, challenges remain, including high costs, large data volumes, somewhat longer reconstruction times, and technical difficulties in combining spectral sensitivity with ultra-high resolution. Prospective randomized studies with clinical endpoints are lacking to confirm the clear advantage over conventional scanners. Future research should focus on endpoint-based studies and robust cost-benefit analyses to evaluate the potential of this technology and facilitate its evidence-based integration in clinical practice.

Reduced iodine loads for computed tomography (CT)-based vascular assessment prior to transcatheter aortic valve implantation (TAVI) may be feasible in conjunction with a spectral detector CT scanner. This prospective single-center study considered 100 consecutive patients clinically referred for pre-TAVI CT. They were examined on a dual-layer detector CT scanner to obtain an ECG-gated cardiac scan and a non-ECG-gated aortoiliofemoral scan. Either a standard contrast media (SCM) protocol using 80 mL Iohexol 350 mgI/mL (iodine load: 28 gI) or a body-mass-index adjusted reduced contrast media (RCM) protocol using 40–70 mL Iohexol 350 mgI/mL (iodine load: 14–24.5 gI) were employed. Conventional images and virtual monoenergetic images at 40–80 keV were reconstructed. A threshold of 250 HU was set for sufficient attenuation along the arterial access pathway. A qualitative assessment used a five-point Likert scale. Sufficient attenuation in the thoracic aorta was observed for all patients in both groups using conventional images. In the abdominal, iliac, and femoral segments, sufficient attenuation was observed for the majority of patients when using virtual monoenergetic images (SCM: 96–100% of patients, RCM: 88–94%) without statistical difference between both groups. Segments with attenuation measurements below the threshold remained qualitatively assessable as well. Likert scores were ‘excellent’ for virtual monoenergetic images 50 keV and 55 keV in both groups (RCM: 1.2–1.4, SCM: 1.2–1.3). With diagnostic image quality maintained, it can be concluded that reduced iodine loads of 14–24.5 gI are feasible for pre-TAVI vascular assessment on a spectral detector CT scanner.

The prospective, multicenter German National Cohort (NAKO) provides a unique dataset of cardiac magnetic resonance (CMR) cine images. Effective processing of these images requires a robust segmentation and quality control pipeline. A deep learning model for semantic segmentation, based on the nnU-Net architecture, was applied to full-cycle short-axis cine images from 29,908 baseline participants. The primary objective was to determine data on structure and function for both ventricles (LV, RV), including end-diastolic volumes (EDV), end-systolic volumes (ESV), and LV myocardial mass. Quality control measures included a visual assessment of outliers in morphofunctional parameters, inter- and intra-ventricular phase differences, and time-volume curves (TVC). These were adjudicated using a five-point rating scale, ranging from five (excellent) to one (non-diagnostic), with ratings of three or lower subject to exclusion. The predictive value of outlier criteria for inclusion and exclusion was evaluated using receiver operating characteristics analysis. The segmentation model generated complete data for 29,609 participants (incomplete in 1.0%), of which 5,082 cases (17.0%) underwent visual assessment. Quality assurance yielded a sample of 26,899 (90.8%) participants with excellent or good quality, excluding 1,875 participants due to image quality issues and 835 participants due to segmentation quality issues. TVC was the strongest single discriminator between included and excluded participants (AUC: 0.684). Of the two-category combinations, the pairing of TVC and phases provided the greatest improvement over TVC alone (AUC difference: 0.044; p<0.001). The best performance was observed when all three categories were combined (AUC: 0.748). By extending the quality-controlled sample to include mid-level 'acceptable' quality ratings, a total of 28,413 (96.0%) participants could be included. The implemented pipeline facilitated the automated segmentation of an extensive CMR dataset, integrating quality control measures. This methodology ensures that ensuing quantitative analyses are conducted with a diminished risk of bias.

Background: To validate the automated quantification of cardiac chamber volumes and myocardial mass on non-contrast chest CT using cardiac MR (CMR) as a reference. Methods: We retrospectively included 53 consecutive patients who received non-contrast chest CT and CMR within three weeks. A deep learning model created cardiac segmentations on axial soft-tissue reconstructions from CT, covering all four cardiac chambers and the left ventricular myocardium. Segmentations on CMR cine short-axis and long-axis images served as a reference. Standard estimates of diagnostic accuracy were calculated for ventricular volumes at end-diastole and end-systole (LVEDV, LVESV, RVEDV, RVESV), left ventricular mass (LVM), and atrial volumes (LA, RA) at ventricular end-diastole. A qualitative assessment noted segmentation issues. Results: The deep learning model generated CT measurements for 52 of the 53 patients (98%). Based on CMR measurements, the average LVEDV was 166 ± 64 mL, RVEDV was 144 ± 51 mL, and LVM was 115 ± 39 g. The CT measurements correlated well with CMR measurements for LVEDV, LVESV, and LVM (ICC = 0.85, ICC = 0.84, and ICC = 0.91; all p < 0.001) and RVEDV and RVESV (ICC = 0.79 and ICC= 0.78; both p < 0.001), and moderately well with LA and RA (ICC = 0.74 and ICC = 0.61; both p < 0.001). Absolute agreements likewise favored LVEDV, LVM, and RVEDV. ECG-gating did not relevantly influence the results. The CT results correctly identified 7/15 LV and 1/1 RV as dilated (one and six false positives, respectively). Major qualitative issues were found in three cases (6%). Conclusions: Automated cardiac chamber volume and myocardial mass quantification on non-contrast chest CT produced viable measurements in this retrospective sample. Relevance Statement: An automated cardiac assessment on non-contrast chest CT provides quantitative morphological data on the heart, enabling a preliminary organ evaluation that aids in incidentally identifying at-risk patients who may benefit from a more targeted diagnostic workup.

Combined photon intensity-modulated radiotherapy (IMRT) and sequential dose-escalated carbon ion beam therapy (IBT) is a technically advanced treatment option for head and neck malignancies. We proposed and evaluated an integrated planning strategy as opposed to an established and largely separated planning workflow. Ten patients with representative malignancies of the head and neck region underwent combined carbon-photon radiotherapy (RT) in our facilities. Clinical plans were created according to the separated workflow with independent optimization stages for both modalities. Experimental plans incorporated the existing carbon IBT dose distribution into the optimization stage of a step-and-shoot photon IMRT (bias dose planning). Cumulative dose distributions showed statistically significant differences between the two planning strategies and were predominantly in favor of the integrated approach. As such, target irradiation was generally maintained or even improved in a subset of metrics, while normal tissue sparing was widely enhanced; for instance, in the ipsilateral temporal lobe with median Dmean of −16% (p < 0.001). Maximum doses D1% (with adjustment for different fractionation) fell below thresholds for toxicity risk in a minority of instances, where they were previously exceeded. Integral dose did not differ significantly. Our findings indicate that combination planning of carbon-photon RT for head and neck malignancies may benefit from a proposed bias dose method, yielding favorable dose distribution characteristics and a streamlined planning workflow with fewer plan revisions. Further research is necessary to validate these observations in terms of robustness and their potential for higher tumor control.