Faculty Bibliography

PURPOSE/OBJECTIVE:To compare image quality between a 2D T1w turbo spin echo (TSE) sequence and a Compressed SENSE accelerated 3D T1w black blood TSE sequence (equipped with a black blood prepulse for blood signal suppression) in pre- and postcontrast imaging of the pituitary and to assess scan time reductions. METHODS AND MATERIALS/METHODS:For this retrospective study, 56 patients underwent pituitary MR imaging at 3T. 28 patients were scanned with the 2D- and 28 patients with the accelerated 3D sequence. Two board certified neuroradiologists independently evaluated 13 qualitative image features (12 features on postcontrast- and 1 feature on precontrast images).SNR and CNR measurements were obtained. Interreader agreement was assessed with the intraclass correlation coefficient while differences in scores were assessed with exact Wilcoxon rank sum tests. RESULTS:The interreader agreement ranged from fair (visibility of the ophthalmic nerve, ICC = 0.57) to excellent (presence and severity of pulsation artefacts, ICC = 0.97). The Compressed SENSE accelerated 3D sequence outperformed the 2D sequence in terms of "overall image quality" (median: 4 versus 3, p = 0.04) and "presence and severity of pulsation artefacts" (median: 0 versus 1, p < 0.001). There were no significant differences in any other qualitative and quantitative (SNR, CNR) image quality features. Scan time was reduced by 03:53 min (33.1%) by replacing the 2D with the 3D sequence. CONCLUSION/CONCLUSIONS:The Compressed SENSE accelerated 3D T1w black blood TSE sequence is a reliable alternative for the standard 2D sequence in pituitary imaging. The black blood prepulse may aid in suppression of pulsation artefacts.

OBJECTIVES/OBJECTIVE:To evaluate a deep convolutional neural network (dCNN) for detection, highlighting, and classification of ultrasound (US) breast lesions mimicking human decision-making according to the Breast Imaging Reporting and Data System (BI-RADS). METHODS AND MATERIALS/METHODS:One thousand nineteen breast ultrasound images from 582 patients (age 56.3 ± 11.5 years) were linked to the corresponding radiological report. Lesions were categorized into the following classes: no tissue, normal breast tissue, BI-RADS 2 (cysts, lymph nodes), BI-RADS 3 (non-cystic mass), and BI-RADS 4-5 (suspicious). To test the accuracy of the dCNN, one internal dataset (101 images) and one external test dataset (43 images) were evaluated by the dCNN and two independent readers. Radiological reports, histopathological results, and follow-up examinations served as reference. The performances of the dCNN and the humans were quantified in terms of classification accuracies and receiver operating characteristic (ROC) curves. RESULTS:In the internal test dataset, the classification accuracy of the dCNN differentiating BI-RADS 2 from BI-RADS 3-5 lesions was 87.1% (external 93.0%) compared with that of human readers with 79.2 ± 1.9% (external 95.3 ± 2.3%). For the classification of BI-RADS 2-3 versus BI-RADS 4-5, the dCNN reached a classification accuracy of 93.1% (external 95.3%), whereas the classification accuracy of humans yielded 91.6 ± 5.4% (external 94.1 ± 1.2%). The AUC on the internal dataset was 83.8 (external 96.7) for the dCNN and 84.6 ± 2.3 (external 90.9 ± 2.9) for the humans. CONCLUSION/CONCLUSIONS:dCNNs may be used to mimic human decision-making in the evaluation of single US images of breast lesion according to the BI-RADS catalog. The technique reaches high accuracies and may serve for standardization of highly observer-dependent US assessment. KEY POINTS/CONCLUSIONS:• Deep convolutional neural networks could be used to classify US breast lesions. • The implemented dCNN with its sliding window approach reaches high accuracies in the classification of US breast lesions. • Deep convolutional neural networks may serve for standardization in US BI-RADS classification.

Background Increasing evidence suggests a psychosomatic link between neural systems and the heart. In light of the growing burden of ischemic cardiovascular disease across the globe, a better understanding of heart-brain interactions and their implications for cardiovascular treatment strategies is needed. Thus, we sought to investigate the interaction between myocardial injury and metabolic alterations in central neural areas in patients with suspected or known coronary artery disease. Methods and Results The association between resting metabolic activity in distinct neural structures and cardiac function was analyzed in 302 patients (aged 66.8±10.2 years; 70.9% men) undergoing fluor-18-deoxyglucose positron emission tomography and ^99mTc-tetrofosmin single-photon emission computed tomography myocardial perfusion imaging. There was evidence for reduction of callosal, caudate, and brainstem fluor-18-deoxyglucose uptake in patients with impaired left ventricular ejection fraction (<55% versus ≥55%: P=0.047, P=0.022, and P=0.013, respectively) and/or in the presence of myocardial ischemia (versus normal perfusion: P=0.010, P=0.013, and P=0.016, respectively). In a sex-stratified analysis, these differences were observed in men, but not in women. A first-order interaction term consisting of sex and impaired left ventricular ejection fraction or myocardial ischemia was identified as predictor of metabolic activity in these neural regions (left ventricular ejection fraction: P=0.015 for brainstem; myocardial ischemia: P=0.004, P=0.018, and P=0.003 for callosal, caudate, or brainstem metabolism, respectively). Conclusions Myocardial dysfunction and injury are associated with reduced resting metabolic activity of central neural structures, including the corpus callosum, the caudate nucleus, and the brainstem. These associations differ in women and men, suggesting sex differences in the pathophysiological interplay of the nervous and cardiovascular systems.

BACKGROUND: Inflammation plays a fundamental role in mediating all stages of atherosclerosis. Given the higher prevalence of inflammatory rheumatologic conditions in women and the female propensity towards worse cardiovascular outcomes, refined strategies are needed to better identify the high-risk female cardiovascular phenotype. OBJECTIVES/OBJECTIVE: This article aims to assess sex-specific links between inflammatory processes and the development and progression of ischemic heart disease. PATIENTS AND METHODS/METHODS:Tc-tetrofosmin single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI). RESULTS: = 0.014). CONCLUSION/CONCLUSIONS: A strong link between bone marrow metabolism and impaired myocardial function and perfusion was observed in women, but not in men. Our data suggest that novel biomarkers of inflammation might help to identify women at risk for ischemic cardiomyopathy and to tailor disease management to the female cardiovascular phenotype.

PURPOSE/OBJECTIVE:To evaluate the diagnostic performance of bone texture analysis (TA) combined with machine learning (ML) algorithms in standard CT scans to identify patients with vertebrae at risk for insufficiency fractures. MATERIALS AND METHODS/METHODS:Standard CT scans of 58 patients with insufficiency fractures of the spine, performed between 2006 and 2013, were analyzed retrospectively. Every included patient had at least two CT scans. Intact vertebrae in a first scan that either fractured ("unstable") or remained intact ("stable") in the consecutive scan were manually segmented on mid-sagittal reformations. TA features for all vertebrae were extracted using open-source software (MaZda). In a paired control study, all vertebrae of the study cohort "cases" and matched controls were classified using ROC analysis of Hounsfield unit (HU) measurements and supervised ML techniques. In a within-subject vertebra comparison, vertebrae of the cases were classified into "unstable" and "stable" using identical techniques. RESULTS:One hundred twenty vertebrae were included. Classification of cases/controls using ROC analysis of HU measurements showed an AUC of 0.83 (95% confidence interval [CI], 0.77-0.88), and ML-based classification showed an AUC of 0.97 (CI, 0.97-0.98). Classification of unstable/stable vertebrae using ROC analysis showed an AUC of 0.52 (CI, 0.42-0.63), and ML-based classification showed an AUC of 0.64 (CI, 0.61-0.67). CONCLUSION/CONCLUSIONS:TA combined with ML allows to identifying patients who will suffer from vertebral insufficiency fractures in standard CT scans with high accuracy. However, identification of single vertebra at risk remains challenging. KEY POINTS/CONCLUSIONS:• Bone texture analysis combined with machine learning allows to identify patients at risk for vertebral body insufficiency fractures on standard CT scans with high accuracy. • Compared to mere Hounsfield unit measurements on CT scans, application of bone texture analysis combined with machine learning improve fracture risk prediction. • This analysis has the potential to identify vertebrae at risk for insufficiency fracture and may thus increase diagnostic value of standard CT scans.

PURPOSE/OBJECTIVE:To investigate and compare the reproducibility and accuracy of qualitative ratings and quantitative texture analysis (TA) in detection and grading of lumbar spinal stenosis (LSS) in magnetic resonance imaging (MR) scans of the lumbar spine. MATERIALS AND METHODS/METHODS:From a nationwide multicenter and multidisciplinary lumbar stenosis outcome study (LSOS) register 82 patients, undergoing MR scans of the lumbar spine due to clinical indication of spinal claudication, with a single level central or lateral severe LSS were included. In total 343 transaxial T2-weighted images of the lumbar spine were included from one to five levels (L1 to S1) per patient. One expert radiologist serving as reference standard rated LSS grade according to a standard four-point (normal to severe) as well as to an eight-point Schizas grading scale. DICOM data were then rescaled to a defined pixel size. Two independent readers performed qualitative ratings analogous to expert reader in addition to TA of spinal canals by manually placing two regions of interest (ROI) per image reflecting qualitative scales: (1) dural sac only (2) inner contour of the spinal canal including epidural fat and bilateral recesses. Interreader agreements of qualitative and quantitative parameters were assessed by Cohen's Kappa (κ) and intraclass correlation (ICC), respectively. TA feature reduction was performed by ICC threshold > 0.75. Remaining features were analyzed with machine learning algorithms (Weka 3 tool) for correlation with LSS grades using 10-fold cross validation. RESULTS:Qualitative ratings showed only moderate reproducibility for both LSS classification systems but high correlation with cut-off cross-sectional area (CSA) <130mm² for severe spinal stenosis. In quantitative TA of both ROIs, machine learning analysis with a decision tree classifier revealed higher performances for LSS grading compared to qualitative assessments using the reference CSA cut-off, respectively. CONCLUSION/CONCLUSIONS:Qualitative LSS grading independent of classification system shows moderate reproducibility. TA with machine learning offers highly reproducible quantitative parameters that increase accuracy for severe LSS detection with minor impact of grading score and CSA border definition.

BACKGROUND/AIMS/OBJECTIVE:The influence of external factors such as opioids and alcohol has been extensively investigated for various segments of the gastrointestinal tract. However, the association between their use and the development of esophagogastric junction outflow obstruction disorders (EGJOODs) is unknown. Therefore, the aim of this study is to analyze prevalence and clinical relevance of opioids and alcohol intake in patients with EGJOODs. METHODS:< 0.05 was considered significant. RESULTS:= 0.011) was associated with EGJOODs. Excessive alcohol consumption was especially frequent in the non-achalasia esophagogastric junction outflow obstruction subgroup (16.2%) and opioid use in the achalasia type III subgroup (20.0%). CONCLUSIONS:We found a significant association between EGJOODs and opioid as well as excessive alcohol consumption. This underlines the importance of detailed history taking regarding medication and ethanol consumption in patients with dysphagia. Further prospective studies on mechanisms undelaying esophagogastric junction dysfunction due to opioids or alcohol are warranted.

Recent research focusing on brown adipose tissue (BAT) function emphasizes its importance in systemic metabolic homeostasis. We show here that genetic and pharmacological inhibition of the mevalonate pathway leads to reduced human and mouse brown adipocyte function in vitro and impaired adipose tissue browning in vivo. A retrospective analysis of a large patient cohort suggests an inverse correlation between statin use and active BAT in humans, while we show in a prospective clinical trial that fluvastatin reduces thermogenic gene expression in human BAT. We identify geranylgeranyl pyrophosphate as the key mevalonate pathway intermediate driving adipocyte browning in vitro and in vivo, whose effects are mediated by geranylgeranyltransferases (GGTases), enzymes catalyzing geranylgeranylation of small GTP-binding proteins, thereby regulating YAP1/TAZ signaling through F-actin modulation. Conversely, adipocyte-specific ablation of GGTase I leads to impaired adipocyte browning, reduced energy expenditure, and glucose intolerance under obesogenic conditions, highlighting the importance of this pathway in modulating brown adipocyte functionality and systemic metabolism.

Impaired cerebral vascular reactivity (CVR) increases long-term stroke risk. Obstructive sleep apnoea (OSA) is associated with peripheral vascular dysfunction and vascular events. The aim of this trial was to evaluate the effect of continuous positive airway pressure (CPAP) withdrawal on CVR.41 OSA patients (88% male, mean age 57±10 years) were randomised to either subtherapeutic or continuation of therapeutic CPAP. At baseline and after 2 weeks, patients underwent a sleep study and magnetic resonance imaging (MRI). CVR was estimated by quantifying the blood oxygen level-dependent (BOLD) MRI response to breathing stimuli.OSA did recur in the subtherapeutic CPAP group (mean treatment effect apnoea-hypopnoea index +38.0 events·h^-1, 95% CI 24.2-52.0; p<0.001) but remained controlled in the therapeutic group. Although there was a significant increase in blood pressure upon CPAP withdrawal (mean treatment effect +9.37 mmHg, 95% CI 1.36-17.39; p=0.023), there was no significant effect of CPAP withdrawal on CVR assessed via BOLD MRI under either hyperoxic or hypercapnic conditions.Short-term CPAP withdrawal did not result in statistically significant changes in CVR as assessed by functional MRI, despite the recurrence of OSA. We thus conclude that, unlike peripheral endothelial function, CVR is not affected by short-term CPAP withdrawal.

OBJECTIVE:High breast density is a risk factor for breast cancer. The aim of this study was to develop a deep convolutional neural network (dCNN) for the automatic classification of breast density based on the mammographic appearance of the tissue according to the American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) Atlas. METHODS:In this study, 20,578 mammography single views from 5221 different patients (58.3 ± 11.5 years) were downloaded from the picture archiving and communications system of our institution and automatically sorted according to the ACR density (a-d) provided by the corresponding radiological reports. A dCNN with 11 convolutional layers and 3 fully connected layers was trained and validated on an augmented dataset. The model was finally tested on two different datasets against: i) the radiological reports and ii) the consensus decision of two human readers. None of the test datasets was part of the dataset used for the training and validation of the algorithm. RESULTS:The optimal number of epochs was 91 for medio-lateral oblique (MLO) projections and 94 for cranio-caudal projections (CC), respectively. Accuracy for MLO projections obtained on the validation dataset was 90.9% (CC: 90.1%). Tested on the first test dataset of mammographies (850 MLO and 880 CC), the algorithm showed an accordance with the corresponding radiological reports of 71.7% for MLO and of 71.0% for CC. The agreement with the radiological reports improved in the differentiation between dense and fatty breast for both projections (MLO = 88.6% and CC = 89.9%). In the second test dataset of 200 mammographies, a good accordance was found between the consensus decision of the two readers on both, the MLO-model (92.2%) and the right craniocaudal-model (87.4%). In the differentiation between fatty (ACR A/B) and dense breasts (ACR C/D), the agreement reached 99% for the MLO and 96% for the CC projections, respectively. CONCLUSIONS:The dCNN allows for accurate classification of breast density based on the ACR BI-RADS system. The proposed technique may allow accurate, standardized, and observer independent breast density evaluation of mammographies. ADVANCES IN KNOWLEDGE/CONCLUSIONS:Standardized classification of mammographies by a dCNN could lead to a reduction of falsely classified breast densities, thereby allowing for a more accurate breast cancer risk assessment for the individual patient and a more reliable decision, whether additional ultrasound is recommended.