TY - JOUR A1 - Weng, Andreas M. A1 - Köstler, Herbert A1 - Bley, Thorsten A. A1 - Ritter, Christian O. T1 - Effect of short-term smoking & L-arginine on coronary endothelial function assessed by cardiac magnetic resonance cold pressor testing: a pilot study JF - BMC Cardiovascular Disorders N2 - Background The effect of smoking on coronary vasomotion has been investigated in the past with various imaging techniques in both short- and long-term smokers. Additionally, coronary vasomotion has been shown to be normalized in long-term smokers by L-Arginine acting as a substrate for NO synthase, revealing the coronary endothelium as the major site of abnormal vasomotor response. Aim of the prospective cohort study was to investigate coronary vasomotion of young healthy short-term smokers via magnetic resonance cold pressor test with and without the administration of L-Arginine and compare obtained results with the ones from nonsmokers. Methods Myocardial blood flow (MBF) was quantified with first-pass perfusion MRI on a 1.5 T scanner in healthy short-term smokers (N = 10, age: 25.0 ± 2.8 years, 5.0 ± 2.9 pack years) and nonsmokers (N = 10, age: 34.3 ± 13.6) both at rest and during cold pressor test (CPT). Smokers underwent an additional examination after administration of L-Arginine within a median of 7 days of the naïve examination. Results MBF at rest turned out to be 0.77 ± 0.30 (smokers with no L-Arginine; mean ± standard deviation), 0.66 ± 0.21 (smokers L-Arginine) and 0.84 ± 0.08 (nonsmokers). Values under CPT were 1.21 ± 0.42 (smokers no L-Arginine), 1.09 ± 0.35 (smokers L-Arginine) and 1.63 ± 0.33 (nonsmokers). In all groups, MBF was significantly increased under CPT compared to the corresponding rest examination (p < 0.05 in all cases). Additionally, MBF under CPT was significantly different between the smokers and the nonsmokers (p = 0.002). MBF at rest was significantly different between the smokers when L-Arginine was given and the nonsmokers (p = 0.035). Conclusion Short-term smokers showed a reduced response to cold both with and without the administration of L-Arginine. However, absolute MBF values under CPT were lower compared to nonsmokers independently of L-Arginine administration. KW - MRI KW - myocardial perfusion KW - cold pressor test KW - endothelium KW - L-arginine KW - smoking Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-260559 VL - 21 ER - TY - JOUR A1 - Weng, Andreas M. A1 - Heidenreich, Julius F. A1 - Metz, Corona A1 - Veldhoen, Simon A1 - Bley, Thorsten A. A1 - Wech, Tobias T1 - Deep learning-based segmentation of the lung in MR-images acquired by a stack-of-spirals trajectory at ultra-short echo-times JF - BMC Medical Imaging N2 - 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. KW - MRI KW - lung KW - deep learning KW - image segmentation Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-260520 VL - 21 ER - TY - JOUR A1 - Petritsch, Bernhard A1 - Pannbecker, Pauline A1 - Weng, Andreas M. A1 - Grunz, Jan-Peter A1 - Veldhoen, Simon A1 - Bley, Thorsten A. A1 - Kosmala, Aleksander T1 - Split-filter dual-energy CT pulmonary angiography for the diagnosis of acute pulmonary embolism: a study on image quality and radiation dose JF - Quantitative Imaging in Medicine and Surgery N2 - Background: Computed tomography (CT) pulmonary angiography is the diagnostic reference standard in suspected pulmonary embolism (PE). Favorable results for dual-energy CT (DECT) images have been reported for this condition. Nowadays, dual-energy data acquisition is feasible with different technical options, including a single-source split-filter approach. Therefore, the aim of this retrospective study was to investigate image quality and radiation dose of thoracic split-filter DECT in comparison to conventional single-energy CT in patients with suspected PE. Methods: A total of 110 CT pulmonary angiographies were accomplished either as standard single-energy CT with automatic tube voltage selection (ATVS) (n=58), or as split-filter DECT (n=52). Objective [pulmonary artery CT attenuation, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR)] and subjective image quality [four-point Likert scale; three readers (R)] were compared among the two study groups. Size-specific dose estimates (SSDE), dose-length-product (DLP) and volume CT dose index (CTDIvol) were assessed for radiation dose analysis. Results: Split-filter DECT images yielded 67.7% higher SNR (27.0 vs. 16.1; P<0.001) and 61.9% higher CNR (22.5 vs. 13.9; P<0.001) over conventional single-energy images, whereas CT attenuation was significantly lower (344.5 vs. 428.2 HU; P=0.013). Subjective image quality was rated good or excellent in 93.0%/98.3%/77.6% (R1/R2/R3) of the single-energy CT scans, and 84.6%/82.7%/80.8% (R1/R2/R3) of the split-filter DECT scans. SSDE, DLP and CTDIvol were significantly lower for conventional single-energy CT compared to split-filter DECT (all P<0.05), which was associated with 26.7% higher SSDE. Conclusions: In the diagnostic workup of acute PE, the split-filter allows for dual-energy data acquisition from single-source single-layer CT scanners. The existing opportunity to assess pulmonary “perfusion” based on analysis of iodine distribution maps is associated with higher radiation dose in terms of increased SSDE than conventional single-energy CT with ATVS. Moreover, a proportion of up to 3.8% non-diagnostic examinations in the current reference standard test for PE is not negligible. KW - dual-energy KW - CT-angiography KW - vascular KW - pulmonary arteries KW - embolism/thrombosis Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-231456 VL - 11 IS - 5 ER -