TY - THES A1 - Heidenreich, Julius Frederik T1 - Characterization of the widely used Rac1-inhibitors NSC23766 and EHT1864 in mouse platelets T1 - Untersuchung der Rac1-Inhibitoren NSC23766 und EHT1864 in murinen Thrombozyten N2 - Platelet activation and aggregation at sites of vascular injury is critical to prevent excessive blood loss, but may also lead to life-threatening ischemic diseases, such as myocardial infarction and stroke. Extracellular agonists induce platelet activation by stimulation of platelet membrane receptors. Signal transduction results in reorganization of the cytoskeleton, shape change, platelet adhesion and aggregation, cumulating in thrombus formation. Several Rho GTPases, including Rac1, Cdc42 and RhoA, are essential mediators of subsequent intracellular transduction of ITAM- and GPCR-signaling. Therefore, inhibition or knockout can result in severely defective platelet signaling. Mice with platelet specific Rac1-deficiency are protected from arterial thrombosis. This benefit highlights further investigation of Rac1-specific functions and its potential as a new pharmacological target for prevention of cardiovascular diseases. Two newly developed synthetic compounds, NSC23766 and EHT1864, were proposed to provide highly specific inhibition of Rac1 activity, but both drugs have never been tested in Rac1-deficient cell systems to rule out potential Rac1-independent effects. This study revealed significant off-target effects of NSC23766 and EHT1864 that occurred in a dose-dependent fashion in both wild-type and Rac1-deficient platelets. Both inhibitors individually affected resting platelets after treatment, either by altering membrane protein expression (NSC23766) or by a marked decrease of platelet viability (EHT1864). Platelet apoptosis could be confirmed by enhanced levels of phosphatidylserine exposure and decreased mitochondrial membrane potential. Phosphorylation studies of the major effector proteins of Rac1 revealed that NSC23766 and EHT1864 abolish PAK1/PAK2 activation independently of Rac1 in wild-type and knockout platelets, which may contribute to the observed off-target effects. Additionally, this study demonstrated the involvement of Rac1 in G protein-coupled receptor-mediated platelet activation and GPIb-induced signaling. Furthermore, the data revealed that Rac1 is dispensable in the process of integrin IIb 3-mediated clot retraction. This study unveiled that new pharmacological approaches in antithrombotic therapy with Rac1 as molecular target have to be designed carefully in order to obtain high specificity and minimize potential off-target effects. N2 - Die Aktivierung und Aggregation von Thrombozyten nach Gefäßverletzungen ist entscheidend um starken Blutverlust zu vermeiden. Allerdings können diese Prozesse auch zu lebensbedrohlichen ischämischen Erkrankungen führen, wie beispielsweise Myokardinfarkt und Schlaganfall. Die Stimulation der Membranrezeptoren durch Triggersubstanzen leitet die Thrombozytenaktivierung und somit die Reorganisation des Zytoskeletts ein. Dies ermöglicht die Adhäsion und Aggregation der Thrombozyten und führt letztendlich zur Thrombusbildung. Die Rho GTPasen Rac1, Cdc42 und RhoA sind als wichtige Mediatoren an der intrazellulären Signaltransduktion beteiligt. Eine medikamentöse Hemmung oder ein genetischer Knockout kann daher die intrazellulären Signalkaskaden so stark beeinträchtigen, dass eine effiziente Aktivierung der Thrombozyten nicht mehr möglich ist. In Mäusen mit thrombozytenspezifischem Knockout von Rac1 wurde festgestellt, dass der Funktionsverlust von Rac1 gleichzeitig auch Schutz vor der Entwicklung von arterieller Thrombose bedeutet. Könnte man sich diese Tatsache pharmakologisch zunutze machen, würde die Hemmung von Rac1 möglicherweise einen neuen, erfolgsversprechenden Ansatz in der Prävention von kardiovaskulären Erkrankungen darstellen. Für den Forschungseinsatz wurden die zwei synthetischen Inhibitoren NSC23766 und EHT1864 entwickelt um Rac1-vermittelte Funktionen zu studieren. Beide Substanzen versprechen eine hochspezifische Hemmung der Rac(1)-Aktivität, wurden bisher jedoch nicht in Zellsystemen mit Rac1-Defizienz verwendet um die Substanzen kritisch auf mögliche, unerwünschte Nebenwirkungen zu untersuchen. In dieser Dissertation wurde gezeigt, dass NSC23766 und EHT1864 zwar effektive Hemmstoffe für Rac1 sind, allerdings genauso Rac1-unabhängige Nebenwirkungen verursachen. Beide Hemmstoffe führten zu Veränderungen der Thrombozyten: Während unter NSC23766 eine verminderte Expression von Membranrezeptoren beobachtet wurde, führte EHT1864 zu einer stark beeinträchtigten Vitalität der Thrombozyten. Anhand von erhöhten Phosphatidylserin-Werten und einer Veränderung des mitochondrialen Membranpotenzials in den behandelten Thrombozyten konnte die EHT1864-vermittelte Apoptose nachgewiesen werden. Letztendlich wurde anhand der verminderten Phosphorylierung von PAK1/PAK2 gezeigt, dass die Aktivierung dieser Rac1-Effektorproteine durch NSC23766 und EHT1864 direkt unterdrückt wird. Zusätzlich zu den Inhibitor-vermittelten Effekten wurde anhand von Rac1-defizienten Thrombozyten nachgewiesen, dass Rac1 auch an GPCR- und GPIb-vermittelten Signalkaskaden beteiligt ist. Außerdem wurde beobachtet, dass Rac1 für die Integrin IIb 3-vermittelte clot retraction entbehrlich ist. Die Ergebnisse dieser Studie legen dar, dass neue pharmakologische Substanzen für die antithrombotische Therapie mit Rac1 als Zielmolekül gründlich erforscht und hinterfragt werden müssen um die Spezifität zu maximieren und vor allem das Nebenwirkungsprofil zu minimieren. KW - Thrombozyt KW - Thrombose KW - Signaltransduktion KW - Enzyminhibitor KW - Rho-Proteine KW - mouse platelets KW - rac1 inhibitors Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-165453 ER - TY - INPR A1 - Heidenreich, Julius F. A1 - Gassenmaier, Tobias A1 - Ankenbrand, Markus J. A1 - Bley, Thorsten A. A1 - Wech, Tobias T1 - Self-configuring nnU-net pipeline enables fully automatic infarct segmentation in late enhancement MRI after myocardial infarction N2 - Purpose To fully automatically derive quantitative parameters from late gadolinium enhancement (LGE) cardiac MR (CMR) in patients with myocardial infarction and to investigate if phase sensitive or magnitude reconstructions or a combination of both results in best segmentation accuracy. Methods In this retrospective single center study, a convolutional neural network with a U-Net architecture with a self-configuring framework (“nnU-net”) was trained for segmentation of left ventricular myocardium and infarct zone in LGE-CMR. A database of 170 examinations from 78 patients with history of myocardial infarction was assembled. Separate fitting of the model was performed, using phase sensitive inversion recovery, the magnitude reconstruction or both contrasts as input channels. Manual labelling served as ground truth. In a subset of 10 patients, the performance of the trained models was evaluated and quantitatively compared by determination of the Sørensen-Dice similarity coefficient (DSC) and volumes of the infarct zone compared with the manual ground truth using Pearson’s r correlation and Bland-Altman analysis. Results The model achieved high similarity coefficients for myocardium and scar tissue. No significant difference was observed between using PSIR, magnitude reconstruction or both contrasts as input (PSIR and MAG; mean DSC: 0.83 ± 0.03 for myocardium and 0.72 ± 0.08 for scars). A strong correlation for volumes of infarct zone was observed between manual and model-based approach (r = 0.96), with a significant underestimation of the volumes obtained from the neural network. Conclusion The self-configuring nnU-net achieves predictions with strong agreement compared to manual segmentation, proving the potential as a promising tool to provide fully automatic quantitative evaluation of LGE-CMR. KW - Deep learning KW - CMR KW - Segmentation KW - Myocardial infarction KW - Scar KW - nnU-net Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-323418 UR - https://doi.org/10.1016/j.ejrad.2021.109817 ET - accepted version ER - TY - JOUR A1 - Grunz, Jan-Peter A1 - Pennig, Lenhard A1 - Fieber, Tabea A1 - Gietzen, Carsten Herbert A1 - Heidenreich, Julius Frederik A1 - Huflage, Henner A1 - Gruschwitz, Philipp A1 - Kuhl, Philipp Josef A1 - Petritsch, Bernhard A1 - Kosmala, Aleksander A1 - Bley, Thorsten Alexander A1 - Gassenmaier, Tobias T1 - Twin robotic x-ray system in small bone and joint trauma: Impact of cone-beam computed tomography on treatment decisions JF - European Radiology N2 - Objectives Trauma evaluation of extremities can be challenging in conventional radiography. A multi-use x-ray system with cone-beam CT (CBCT) option facilitates ancillary 3-D imaging without repositioning. We assessed the clinical value of CBCT scans by analyzing the influence of additional findings on therapy. Methods Ninety-two patients underwent radiography and subsequent CBCT imaging with the twin robotic scanner (76 wrist/hand/finger and 16 ankle/foot/toe trauma scans). Reports by on-call radiologists before and after CBCT were compared regarding fracture detection, joint affliction, comminuted injuries, and diagnostic confidence. An orthopedic surgeon recommended therapy based on reported findings. Surgical reports (N = 52) and clinical follow-up (N = 85) were used as reference standard. Results CBCT detected more fractures (83/64 of 85), joint involvements (69/53 of 71), and multi-fragment situations (68/50 of 70) than radiography (all p < 0.001). Six fractures suspected in radiographs were ruled out by CBCT. Treatment changes based on additional information from CBCT were recommended in 29 patients (31.5%). While agreement between advised therapy before CBCT and actual treatment was moderate (κ = 0.41 [95% confidence interval 0.35–0.47]; p < 0.001), agreement after CBCT was almost perfect (κ = 0.88 [0.83–0.93]; p < 0.001). Diagnostic confidence increased considerably for CBCT studies (p < 0.001). Median effective dose for CBCT was 4.3 μSv [3.3–5.3 μSv] compared to 0.2 μSv [0.1–0.2 μSv] for radiography. Conclusions CBCT provides advantages for the evaluation of acute small bone and joint trauma by detecting and excluding extremity fractures and fracture-related findings more reliably than radiographs. Additional findings induced therapy change in one third of patients, suggesting substantial clinical impact. KW - cone-beamcomputed tomography KW - extremities KW - fractures, bone KW - radiography Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-235233 SN - 0938-7994 VL - 31 ER - TY - JOUR A1 - Wech, Tobias A1 - Ankenbrand, Markus Johannes A1 - Bley, Thorsten Alexander A1 - Heidenreich, Julius Frederik T1 - A data-driven semantic segmentation model for direct cardiac functional analysis based on undersampled radial MR cine series JF - Magnetic Resonance in Medicine N2 - Purpose Image acquisition and subsequent manual analysis of cardiac cine MRI is time-consuming. The purpose of this study was to train and evaluate a 3D artificial neural network for semantic segmentation of radially undersampled cardiac MRI to accelerate both scan time and postprocessing. Methods A database of Cartesian short-axis MR images of the heart (148,500 images, 484 examinations) was assembled from an openly accessible database and radial undersampling was simulated. A 3D U-Net architecture was pretrained for segmentation of undersampled spatiotemporal cine MRI. Transfer learning was then performed using samples from a second database, comprising 108 non-Cartesian radial cine series of the midventricular myocardium to optimize the performance for authentic data. The performance was evaluated for different levels of undersampling by the Dice similarity coefficient (DSC) with respect to reference labels, as well as by deriving ventricular volumes and myocardial masses. Results Without transfer learning, the pretrained model performed moderately on true radial data [maximum number of projections tested, P = 196; DSC = 0.87 (left ventricle), DSC = 0.76 (myocardium), and DSC =0.64 (right ventricle)]. After transfer learning with authentic data, the predictions achieved human level even for high undersampling rates (P = 33, DSC = 0.95, 0.87, and 0.93) without significant difference compared with segmentations derived from fully sampled data. Conclusion A 3D U-Net architecture can be used for semantic segmentation of radially undersampled cine acquisitions, achieving a performance comparable with human experts in fully sampled data. This approach can jointly accelerate time-consuming cine image acquisition and cumbersome manual image analysis. KW - undersampling KW - cardiovascular magnetic resonance (CMR) KW - deep learning KW - radial KW - semantic segmentation Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-257616 VL - 87 IS - 2 ER - TY - JOUR A1 - Winter, Patrick A1 - Andelovic, Kristina A1 - Kampf, Thomas A1 - Gutjahr, Fabian Tobias A1 - Heidenreich, Julius A1 - Zernecke, Alma A1 - Bauer, Wolfgang Rudolf A1 - Jakob, Peter Michael A1 - Herold, Volker T1 - Fast self-navigated wall shear stress measurements in the murine aortic archusing radial 4D-phase contrast cardiovascular magnetic resonance at 17.6 T JF - Journal of Cardiovascular Magnetic Resonance N2 - Purpose 4D flow cardiovascular magnetic resonance (CMR) and the assessment of wall shear stress (WSS) are non-invasive tools to study cardiovascular risks in vivo. Major limitations of conventional triggered methods are the long measurement times needed for high-resolution data sets and the necessity of stable electrocardiographic (ECG) triggering. In this work an ECG-free retrospectively synchronized method is presented that enables accelerated high-resolution measurements of 4D flow and WSS in the aortic arch of mice. Methods 4D flow and WSS were measured in the aortic arch of 12-week-old wildtype C57BL/6 J mice (n = 7) with a radial 4D-phase-contrast (PC)-CMR sequence, which was validated in a flow phantom. Cardiac and respiratory motion signals were extracted from the radial CMR signal and were used for the reconstruction of 4D-flow data. Rigid motion correction and a first order B0 correction was used to improve the robustness of magnitude and velocity data. The aortic lumen was segmented semi-automatically. Temporally averaged and time-resolved WSS and oscillatory shear index (OSI) were calculated from the spatial velocity gradients at the lumen surface at 14 locations along the aortic arch. Reproducibility was tested in 3 animals and the influence of subsampling was investigated. Results Volume flow, cross-sectional areas, WSS and the OSI were determined in a measurement time of only 32 min. Longitudinal and circumferential WSS and radial stress were assessed at 14 analysis planes along the aortic arch. The average longitudinal, circumferential and radial stress values were 1.52 ± 0.29 N/m2, 0.28 ± 0.24 N/m2 and − 0.21 ± 0.19 N/m2, respectively. Good reproducibility of WSS values was observed. Conclusion This work presents a robust measurement of 4D flow and WSS in mice without the need of ECG trigger signals. The retrospective approach provides fast flow quantification within 35 min and a flexible reconstruction framework. KW - 4D flow KW - WSS KW - OSI KW - Self-navigation KW - Mouse KW - Aortic arch Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-201120 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 - Heidenreich, Julius F. A1 - Weng, Andreas M. A1 - Donhauser, Julian A1 - Greiser, Andreas A1 - Chow, Kelvin A1 - Nordbeck, Peter A1 - Bley, Thorsten A. A1 - Köstler, Herbert T1 - T1- and ECV-mapping in clinical routine at 3 T: differences between MOLLI, ShMOLLI and SASHA JF - BMC Medical Imaging N2 - Background T1 mapping sequences such as MOLLI, ShMOLLI and SASHA make use of different technical approaches, bearing strengths and weaknesses. It is well known that obtained T1 relaxation times differ between the sequence techniques as well as between different hardware. Yet, T1 quantification is a promising tool for myocardial tissue characterization, disregarding the absence of established reference values. The purpose of this study was to evaluate the feasibility of native and post-contrast T1 mapping methods as well as ECV maps and its diagnostic benefits in a clinical environment when scanning patients with various cardiac diseases at 3 T. Methods Native and post-contrast T1 mapping data acquired on a 3 T full-body scanner using the three pulse sequences 5(3)3 MOLLI, ShMOLLI and SASHA in 19 patients with clinical indication for contrast enhanced MRI were compared. We analyzed global and segmental T1 relaxation times as well as respective extracellular volumes and compared the emerged differences between the used pulse sequences. Results T1 times acquired with MOLLI and ShMOLLI exhibited systematic T1 deviation compared to SASHA. Myocardial MOLLI T1 times were 19% lower and ShMOLLI T1 times 25% lower compared to SASHA. Native blood T1 times from MOLLI were 13% lower than SASHA, while post-contrast MOLLI T1-times were only 5% lower. ECV values exhibited comparably biased estimation with MOLLI and ShMOLLI compared to SASHA in good agreement with results reported in literature. Pathology-suspect segments were clearly differentiated from remote myocardium with all three sequences. Conclusion Myocardial T1 mapping yields systematically biased pre- and post-contrast T1 times depending on the applied pulse sequence. Additionally calculating ECV attenuates this bias, making MOLLI, ShMOLLI and SASHA better comparable. Therefore, myocardial T1 mapping is a powerful clinical tool for classification of soft tissue abnormalities in spite of the absence of established reference values. KW - T1 mapping KW - MOLLI KW - ShMOLLI KW - SASHA KW - Extracellular volume KW - 3 T Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-201999 VL - 19 ER -