Association between myosteatosis and impaired glucose metabolism: A deep learning whole-body magnetic resonance imaging population phenotyping approach.

BACKGROUND: There is increasing evidence that myosteatosis, which is currently not assessed in clinical routine, plays an important role in risk estimation in individuals with impaired glucose metabolism, as it is associated with the progression of insulin resistance. With advances in artificial intelligence, automated and accurate algorithms have become feasible to fill this gap. METHODS: In this retrospective study, we developed and tested a fully automated deep learning model using data from two prospective cohort studies (German National Cohort [NAKO] and Cooperative Health Research in the Region of Augsburg [KORA]) to quantify myosteatosis on whole-body T1-weighted Dixon magnetic resonance imaging as (1) intramuscular adipose tissue (IMAT; the current standard) and (2) quantitative skeletal muscle (SM) fat fraction (SMFF). Subsequently, we investigated the two measures for their discrimination of and association with impaired glucose metabolism beyond baseline demographics (age, sex and body mass index [BMI]) and cardiometabolic risk factors (lipid panel, systolic blood pressure, smoking status and alcohol consumption) in asymptomatic individuals from the KORA study. Impaired glucose metabolism was defined as impaired fasting glucose or impaired glucose tolerance (140-200 mg/dL) or prevalent diabetes mellitus. RESULTS: Model performance was high, with Dice coefficients of ≥0.81 for IMAT and ≥0.91 for SM in the internal (NAKO) and external (KORA) testing sets. In the target population (380 KORA participants: mean age of 53.6 ± 9.2 years, BMI of 28.2 ± 4.9 kg/m2, 57.4% male), individuals with impaired glucose metabolism (n = 146; 38.4%) were older and more likely men and showed a higher cardiometabolic risk profile, higher IMAT (4.5 ± 2.2% vs. 3.9 ± 1.7%) and higher SMFF (22.0 ± 4.7% vs. 18.9 ± 3.9%) compared to normoglycaemic controls (all P ≤ 0.005). SMFF showed better discrimination for impaired glucose metabolism than IMAT (area under the receiver operating characteristic curve [AUC] 0.693 vs. 0.582, 95% confidence interval [CI] [0.06-0.16]; P < 0.001) but was not significantly different from BMI (AUC 0.733 vs. 0.693, 95% CI [-0.09 to 0.01]; P = 0.15). In univariable logistic regression, IMAT (odds ratio [OR] = 1.18, 95% CI [1.06-1.32]; P = 0.004) and SMFF (OR = 1.19, 95% CI [1.13-1.26]; P < 0.001) were associated with a higher risk of impaired glucose metabolism. This signal remained robust after multivariable adjustment for baseline demographics and cardiometabolic risk factors for SMFF (OR = 1.10, 95% CI [1.01-1.19]; P = 0.028) but not for IMAT (OR = 1.14, 95% CI [0.97-1.33]; P = 0.11). CONCLUSIONS: Quantitative SMFF, but not IMAT, is an independent predictor of impaired glucose metabolism, and discrimination is not significantly different from BMI, making it a promising alternative for the currently established approach. Automated methods such as the proposed model may provide a feasible option for opportunistic screening of myosteatosis and, thus, a low-cost personalized risk assessment solution.

Accuracy of a deep learning-based algorithm for the detection of thoracic aortic calcifications in chest computed tomography and cardiovascular surgery planning.

OBJECTIVES: To assess the accuracy of a deep learning-based algorithm for fully automated detection of thoracic aortic calcifications in chest computed tomography (CT) with a focus on the aortic clamping zone. METHODS: We retrospectively included 100 chest CT scans from 91 patients who were examined on second- or third-generation dual-source scanners. Subsamples comprised 47 scans with an electrocardiogram-gated aortic angiography and 53 unenhanced scans. A deep learning model performed aortic landmark detection and aorta segmentation to derive 8 vessel segments. Associated calcifications were detected and their volumes measured using a mean-based density thresholding. Algorithm parameters (calcium cluster size threshold, aortic mask dilatation) were varied to determine optimal performance for the upper ascending aorta that encompasses the aortic clamping zone. A binary visual rating served as a reference. Standard estimates of diagnostic accuracy and inter-rater agreement using Cohen's Kappa were calculated. RESULTS: Thoracic aortic calcifications were observed in 74% of patients with a prevalence of 27-70% by aorta segment. Using different parameter combinations, the algorithm provided binary ratings for all scans and segments. The best performing parameter combination for the presence of calcifications in the aortic clamping zone yielded a sensitivity of 93% and a specificity of 82%, with an area under the receiver operating characteristic curve of 0.874. Using these parameters, the inter-rater agreement ranged from κ 0.66 to 0.92 per segment. CONCLUSIONS: Fully automated segmental detection of thoracic aortic calcifications in chest CT performs with high accuracy. This includes the critical preoperative assessment of the aortic clamping zone.

Photon-counting CT-angiography in pre-TAVR aortic annulus assessment: effects of retrospective vs. prospective ECG-synchronization on prosthesis valve selection.

To compare the diagnostic value of ultrahigh-resolution CT-angiography (UHR-CTA) compared with high-pitch spiral CTA (HPS-CTA) using a first-generation, dual-source photon-counting CT (PCD-CT) scanner for preprocedural planning of transcatheter aortic valve replacement (TAVR). Clinically referred patients with severe aortic valve stenosis underwent both, retrospective ECG-gated cardiac UHR-CTA (collimation: 120 × 0.2 mm) and prospective ECG-triggered aortoiliac HPS-CTA (collimation: 144 × 0.4 mm, full spectral capabilities) for TAVR planning from August 2022 to March 2023. Radiation dose was extracted from the CT reports, and the effective dose was calculated. Two radiologists analyzed UHR-CTA and HPS-CTA datasets, assessing the image quality of the aortic annulus, with regard to the lumen visibility and margin delineation using a 4-point visual-grading scale (ranges: 4 = "excellent" to 1 = "poor"). Aortic annulus area (AAA) measurements were taken for valve prosthesis sizing, with retrospective UHR-CTA serving as reference standard. A total of 64 patients were included (mean age, 81 years ± 7 SD; 28 women) in this retrospective study. HPS-CTA showed a lower radiation dose, 4.1 mSv vs. 12.6 mSv (p < 0.001). UHR-CTA demonstrated higher image quality to HPS-CTA (median score, 4 [IQR, 3-4] vs. 3 [IQR, 2-3]; p < 0.001). Quantitative assessments of AAA from both CTA datasets were strongly positively correlated (mean 477.4 ± 91.1 mm2 on UHR-CTA and mean 476.5 ± 90.4 mm2 on HPS-CTA, Pearson r2 = 0.857, p < 0.001) with a mean error of 22.3 ± 24.6 mm2 and resulted in identical valve prosthesis sizing in the majority of patients (91%). Patients with lower image quality on HPS-CTA (score value 1 or 2, n = 28) were more likely to receive different sizing recommendations (82%). Both UHR-CTA and HPS-CTA acquisitions using photon-counting CT technology provided reliable aortic annular assessments for TAVR planning. While UHR-CTA offers superior image quality, HPS-CTA is associated with lower radiation exposure. However, severely impaired image quality on HPS-CTA may impact on prosthesis sizing, suggesting that immediate post-scan image evaluations may require complementary UHR-CTA scanning.

Deep learning to estimate impaired glucose metabolism from Magnetic Resonance Imaging of the liver: An opportunistic population screening approach.

AIM: Diabetes is a global health challenge, and many individuals are undiagnosed and not aware of their increased risk of morbidity/mortality although dedicated tests are available, which indicates the need for novel population-wide screening approaches. Here, we developed a deep learning pipeline for opportunistic screening of impaired glucose metabolism using routine magnetic resonance imaging (MRI) of the liver and tested its prognostic value in a general population setting. METHODS: In this retrospective study a fully automatic deep learning pipeline was developed to quantify liver shape features on routine MR imaging using data from a prospective population study. Subsequently, the association between liver shape features and impaired glucose metabolism was investigated in individuals with prediabetes, type 2 diabetes and healthy controls without prior cardiovascular diseases. K-medoids clustering (3 clusters) with a dissimilarity matrix based on Euclidean distance and ordinal regression was used to assess the association between liver shape features and glycaemic status. RESULTS: The deep learning pipeline showed a high performance for liver shape analysis with a mean Dice score of 97.0±0.01. Out of 339 included individuals (mean age 56.3±9.1 years; males 58.1%), 79 (23.3%) and 46 (13.6%) were classified as having prediabetes and type 2 diabetes, respectively. Individuals in the high risk cluster using all liver shape features (n = 14) had a 2.4 fold increased risk of impaired glucose metabolism after adjustment for cardiometabolic risk factors (age, sex, BMI, total cholesterol, alcohol consumption, hypertension, smoking and hepatic steatosis; OR 2.44 [95% CI 1.12-5.38]; p = 0.03). Based on individual shape features, the strongest association was found between liver volume and impaired glucose metabolism after adjustment for the same risk factors (OR 1.97 [1.38-2.85]; p<0.001). CONCLUSIONS: Deep learning can estimate impaired glucose metabolism on routine liver MRI independent of cardiometabolic risk factors and hepatic steatosis.

Photon-counting computed tomography - clinical application in oncological, cardiovascular, and pediatric radiology. / Photon-Counting Computertomographie ­ klinische Anwendungen in der onkologischen, kardiovaskulären und pädiatrischen Radiologie.

BACKGROUND: Photon-counting detector computed tomography (PCD-CT) is a promising new technology with the potential to fundamentally change workflows in the daily routine and provide new quantitative imaging information to improve clinical decision-making and patient management. METHOD: The contents of this review are based on an unrestricted literature search of PubMed and Google Scholar using the search terms "photon-counting CT", "photon-counting detector", "spectral CT", "computed tomography" as well as on the authors' own experience. RESULTS: The fundamental difference with respect to the currently established energy-integrating CT detectors is that PCD-CT allows for the counting of every single photon at the detector level. Based on the identified literature, PCD-CT phantom measurements and initial clinical studies have demonstrated that the new technology allows for improved spatial resolution, reduced image noise, and new possibilities for advanced quantitative image postprocessing. CONCLUSION: For clinical practice, the potential benefits include fewer beam hardening artifacts, a radiation dose reduction, and the use of new or combinations of contrast agents. In particular, critical patient groups such as oncological, cardiovascular, lung, and head & neck as well as pediatric patient collectives benefit from the clinical advantages. KEY POINTS: · Photon-counting computed tomography (PCD-CT) is being used for the first time in routine clinical practice, enabling a significant dose reduction in critical patient populations such as oncology, cardiology, and pediatrics.. · Compared to conventional CT, PCD-CT enables a reduction in electronic image noise.. · Due to the spectral data sets, PCD-CT enables fully comprehensive post-processing applications.. CITATION FORMAT: · Hagen F, Soschynski M, Weis M et al. Photon-counting computed tomography - clinical application in oncological, cardiovascular, and pediatric radiology. Fortschr Röntgenstr 2024; 196: 25 - 34.

Trajectories of glycaemic traits exhibit sex-specific associations with hepatic iron and fat content: Results from the KORA-MRI study.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) represents a major disease burden in the population. While the bidirectional association between NAFLD and diabetes is established, little is known about the association of hepatic iron content and glycaemia. Moreover, analyses of sex-specific effects and of dynamic changes in glycaemia are scarce. METHODS: We investigated 7-year sex-specific trajectories of glycaemia and related traits (HbA1c, fasting glucose, fasting insulin, HOMA-IR, 2-h glucose and cross-sectional 2-h insulin) in a sample from a population-based cohort (N = 365; 41.1% female). Hepatic iron and fat content were assessed by 3T-Magnetic Resonance Imaging (MRI). Two-step multi-level models adjusted for glucose-lowering medication and confounders were applied. RESULTS: In women and men, markers of glucose metabolism correlated with hepatic iron and fat content. Deterioration of glycaemia was associated with increased hepatic iron content in men (normoglycaemia to prediabetes: beta = 2.21 s-1 , 95% CI [0.47, 3.95]). Additionally, deterioration of glycaemia (e.g. prediabetes to diabetes: 1.27 log(%), [0.84, 1.70]) and trajectories of glucose, insulin and HOMA-IR were significantly associated with hepatic fat content in men. Similarly, deterioration of glycaemia as well as trajectories of glucose, insulin and HOMA-IR was significantly associated with increased hepatic fat content in women (e.g. trajectory of fasting insulin: 0.63 log(%), [0.36, 0.90]). CONCLUSIONS: Unfavourable 7-year trajectories of markers of glucose metabolism are associated with increased hepatic fat content, particularly in women, whereas the association with hepatic iron content was less clear. Monitoring changes of glycaemia in the sub-diabetic range might enable early identification of hepatic iron overload and steatosis.

Adipose tissue is associated with kidney function parameters.

Obesity is characterized by the accumulation of adipose tissue in different body compartments. Whether adipose tissue directly affects kidney function is still unknown. We aimed to investigate the role of the adipose tissue and circulating creatinine, cystatin C and kidney function in subjects free of cardio-renal diseases. In the KORA-MRI population-based study, 377 subjects (mean age 56.2 ± 9.2 years; 41.6% female) underwent whole-body 3T-MRI examination. Adipose tissue defined as visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were quantified from T1-DIXON sequence using a semi-automatic algorithm. Serum creatinine and cystatin C were measured using standard laboratory and estimated glomerular filtration rate (e-GFR) was performed based on creatinine (e-GFRcrea), cystatin C (e-GFRcys) and creatinine-cystatin C (e-GFRcc). Linear regression analysis, adjusted for risk factors, was used to investigate the relationship between adipose tissue and circulating creatinine, cystatin C, and kidney function. In multivariate analyses VAT was inversely associated with eGFRcys (ß = - 4.88, p = < 0.001), and positively associated with serum cystatin C (ß = 0.05, p = < 0.001), respectively. No association was found between other adipose parameters such as total adipose tissue (TAT) and subcutaneous adipose tissue (SAT) and serum creatinine, urine microalbumin and eGFRcrea. Stratified analyses according to BMI revealed confirmatory results for category of BMI > 30. VAT is positively associated with serum cystatin C and inversely with eGFR based on cystatin C, suggesting a direct involvement of visceral adipose tissue in increased metabolism of cystatin C and consequently decreased kidney function.

Enhancing Radiation Dose Efficiency in Prospective ECG-Triggered Coronary CT Angiography Using Calcium-Scoring CT.

BACKGROUND: This study investigates whether the scan length adjustment of prospectively ECG-triggered coronary CT angiography (CCTA) using calcium-scoring CT (CAS-CT) images can reduce overall radiation doses. METHODS: A retrospective analysis was conducted on 182 patients who underwent CAS-CT and prospectively ECG-triggered CCTA using a second-generation Dual-Source CT scanner. CCTA planning was based on CAS-CT images, for which simulated scout view planning was performed for comparison. Effective doses were compared between two scenarios: Scenario 1-CAS-CT-derived CCTA + CAS-CT and Scenario 2-scout-view-derived CCTA without CAS-CT. Dose differences were further analyzed with respect to scan mode and body mass index. RESULTS: Planning CCTA using CAS-CT led to a shorter scan length than planning via scout view (114.3 ± 9.7 mm vs. 133.7 ± 13.2 mm, p < 0.001). The whole-examination effective dose was slightly lower for Scenario 1 (3.2 [1.8-5.3] mSv vs. 3.4 [1.5-5.9] mSv; p < 0.001, n = 182). Notably, Scenario 1 resulted in a significantly lower radiation dose for sequential scans and obese patients. Only high-pitch spiral CCTA showed dose reduction in Scenario 2. CONCLUSIONS: Using CAS-CT for planning prospectively ECG-triggered CCTA reduced the overall radiation dose administered compared to scout view planning without CAS-CT, except for high-pitch spiral CCTA, where a slightly opposite effect was observed.

Prediction of upgrade to clinically significant prostate cancer in patients under active surveillance: Performance of a fully automated AI-algorithm for lesion detection and classification.

BACKGROUND: Multiparametric MRI (mpMRI) improves the detection of aggressive prostate cancer (PCa) subtypes. As cases of active surveillance (AS) increase and tumor progression triggers definitive treatment, we evaluated whether an AI-driven algorithm can detect clinically significant PCa (csPCa) in patients under AS. METHODS: Consecutive patients under AS who received mpMRI (PI-RADSv2.1 protocol) and subsequent MR-guided ultrasound fusion (targeted and extensive systematic) biopsy between 2017 and 2020 were retrospectively analyzed. Diagnostic performance of an automated clinically certified AI-driven algorithm was evaluated on both lesion and patient level regarding the detection of csPCa. RESULTS: Analysis of 56 patients resulted in 93 target lesions. Patient level sensitivity and specificity of the AI algorithm was 92.5%/31% for the detection of ISUP ≥ 1 and 96.4%/25% for the detection of ISUP ≥ 2, respectively. The only case of csPCa missed by the AI harbored only 1/47 Gleason 7a core (systematic biopsy; previous and subsequent biopsies rendered non-csPCa). CONCLUSIONS: AI-augmented lesion detection and PI-RADS scoring is a robust tool to detect progression to csPCa in patients under AS. Integration in the clinical workflow can serve as reassurance for the reader and streamline reporting, hence improve efficiency and diagnostic confidence.

[Lesions of the prevascular mediastinum]. / Läsionen des prävaskulären Mediastinums.

CLINICAL/METHODOLOGICAL ISSUE: Mediastinal masses are a group of heterogenous lesions which may be a coincidental finding or present with symptoms. More than half of mediastinal lesions are located in the prevascular mediastinum. As these tumors are not often encountered in clinical routine, making a diagnosis with a high degree of confidence may seem challenging. STANDARD RADIOLOGICAL METHODS: The latest computed tomography (CT)-based classification of the mediastinal compartments by the International Thymic Malignancy Interest Group (ITMIG) moves away from earlier radiograph-based classification systems and helps to reliably classify tumors based on location, although in some cases the lesion can occupy more than one compartment. PERFORMANCE: Radiology plays an important role in the evaluation of anterior mediastinal lesions. Although in some cases imaging features alone allow a diagnosis to be made; in other cases knowledge of important radiological features and their analysis in the context of patient factors like age and duration of symptoms can help to narrow down the differential diagnosis, avoid unnecessary workup, and guide further steps. PRACTICAL RECOMMENDATIONS: Computed tomography (CT) is central for the characterization of mediastinal masses in clinical routine. In some situations, like in the case of thymic cysts or thymic hyperplasia, magnetic resonance imaging (MRI) can be used as a noninvasive problem-solving tool.

Intervention rates and outcomes in medically managed uncomplicated descending thoracic aortic dissections.

OBJECTIVE: To evaluate the long-term incidence and outcome of aortic interventions for medically managed uncomplicated thoracic aortic dissections. METHODS: Between January 2012 and December 2018, 91 patients were discharged home with an uncomplicated, medically treated aortic dissection (involving the descending aorta with or without aortic arch involvement, no ascending involvement). After a median period of 4 (first quartile: 2, third quartile: 11) months, 30 patients (33%) required an aortic intervention. Patient characteristics, radiographic, treatment, and follow-up data were compared for patients with and without aortic interventions. A competing risk regression model was analyzed to identify independent predictors of aortic intervention and to predict the risk for intervention. RESULTS: Patients who underwent aortic interventions had significantly larger thoracic (P = .041) and abdominal (P = .015) aortic diameters, the dissection was significantly longer (P = .035), there were more communications between both lumina (P = .040), and the first communication was significantly closer to the left subclavian artery (P = .049). A descending thoracic aortic diameter exceeding 45 mm was predictive for an aortic intervention (P = .001; subdistribution hazard ratio: 3.51). The risk for aortic intervention was 27% ± 10% and 36% ± 11% after 1 and 3 years, respectively. Fourteen patients (47%) underwent thoracic endovascular aortic repair, 11 patients (37%) thoracic endovascular aortic repair and left carotid to subclavian bypass, 3 patients (10%) total arch replacement with the frozen elephant trunk technique, and 2 patients (7%) thoracoabdominal aortic replacement. We observed no in-hospital mortality. CONCLUSIONS: The need for secondary aortic interventions in patients with initially medically managed, uncomplicated descending aortic dissections is substantial. The full spectrum of aortic treatment options (endovascular, hybrid, conventional open surgical) is required in these patients.

Association of lumbar vertebral bone marrow and paraspinal muscle fat composition with intervertebral disc degeneration: 3T quantitative MRI findings from the population-based KORA study.

OBJECTIVE: To assess the association of lumbar bone marrow adipose tissue fat fraction (BMAT-FF) and paraspinal muscle proton density fat fraction (PDFF) and their interplay with intervertebral disc degeneration (IVDD). METHODS: In this retrospective cross-sectional study based on a prospective population-based cohort, BMAT-FF and PDFF of asymptomatic individuals were calculated based on 3T-MRI dual-echo and multi-echo Dixon VIBE sequences. IVDD was assessed at motion segments L1 to L5 and dichotomized based on Pfirrmann grade ≥ 4 and/or presence of other severe degenerative changes or spinal abnormalities at least at one segment. Pearson's correlation coefficients were calculated for BMAT-FF and PDFF. Univariable and multivariable logistic regression models for IVDD were calculated. RESULTS: Among 335 participants (mean age: 56.2 ± 9.0 years, 43.3% female), the average BMI was 27.7 ± 4.5 kg/m2 and the prevalence of IVDD was high (69.9%). BMAT-FF and PDFF were significantly correlated (r = 0.31-0.34; p < 0.001). The risk for IVDD increased with higher PDFF (OR = 1.45; CI 1.03, 2.04) and BMAT-FF (OR = 1.56; CI 1.16, 2.11). Pairwise combinations of PDFF and BMAT-FF quartiles revealed a lower risk for IVDD in individuals in the lowest BMAT-FF and PDFF quartile (OR = 0.21; CI 0.1, 0.48). Individuals in the highest BMAT-FF and PDFF quartile showed an increased risk for IVDD (OR = 5.12; CI 1.17, 22.34) CONCLUSION: Lumbar BMAT-FF and paraspinal muscle PDFF are correlated and represent both independent and additive risk factors for IVDD. Quantitative MRI measurements of paraspinal myosteatosis and vertebral bone marrow fatty infiltration may serve as imaging biomarkers to assess the individual risk for IVDD. KEY POINTS: • Fat composition of the lumbar vertebral bone marrow is positively correlated with paraspinal skeletal muscle fat. • Higher fat-fractions of lumbar vertebral bone marrow and paraspinal muscle are both independent as well as additive risk factors for intervertebral disc degeneration. • Quantitative magnetic resonance imaging measurements of bone marrow and paraspinal muscle may serve as imaging biomarkers for intervertebral disc degeneration.

Changes in aortic diameter induced by weight loss: The HELENA trial- whole-body MR imaging in a dietary intervention trial.

Obesity-related metabolic disorders such as hypertension, hyperlipidemia and chronic inflammation have been associated with aortic dilatation and resulting in aortic aneurysms in many cases. Whether weight loss may reduce the risk of aortic dilatation is not clear. In this study, the diameter of the descending thoracic aorta, infrarenal abdominal aorta and aortic bifurcation of 144 overweight or obese non-smoking adults were measured by MR-imaging, at baseline, and 12 and 50 weeks after weight loss by calorie restriction. Changes in aortic diameter, anthropometric measures and body composition and metabolic markers were evaluated using linear mixed models. The association of the aortic diameters with the aforementioned clinical parameters was analyzed using Spearman`s correlation. Weight loss was associated with a reduction in the thoracic and abdominal aortic diameters 12 weeks after weight loss (predicted relative differences for Quartile 4: 2.5% ± 0.5 and -2.2% ± 0.8, p < 0.031; respectively). Furthermore, there was a nominal reduction in aortic diameters during the 50-weeks follow-up period. Aortic diameters were positively associated with weight, visceral adipose tissue, glucose, HbA1c and with both systolic and diastolic blood pressure. Weight loss induced by calorie restriction may reduce aortic diameters. Future studies are needed to investigate, whether the reduction of aortic diameters via calorie restriction may help to prevent aortic aneurysms.

Association of MRI-based adrenal gland volume and impaired glucose metabolism in a population-based cohort study.

OBJECTIVES: The aim of this study was to assess adrenal gland volume by using magnetic resonance imaging (MRI) and to study its role as an indirect marker of impaired glucose metabolism and hypothalamic-pituitary-adrenal (HPA) axis activation in a population-based cohort. METHODS: Asymptomatic participants were enrolled in a nested case-control study and underwent a 3-T MRI, including T1w-VIBE-Dixon sequences. For the assessment of adrenal gland volume, adrenal glands were manually segmented in a blinded fashion. Impaired glucose metabolism was determined using fasting glucose and oral glucose tolerance test. Cardiometabolic risk factors were also obtained. Inter- and intrareader reliability as well as univariate and multivariate associations were derived. RESULTS: Among 375 subjects included in the analysis (58.5% male, 56.1 ± 9.1 years), 25.3% participants had prediabetes and 13.6% had type 2 diabetes (T2DM). Total adrenal gland volume was 11.2 ± 4.2 ml and differed significantly between impaired glucose metabolism and healthy controls with largest total adrenal gland volume in T2DM (healthy controls: 10.0 ± 3.9 ml, prediabetes: 12.5 ± 3.8 ml, T2DM: 13.9 ± 4.6 ml; p < 0.001). In the multivariate analysis, association of T2DM and increased adrenal gland volume was independent of age, sex, hypertension, triglycerides and body mass index (BMI), but was attenuated in subjects with prediabetes after adjustment for BMI. CONCLUSIONS: T2DM is significantly associated with increased adrenal gland volume by MRI in an asymptomatic cohort, independent of age, sex, dyslipidaemia, hypertension and BMI. Adrenal gland volume may represent an indirect marker of impaired glucose metabolism and HPA axis dysfunction.

Population-based cohort imaging: skeletal muscle mass by magnetic resonance imaging in correlation to bioelectrical-impedance analysis.

BACKGROUND: Skeletal muscle mass is subjected to constant changes and is considered a good predictor for outcome in various diseases. Bioelectrical-impedance analysis (BIA) and magnetic resonance imaging (MRI) are approved methodologies for its assessment. However, muscle mass estimations by BIA may be influenced by excess intramuscular lipids and adipose tissue in obesity. The objective of this study was to evaluate the feasibility of quantitative assessment of skeletal muscle mass by MRI as compared with BIA. METHODS: Subjects from a population-based cohort underwent BIA (50 kHz, 0.8 mA) and whole-body MRI including chemical-shift encoded MRI (six echo times). Abdominal muscle mass by MRI was quantified as total and fat-free cross-sectional area by a standardized manual segmentation-algorithm and normalized to subjects' body height2 (abdominal muscle mass indices: AMMIMRI ). RESULTS: Among 335 included subjects (56.3 ± 9.1 years, 56.1% male), 95 (28.4%) were obese (BMI ≥ 30 kg/m2 ). MRI-based and BIA-based measures of muscle mass were strongly correlated, particularly in non-obese subjects [r < 0.74 in non-obese (P < 0.001) vs. r < 0.56 in obese (P < 0.001)]. Median AMMITotal(MRI) was significantly higher in obese as compared with non-obese subjects (3246.7 ± 606.1 mm2 /m2 vs. 2839.0 ± 535.8 mm2 /m2 , P < 0.001, respectively), whereas the ratio AMMIFat-free /AMMITotal (by MRI) was significantly higher in non-obese individuals (59.3 ± 10.1% vs. 53.5 ± 10.6%, P < 0.001, respectively). No significant difference was found regarding AMMIFat-free(MRI) (P = 0.424). In analyses adjusted for age and sex, impaired glucose tolerance and measures of obesity were significantly and positively associated with AMMITotal(MRI) and significantly and inversely with the ratio AMMIFat-free(MRI) /AMMITotal(MRI) (P < 0.001). CONCLUSIONS: MRI-based assessment of muscle mass is feasible in population-based imaging and strongly correlated with BIA. However, the observed weaker correlation in obese subjects may explain the known limitation of BIA in obesity and promote MRI-based assessments. Thus, skeletal muscle mass parameters by MRI may serve as practical imaging biomarkers independent of subjects' body weight.

Aortic Arch Anatomy in Candidates for Aortic Arch Repair.

Detailed knowledge of aortic anatomy is necessary before new prostheses can be developed. Our aim was to provide a thorough analysis of aortic arch anatomy in patients who are potential candidates for arch repair. Patients' charts were screened between 2001 and 2019 for all those with a dissection or aneurysm involving aortic arch. Aortic diameters, segmental lengths, aortic arch type, tortuosity, diameters and length of supraaortic vessels were analyzed via computed tomography angiography. We included 558 patients who underwent thoracic aortic treatment for type A, B, non-A non-B dissection, or aortic arch aneurysm. Incidence of all three arch types was similar in patients with type A dissection. In type B dissection and arch aneurysm patients, arch type III was most commonly observed (47% and 52%, respectively). The left vertebral artery offspring from aortic arch was observed in 6.6%. The mid-ascending aorta and aortic arch were not dilated in type B and non-A non-B dissection patients. The innominate, left common carotid and left subclavian arteries median diameters were 16 (14; 18), 8 (7; 9) and 11 (10; 12) mm, respectively. The median innominate artery length was 37 (30; 44) mm. The median left subclavian artery length was 40 (34; 46) mm. Arch types are distributed differently among patients with various arch pathologies. Patients with aortic dissection type B and non-A non-B have a non-dilated ascending aorta and aortic arch. Aortic arch tortuosity, innominate and left subclavian artery lengths do not differ among aortic pathologies.

Association between cardiovascular risk factors and degenerative disc disease of the thoracolumbar spine in the general population: results from the KORA MRI Study.

BACKGROUND: Little is known about the associations between cardiovascular risk factors (CRF) and disc degeneration (DD). PURPOSE: To evaluate the potential association between CRFs and intervertebral DD in a population-based sample. METHODS: A total of 400 participants from the community-based KORA-study were assessed in terms of CRFs, specifically obesity, hypertension, diabetes, elevated LDL-c, low HDL-c, elevated triglycerides, smoking status, and alcohol consumption. The patients additionally underwent whole-body magnetic resonance imaging (MRI) using T2-weighted single-shot fast-spin-echo and T1 dual-echo gradient-echo Dixon pulse sequences. Thoracic and lumbar DD were assessed using the Pfirrmann score and for the presence of disc bulging/protrusion. Cross-sectional associations between CRFs and MR-based Pfirrmann score were then analyzed. RESULTS: A total of 385 individuals (58.2% men; mean age 56.3 ± 9.2 years) were included. Prevalence of DD was 76.4%. Older age (ß = 0.18; 95% CI 0.12-0.25; P < 0.001) and higher body mass index (BMI) (ß = 0.19; 95% CI 0.06-0.30; P = 0.003) were significantly associated with DD of the thoracolumbar spine. Diabetes was significantly associated with DD at T7/8 (P = 0.029) and L3/4 (P = 0.017). Hypertension correlated significantly with DD in univariate analysis, but the association did not persist using multivariate analysis (ß = 0.53; 95% CI -0.74 to 1.81; P = 0.41). None of the other CRFs (P ≥ 0.11) were associated with advanced DD. Disc bulging was independently associated with hypertension (ß = 0.47; 95% CI 0.27-0.81; P = 0.01). CONCLUSION: A significant independent association exists between age, BMI, and intervertebral DD. In contrast, there is no significant association between cardiovascular risk factors and DD. Providing strong evidence that the pathologic process undergirding DD is mechanical, rather than microvascular, in nature.

Pulmonary artery diameter: means and normal limits-assessment by computed tomography angiography.

OBJECTIVES: Normal pulmonary artery (PA) diameter remains blurred and the definitions of PA aneurysm are heterogenous. We aimed to assess PA diameters, identify a threshold for normal diameters, define PA aneurysms, possible predictors of PA size and evaluate the correlation with mid-ascending aortic diameters. METHODS: Between April 2018 and August 2019, 497 consecutive patients who underwent whole-body computed tomographic angiography were reviewed. Clinical and imaging data were collected from our institutional database. Precise three-dimensional centreline measurements were taken. Linear regression analysis was performed to detect parameters associated with PA diameter. A two-stage model was created to identify potential predictors and the resulting statistically significant interactions were tested. Data were grouped and PA, standard deviation, and upper normal limits were calculated. RESULTS: Among 497 patients with an average age of 51.4 (20.2) (74.6% males), the mean PA diameter measured 32.0 (4.6) mm [female: 31.2 (4.7) mm vs male: 32.2 (4.5) mm; P = 0.032]. The mean PA length, left PA and right PA diameters were similar between male and female patients. We found a significant correlation (r = 0.352; P < 0.001) between the PAs and mid-ascending aortic diameters. Body surface area (P = 0.032, ß = 4.52 [0.40; 8.64] 95% CI) was the only significant influencing variable for PA diameter. CONCLUSIONS: The normal mean PA diameter in a reference cohort is 32.0 (4.6) mm. Body surface area is the only influencing variable of PA diameter. The normal diameters measured and corresponding upper limits of normal revealed that a PA aneurysm should not be considered below a threshold of 45 mm.