@article{FroehlichSerflingHiguchietal.2021,
  author    = {Fr{\"o}hlich, Matthias and Serfling, Sebastian and Higuchi, Takahiro and Pomper, Martin G. and Rowe, Steven P. and Schmalzing, Marc and Tony, Hans-Peter and Gernert, Michael and Strunz, Patrick-Pascal and Portegys, Jan and Schwaneck, Eva-Christina and Gadeholt, Ottar and Weich, Alexander and Buck, Andreas K. and Bley, Thorsten A. and Guggenberger, Konstanze V. and Werner, Rudolf A.},
  title     = {Whole-Body [\(^{18}\)F]FDG PET/CT Can Alter Diagnosis in Patients with Suspected Rheumatic Disease},
  series = {Diagnostics},
  volume    = {11},
  journal   = {Diagnostics},
  number    = {11},
  issn      = {2075-4418},
  doi       = {10.3390/diagnostics11112073},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-250227},
  year      = {2021},
  abstract  = {The 2-deoxy-d-[\(^{18}\)F]fluoro-D-glucose (FDG) positron emission tomography/computed tomography (PET/CT) is widely utilized to assess the vascular and articular inflammatory burden of patients with a suspected diagnosis of rheumatic disease. We aimed to elucidate the impact of [\(^{18}\)F]FDG PET/CT on change in initially suspected diagnosis in patients at the time of the scan. Thirty-four patients, who had undergone [\(^{18}\)F]FDG PET/CT, were enrolled and the initially suspected diagnosis prior to [18F]FDG PET/CT was compared to the final diagnosis. In addition, a semi-quantitative analysis including vessel wall-to-liver (VLR) and joint-to-liver (JLR) ratios was also conducted. Prior to [\(^{18}\)F]FDG PET/CT, 22/34 (64.7\%) of patients did not have an established diagnosis, whereas in 7/34 (20.6\%), polymyalgia rheumatica (PMR) was suspected, and in 5/34 (14.7\%), giant cell arteritis (GCA) was suspected by the referring rheumatologists. After [\(^{18}\)F]FDG PET/CT, the diagnosis was GCA in 19/34 (55.9\%), combined GCA and PMR (GCA + PMR) in 9/34 (26.5\%) and PMR in the remaining 6/34 (17.6\%). As such, [\(^{18}\)F]FDG PET/CT altered suspected diagnosis in 28/34 (82.4\%), including in all unclear cases. VLR of patients whose final diagnosis was GCA tended to be significantly higher when compared to VLR in PMR (GCA, 1.01 ± 0.08 (95\%CI, 0.95-1.1) vs. PMR, 0.92 ± 0.1 (95\%CI, 0.85-0.99), p = 0.07), but not when compared to PMR + GCA (1.04 ± 0.14 (95\%CI, 0.95-1.13), p = 1). JLR of individuals finally diagnosed with PMR (0.94 ± 0.16, (95\%CI, 0.83-1.06)), however, was significantly increased relative to JLR in GCA (0.58 ± 0.04 (95\%CI, 0.55-0.61)) and GCA + PMR (0.64 ± 0.09 (95\%CI, 0.57-0.71); p < 0.0001, respectively). In individuals with a suspected diagnosis of rheumatic disease, an inflammatory-directed [\(^{18}\)F]FDG PET/CT can alter diagnosis in the majority of the cases, particularly in subjects who were referred because of diagnostic uncertainty. Semi-quantitative assessment may be helpful in establishing a final diagnosis of PMR, supporting the notion that a quantitative whole-body read-out may be useful in unclear cases.},
  language  = {en}
}
@article{PetritschPannbeckerWengetal.2021,
  author    = {Petritsch, Bernhard and Pannbecker, Pauline and Weng, Andreas M. and Grunz, Jan-Peter and Veldhoen, Simon and Bley, Thorsten A. and Kosmala, Aleksander},
  title     = {Split-filter dual-energy CT pulmonary angiography for the diagnosis of acute pulmonary embolism: a study on image quality and radiation dose},
  series = {Quantitative Imaging in Medicine and Surgery},
  volume    = {11},
  journal   = {Quantitative Imaging in Medicine and Surgery},
  number    = {5},
  doi       = {10.21037/qims-20-740},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-231456},
  pages     = {1817-1827},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@article{WengHeidenreichMetzetal.2021,
  author    = {Weng, Andreas M. and Heidenreich, Julius F. and Metz, Corona and Veldhoen, Simon and Bley, Thorsten A. and Wech, Tobias},
  title     = {Deep learning-based segmentation of the lung in MR-images acquired by a stack-of-spirals trajectory at ultra-short echo-times},
  series = {BMC Medical Imaging},
  volume    = {21},
  journal   = {BMC Medical Imaging},
  doi       = {10.1186/s12880-021-00608-1},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-260520},
  year      = {2021},
  abstract  = {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 S{\o}rensen-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.},
  language  = {en}
}
@article{WengKoestlerBleyetal.2021,
  author    = {Weng, Andreas M. and K{\"o}stler, Herbert and Bley, Thorsten A. and Ritter, Christian O.},
  title     = {Effect of short-term smoking \& L-arginine on coronary endothelial function assessed by cardiac magnetic resonance cold pressor testing: a pilot study},
  series = {BMC Cardiovascular Disorders},
  volume    = {21},
  journal   = {BMC Cardiovascular Disorders},
  doi       = {10.1186/s12872-021-02050-1},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-260559},
  year      = {2021},
  abstract  = {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 na{\"i}ve 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.},
  language  = {en}
}