Bettina Baeßler

Scopus Publications

Fully Automated AI-Based Lymph Node Measurements in Chest CT: Accuracy and Reproducibility Compared with Multi-Reader Assessment
Andra-Iza Iuga, Heike Carolus, Liliana Lourenco Caldeira, Jonathan Kottlors, Miriam Rinneburger, Mathilda Weisthoff, Philipp Fervers, Philip Rauen, Florian Fichter, Lukas Goertz, Pia Niederau, Florian Siedek, Carola Heneweer, Carsten Gietzen, Lenhard Pennig, Anja Dobrostal, Fabian Laqua, Piotr Woznicki, David Maintz, Bettina Baessler, Thorsten Persigehl
Diagnostics, 2026
Background/Objectives: Accurate and reproducible lymph node (LN) measurement is essential for oncologic staging and therapy monitoring but is subject to inter-reader variability. This study evaluated the accuracy and reproducibility of a fully automated artificial intelligence (AI)-based LN measurement workflow in contrast-enhanced chest CT, using multi-reader manual measurements as reference. Methods: Sixty thoracic LNs from seven patients were independently measured by 13 radiologists in two reading rounds. The median of all measurements served as the ground truth (GT). Automated short- and long-axis diameters were derived from fully automated 3D CNN-based segmentations. Agreement between AI and manual measurements was assessed using Friedman testing, intraclass correlation coefficients (ICCs), and concordance correlation coefficients (CCCs). Measurement stability was evaluated across repeated runs on different hardware systems. Results: A total of 2280 manual measurements were analyzed. Manual assessment showed significant inter-reader variability (p < 0.01), while intra-reader agreement was high. No significant differences were observed between AI-based measurements and the GT (all p > 0.01). Agreement was good, with CCC values of 0.86 (SAD) and 0.79 (LAD). AI-based measurements were numerically stable across hardware configurations. Conclusions: Fully automated AI-based LN measurements in chest CT scans provide strong agreement with multi-reader consensus and high numerical stability. Automated measurement may support more standardized and reproducible oncologic imaging assessment.
Unconditional latent diffusion models memorize patient imaging data
Salman Ul Hassan Dar, Marvin Seyfarth, Isabelle Ayx, Theano Papavassiliu, Stefan O. Schoenberg, Robert Malte Siepmann, Fabian Christopher Laqua, Jannik Kahmann, Norbert Frey, Bettina Baeßler, Sebastian Foersch, Daniel Truhn, Jakob Nikolas Kather, Sandy Engelhardt
Nature Biomedical Engineering, 2026
Automated Lymph Node Localization and Segmentation in Patients with Head and Neck Cancer: Opportunities and Limitations of Using a Generic AI Model
Miriam Rinneburger, Heike Carolus, Andra-Iza Iuga, Mathilda Weisthoff, Simon Lennartz, Nils Große Hokamp, Liliana Lourenco Caldeira, Astha Jaiswal, David Maintz, Fabian Christopher Laqua, Bettina Baeßler, Tobias Klinder, Thorsten Persigehl
Diagnostics, 2026
Background/Objectives: Accurate assessment of lymph nodes is of paramount importance for correct cN staging in head and neck cancer; however, it is very time-consuming for radiologists, and lymph node metastases of head and neck cancers may show distinct characteristics, such as central necrosis or very large size. Here, we evaluate the performance of a previously developed generic cervical lymph node segmentation model in a cohort of patients with head and neck cancer. Methods: In our retrospective single-center, multi-vendor study, we included 125 patients with head and neck cancer with at least one untreated lymph node metastasis. On the respective cervical CT scan, an experienced radiologist segmented lymph nodes semi-automatically. All 3D segmentations were confirmed by a second reader. These manual segmentations were compared to segmentations generated by an AI model previously trained on a different dataset of varying cancers. Results: In cervical CT scans from 125 patients (61.9 years ± 10.6, 100 men), 3656 lymph nodes were segmented as ground-truth, including 544 clinical metastases. The AI achieved an average recall of 0.70 with 6.5 false positives per CT scan. The average global Dice accounts for 0.73 per scan, with an average Hausdorff distance of 0.88 mm. When analyzing the individual nodes, segmentation accuracy was similar for non-metastatic and metastatic lymph nodes, with a sensitivity of 0.89 and 0.85. Localization performance was lower for metastatic than for non-metastatic lymph nodes, with a recall of 0.65 and 0.74, respectively. Model performance was worse for enlarged nodes (short-axis diameter ≥ 15 mm), with a recall of 0.36 and a sensitivity of 0.67. Conclusions: The AI model for generic cervical lymph node segmentation shows good performance for smaller nodes (SAD ≤ 15 mm) with respect to localization and segmentation accuracy. However, for clearly enlarged and necrotic nodes, a retraining of the generic AI algorithm seems to be required for accurate cN staging.
Joint image reconstruction and segmentation of real-time cardiovascular magnetic resonance imaging in free-breathing using a model based on disentangled representation learning
Tobias Wech, Oliver Schad, Simon Sauer, Jonas Kleineisel, Nils Petri, Peter Nordbeck, Thorsten A. Bley, Bettina Baeßler, Bernhard Petritsch, Julius F. Heidenreich
Journal of Cardiovascular Magnetic Resonance, 2025
BACKGROUND: To investigate image quality and agreement of derived cardiac function parameters in a novel joint image reconstruction and segmentation approach based on disentangled representation learning, enabling real-time cardiac cine imaging during free-breathing. METHODS: A multi-tasking neural network architecture, incorporating disentangled representation learning, was trained using simulated examinations based on data from a public repository along with cardiovascular magnetic resonance (CMR) scans specifically acquired for model development. An exploratory feasibility study evaluated the method on undersampled real-time acquisitions using an in-house developed spiral balanced steady-state free precession pulse sequence in eight healthy participants and five patients with intermittent atrial fibrillation. Images and predicted left ventricle segmentations were compared to the reference standard of electrocardiography (ECG)-gated segmented Cartesian cine with repeated breath-holds and corresponding manual segmentation. RESULTS: On a 5-point Likert scale, image quality of the real-time breath-hold approach and Cartesian cine was comparable in healthy participants (RT-BH: 1.99 ± 0.98, Cartesian: 1.94 ± 0.86, p = 0.052), but slightly inferior in free-breathing (RT-FB: 2.40 ± 0.98, p < 0.001). In patients with arrhythmia, both real-time approaches demonstrated favorable image quality (RT-BH: 2.10 ± 1.28, p < 0.001, RT-FB: 2.40 ± 1.13, p < 0.01, Cartesian: 2.68 ± 1.13). Intra-observer reliability was good (intraclass correlation coefficient = 0.77, 95% confidence interval [0.75, 0.79], p < 0.001). In functional analysis, a positive bias was observed for ejection fractions derived from the proposed model compared to the clinical reference standard (RT-BH mean: 58.5 ± 5.6%, bias: +3.47%, 95% confidence interval [-0.86, 7.79%], RT-FB mean: 57.9 ± 10.6%, bias: +1.45%, [-3.02, 5.91%], Cartesian mean: 54.9 ± 6.7%). CONCLUSION: The introduced real-time CMR imaging technique enables high-quality cardiac cine data acquisitions in 1-2 min, eliminating the need for ECG gating and breath-holds. This approach offers a promising alternative to the current clinical practice of segmented acquisition, with shorter scan times, improved patient comfort, and increased robustness to arrhythmia and patient non-compliance.
Color Maps: Facilitating the Clinical Impact of Quantitative MRI
Nico Sollmann, Miha Fuderer, Fabio Crameri, Sebastian Weingärtner, Bettina Baeßler, Vikas Gulani, Kathryn E. Keenan, Stefano Mandija, Xavier Golay, Nandita M. deSouza
Journal of Magnetic Resonance Imaging, 2025
Presenting quantitative data using non‐standardized color maps potentially results in unrecognized misinterpretation of data. Clinically meaningful color maps should intuitively and inclusively represent data without misleading interpretation. Uniformity of the color gradient for color maps is critically important. Maximal color and lightness contrast, readability for color vision‐impaired individuals, and recognizability of the color scheme are highly desirable features. This article describes the use of color maps in five key quantitative MRI techniques: relaxometry, diffusion‐weighted imaging (DWI), dynamic contrast‐enhanced (DCE)‐MRI, MR elastography (MRE), and water‐fat MRI. Current display practice of color maps is reviewed and shortcomings against desirable features are highlighted.Evidence Level5Technical EfficacyStage 2
Automatic structuring of radiology reports with on-premise open-source large language models
Piotr Woźnicki, Caroline Laqua, Ina Fiku, Amar Hekalo, Daniel Truhn, Sandy Engelhardt, Jakob Kather, Sebastian Foersch, Tugba Akinci D’Antonoli, Daniel Pinto dos Santos, Bettina Baeßler, Fabian Christopher Laqua
European Radiology, 2025
Objectives Structured reporting enhances comparability, readability, and content detail. Large language models (LLMs) could convert free text into structured data without disrupting radiologists’ reporting workflow. This study evaluated an on-premise, privacy-preserving LLM for automatically structuring free-text radiology reports. Materials and methods We developed an approach to controlling the LLM output, ensuring the validity and completeness of structured reports produced by a locally hosted Llama-2-70B-chat model. A dataset with de-identified narrative chest radiograph (CXR) reports was compiled retrospectively. It included 202 English reports from a publicly available MIMIC-CXR dataset and 197 German reports from our university hospital. Senior radiologist prepared a detailed, fully structured reporting template with 48 question-answer pairs. All reports were independently structured by the LLM and two human readers. Bayesian inference (Markov chain Monte Carlo sampling) was used to estimate the distributions of Matthews correlation coefficient (MCC), with [−0.05, 0.05] as the region of practical equivalence (ROPE). Results The LLM generated valid structured reports in all cases, achieving an average MCC of 0.75 (94% HDI: 0.70–0.80) and F1 score of 0.70 (0.70–0.80) for English, and 0.66 (0.62–0.70) and 0.68 (0.64–0.72) for German reports, respectively. The MCC differences between LLM and humans were within ROPE for both languages: 0.01 (−0.05 to 0.07), 0.01 (−0.05 to 0.07) for English, and −0.01 (−0.07 to 0.05), 0.00 (−0.06 to 0.06) for German, indicating approximately comparable performance. Conclusion Locally hosted, open-source LLMs can automatically structure free-text radiology reports with approximately human accuracy. However, the understanding of semantics varied across languages and imaging findings. Key Points QuestionWhy has structured reporting not been widely adopted in radiology despite clear benefits and how can we improve this? FindingsA locally hosted large language model successfully structured narrative reports, showing variation between languages and findings. Critical relevanceStructured reporting provides many benefits, but its integration into the clinical routine is limited. Automating the extraction of structured information from radiology reports enables the capture of structured data while allowing the radiologist to maintain their reporting workflow.
Repeatability of radiomic features in myocardial T1 and T2 mapping
Mathias Manzke, Fabian C. Laqua, Benjamin Böttcher, Ann-Christin Klemenz, Marc-André Weber, Bettina Baeßler, Felix G. Meinel
European Radiology, 2025
Purpose To investigate the test–retest repeatability of radiomic features in myocardial native T1 and T2 mapping. Methods In this prospective study, 50 healthy volunteers (29 women and 21 men, mean age 39.4 ± 13.7 years) underwent two identical cardiac magnetic resonance imaging (MRI) examinations at 1.5 T. The protocol included native T1 and T2 mapping in both short-axis and long-axis orientation. For T1 mapping, we investigated standard (1.9 × 1.9 mm) and high (1.4 × 1.4 mm) spatial resolution. After manual segmentation of the left ventricular myocardium, 100 radiomic features from seven feature classes were extracted and analyzed. Test–retest repeatability of radiomic features was assessed using the intraclass correlation coefficient (ICC) and classified as poor (ICC < 0.50), moderate (0.50–0.75), good (0.75–0.90), and excellent (> 0.90). Results For T1 maps acquired in short-axis orientation at standard resolution, repeatability was excellent for 6 features, good for 29 features, moderate for 19 features, and poor for 46 features. We identified 15 features from 6 classes which showed good to excellent reproducibility for T1 mapping in all resolutions and all orientations. For short-axis T2 maps, repeatability was excellent for 6 features, good for 25 features, moderate for 23 features, and poor for 46 features. 12 features from 5 classes were found to have good to excellent repeatability in T2 mapping independent of slice orientation. Conclusion We have identified a subset of features with good to excellent repeatability independent of slice orientation and spatial resolution. We recommend using these features for further radiomics research in myocardial T1 and T2 mapping. Key Points Question The study addresses the need for reliable radiomic features for quantitative analysis of the myocardium to ensure diagnostic consistency in cardiac MRI. Findings We have identified a subset of radiomic features demonstrating good to excellent repeatability in native T1 and T2 mapping independent of slice orientation and resolution. Clinical relevance Radiomic features have been proposed as diagnostic and prognostic biomarkers in various heart diseases. By identifying a subset of particularly reproducible radiomic features our study serves to inform the selection of radiomic features in future research and clinical applications. Graphical Abstract
Color-map recommendation for MR relaxometry maps
Miha Fuderer, Barbara Wichtmann, Fabio Crameri, Nandita M. de Souza, Bettina Baeßler, Vikas Gulani, Meiyun Wang, Dirk Poot, Ruud de Boer, Matt Cashmore, Kathryn E. Keenan, Dan Ma, Carolin Pirkl, Nico Sollmann, Sebastian Weingärtner, Stefano Mandija, Xavier Golay
Magnetic Resonance in Medicine, 2025
PurposeTo harmonize the use of color for MR relaxometry maps and therefore recommend the use of specific color‐maps for representing , , and maps and their inverses.MethodsPerceptually linearized color‐maps were chosen to have similar color settings as those proposed by Griswold et al. in 2018. A Delphi process, polling the opinion of a panel of 81 experts, was used to generate consensus on the suitability of these maps.ResultsConsensus was reached on the suitability of the logarithm‐processed Lipari color‐map for and the logarithm‐processed Navia color‐map for and . There was consensus on color bars being mandatory and on the use of a specific value indicating “invalidity.” There was no consensus on whether the ranges should be fixed per anatomy.ConclusionThe authors recommend the use of the logarithm‐processed Lipari color‐map for displaying quantitative maps and maps; likewise, the authors recommend the logarithm‐processed Navia color‐map for displaying , , , and maps.This work originated with the Quantitative MR Study Group of the International Society of Magnetic Resonance in Medicine (ISMRM); it has the approval of the Publication Committee and of the Board of the ISMRM.
Interpretation of CheckList for EvaluAtion of Radiomics research (CLEAR)
Chinese Journal of Evidence Based Medicine, 2025
Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics
Ralf Floca, Jonas Bohn, Christian Haux, Benedikt Wiestler, Frank G. Zöllner, Annika Reinke, Jakob Weiß, Marco Nolden, Steffen Albert, Thorsten Persigehl, Tobias Norajitra, Bettina Baeßler, Marc Dewey, Rickmer Braren, Martin Büchert, Eva Maria Fallenberg, Norbert Galldiks, Annika Gerken, Michael Götz, Horst K. Hahn, Johannes Haubold, Tobias Haueise, Nils Große Hokamp, Michael Ingrisch, Andra-Iza Iuga, Marco Janoschke, Matthias Jung, Lena Sophie Kiefer, Philipp Lohmann, Jürgen Machann, Jan Hendrik Moltz, Johanna Nattenmüller, Tobias Nonnenmacher, Benedict Oerther, Ahmed E. Othman, Felix Peisen, Fritz Schick, Lale Umutlu, Barbara D. Wichtmann, Wenzhao Zhao, Svenja Caspers, Heinz-Peter Schlemmer, Christopher L. Schlett, Klaus Maier-Hein, Fabian Bamberg
Insights into Imaging, 2024
Objectives Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation. Materials and methods The consensus was achieved by a multi-stage process. Stage 1 comprised a definition screening, a retrospective analysis with semantic mapping of terms found in 22 workflow definitions, and the compilation of an initial baseline definition. Stages 2 and 3 consisted of a Delphi process with over 45 experts hailing from sites participating in the German Research Foundation (DFG) Priority Program 2177. Stage 2 aimed to achieve a broad consensus for a definition proposal, while stage 3 identified the importance of translational challenges. Results Workflow definitions from 22 publications (published 2012–2020) were analyzed. Sixty-nine definition terms were extracted, mapped, and semantic ambiguities (e.g., homonymous and synonymous terms) were identified and resolved. The consensus definition was developed via a Delphi process. The final definition comprising seven phases and 37 aspects reached a high overall consensus (> 89% of experts “agree” or “strongly agree”). Two aspects reached no strong consensus. In addition, the Delphi process identified and characterized from the participating experts’ perspective the ten most important challenges in radiomics workflows. Conclusion To overcome semantic inconsistencies between existing definitions and offer a well-defined, broad, referenceable terminology, a consensus workflow definition for radiomics-based setups and a terms mapping to existing literature was compiled. Moreover, the most relevant challenges towards clinical application were characterized. Critical relevance statement Lack of standardization represents one major obstacle to successful clinical translation of radiomics. Here, we report a consensus workflow definition on different aspects of radiomics studies and highlight important challenges to advance the clinical adoption of radiomics. Key Points Published radiomics workflow terminologies are inconsistent, hindering standardization and translation. A consensus radiomics workflow definition proposal with high agreement was developed. Publicly available result resources for further exploitation by the scientific community. Graphical Abstract
Deep Learning k-Space-to-Image Reconstruction Facilitates High Spatial Resolution and Scan Time Reduction in Diffusion-Weighted Imaging Breast MRI
Stephanie Tina Sauer, Sara Aniki Christner, Anna‐Maria Lois, Piotr Woznicki, Carolin Curtaz, Andreas Steven Kunz, Elisabeth Weiland, Thomas Benkert, Thorsten Alexander Bley, Bettina Baeßler, Jan‐Peter Grunz
Journal of Magnetic Resonance Imaging, 2024
Cooperative AI training for cardiothoracic segmentation in computed tomography: An iterative multi-center annotation approach
Bianca Lassen-Schmidt, Bettina Baessler, Matthias Gutberlet, Josephine Berger, Jan M. Brendel, Andreas M. Bucher, Tilman Emrich, Philipp Fervers, Jonathan Kottlors, Philipp Kuhl, Matthias S. May, Tobias Penzkofer, Thorsten Persigehl, Diane Renz, Marwin-Jonathan Sähn, Lisa Siegler, Peter Kohlmann, Alexander Köhn, Florian Link, Hans Meine, Marc T. Thiemann, Horst K. Hahn, Malte M. Sieren
European Journal of Radiology, 2024
Perfect Match: Radiomics and Artificial Intelligence in Cardiac Imaging
Bettina Baeßler, Sandy Engelhardt, Amar Hekalo, Anja Hennemuth, Markus Hüllebrand, Ann Laube, Clemens Scherer, Malte Tölle, Tobias Wech
Circulation Cardiovascular Imaging, 2024
Robustness of radiomic features in healthy abdominal parenchyma of patients with repeated examinations on dual-layer dual-energy CT
Mirjam Schöneck, Simon Lennartz, David Zopfs, Kristina Sonnabend, Robert Wawer Matos Reimer, Miriam Rinneburger, Josefine Graffe, Thorsten Persigehl, Clemens Hentschke, Bettina Baeßler, Liliana Lourenco Caldeira, Nils Große Hokamp
European Journal of Radiology, 2024
The challenges of research data management in cardiovascular science: a DGK and DZHK position paper—executive summary
Sabine Steffens, Katrin Schröder, Martina Krüger, Christoph Maack, Katrin Streckfuss-Bömeke, Johannes Backs, Rolf Backofen, Bettina Baeßler, Yvan Devaux, Ralf Gilsbach, Jordi Heijman, Jochen Knaus, Rafael Kramann, Dominik Linz, Allyson L. Lister, Henrike Maatz, Lars Maegdefessel, Manuel Mayr, Benjamin Meder, Sara Y. Nussbeck, Eva A. Rog-Zielinska, Marcel H. Schulz, Albert Sickmann, Gökhan Yigit, Peter Kohl
Clinical Research in Cardiology, 2024
LernRad: New format with Dicom viewer and continuing education credits
Hanna Styczen
Rofo Fortschritte Auf Dem Gebiet Der Rontgenstrahlen Und Der Bildgebenden Verfahren, 2024
Native myocardial T1 mapping: influence of spatial resolution on quantitative results and reproducibility
Antonia Dalmer, Felix G. Meinel, Benjamin Böttcher, Mathias Manzke, Roberto Lorbeer, Marc-André Weber, Bettina Baeßler, Ann-Christin Klemenz
Quantitative Imaging in Medicine and Surgery, 2024
METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII
Burak Kocak, Tugba Akinci D’Antonoli, Nathaniel Mercaldo, Angel Alberich-Bayarri, Bettina Baessler, Ilaria Ambrosini, Anna E. Andreychenko, Spyridon Bakas, Regina G. H. Beets-Tan, Keno Bressem, Irene Buvat, Roberto Cannella, Luca Alessandro Cappellini, Armando Ugo Cavallo, Leonid L. Chepelev, Linda Chi Hang Chu, Aydin Demircioglu, Nandita M. deSouza, Matthias Dietzel, Salvatore Claudio Fanni, Andrey Fedorov, Laure S. Fournier, Valentina Giannini, Rossano Girometti, Kevin B. W. Groot Lipman, Georgios Kalarakis, Brendan S. Kelly, Michail E. Klontzas, Dow-Mu Koh, Elmar Kotter, Ho Yun Lee, Mario Maas, Luis Marti-Bonmati, Henning Müller, Nancy Obuchowski, Fanny Orlhac, Nikolaos Papanikolaou, Ekaterina Petrash, Elisabeth Pfaehler, Daniel Pinto dos Santos, Andrea Ponsiglione, Sebastià Sabater, Francesco Sardanelli, Philipp Seeböck, Nanna M. Sijtsema, Arnaldo Stanzione, Alberto Traverso, Lorenzo Ugga, Martin Vallières, Lisanne V. van Dijk, Joost J. M. van Griethuysen, Robbert W. van Hamersvelt, Peter van Ooijen, Federica Vernuccio, Alan Wang, Stuart Williams, Jan Witowski, Zhongyi Zhang, Alex Zwanenburg, Renato Cuocolo
Insights into Imaging, 2024
Towards reproducible radiomics research: introduction of a database for radiomics studies
Tugba Akinci D’Antonoli, Renato Cuocolo, Bettina Baessler, Daniel Pinto dos Santos
European Radiology, 2024
On the Stability of Neural Segmentation in Radiology
moritz wolter, Lokesh Veeramacheneni, Bettina Baeßler, Ulrike I. Attenberger, Barbara D. Wichtmann
Esann 2024 Proceedings 32ndeuropean Symposium on Artificial Neural Networks Computational Intelligence and Machine Learning, 2024
Addressing challenges in radiomics research: systematic review and repository of open-access cancer imaging datasets
Piotr Woznicki, Fabian Christopher Laqua, Adam Al-Haj, Thorsten Bley, Bettina Baeßler
Insights into Imaging, 2023
Clinical audit in European radiology: current status and recommendations for improvement endorsed by the European Society of Radiology (ESR)
David C. Howlett, Paulette Kumi, Roman Kloeckner, Nuria Bargallo, Bettina Baessler, Minerva Becker, Steve Ebdon-Jackson, Alexandra Karoussou-Schreiner, Christian Loewe, Marta Sans Merce, Marta Serrallonga-Mercader, Vasilis Syrgiamiotis, and
Insights into Imaging, 2023
CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII
Burak Kocak, Bettina Baessler, Spyridon Bakas, Renato Cuocolo, Andrey Fedorov, Lena Maier-Hein, Nathaniel Mercaldo, Henning Müller, Fanny Orlhac, Daniel Pinto dos Santos, Arnaldo Stanzione, Lorenzo Ugga, Alex Zwanenburg
Insights into Imaging, 2023
Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network
Miriam Rinneburger, Heike Carolus, Andra-Iza Iuga, Mathilda Weisthoff, Simon Lennartz, Nils Große Hokamp, Liliana Caldeira, Rahil Shahzad, David Maintz, Fabian Christopher Laqua, Bettina Baeßler, Tobias Klinder, Thorsten Persigehl
European Radiology Experimental, 2023
Denoising diffusion probabilistic models for 3D medical image generation
Firas Khader, Gustav Müller-Franzes, Soroosh Tayebi Arasteh, Tianyu Han, Christoph Haarburger, Maximilian Schulze-Hagen, Philipp Schad, Sandy Engelhardt, Bettina Baeßler, Sebastian Foersch, Johannes Stegmaier, Christiane Kuhl, Sven Nebelung, Jakob Nikolas Kather, Daniel Truhn
Scientific Reports, 2023
Trends and statistics of artificial intelligence and radiomics research in Radiology, Nuclear Medicine, and Medical Imaging: bibliometric analysis
Burak Kocak, Bettina Baessler, Renato Cuocolo, Nathaniel Mercaldo, Daniel Pinto dos Santos
European Radiology, 2023
An overview and a roadmap for artificial intelligence in hematology and oncology
Wiebke Rösler, Michael Altenbuchinger, Bettina Baeßler, Tim Beissbarth, Gernot Beutel, Robert Bock, Nikolas von Bubnoff, Jan-Niklas Eckardt, Sebastian Foersch, Chiara M. L. Loeffler, Jan Moritz Middeke, Martha-Lena Mueller, Thomas Oellerich, Benjamin Risse, André Scherag, Christoph Schliemann, Markus Scholz, Rainer Spang, Christian Thielscher, Ioannis Tsoukakis, Jakob Nikolas Kather
Journal of Cancer Research and Clinical Oncology, 2023
Transfer-Learning Deep Radiomics and Hand-Crafted Radiomics for Classifying Lymph Nodes from Contrast-Enhanced Computed Tomography in Lung Cancer
Fabian Christopher Laqua, Piotr Woznicki, Thorsten A. Bley, Mirjam Schöneck, Miriam Rinneburger, Mathilda Weisthoff, Matthias Schmidt, Thorsten Persigehl, Andra-Iza Iuga, Bettina Baeßler
Cancers, 2023
Automation Bias in Mammography: The Impact of Artificial Intelligence BI-RADS Suggestions on Reader Performance
Thomas Dratsch, Xue Chen, Mohammad Rezazade Mehrizi, Roman Kloeckner, Aline Mähringer-Kunz, Michael Püsken, Bettina Baeßler, Stephanie Sauer, David Maintz, Daniel Pinto dos Santos
Radiology, 2023
European Association of Nuclear Medicine (EANM) Focus 4 consensus recommendations: molecular imaging and therapy in haematological tumours
Cristina Nanni, Carsten Kobe, Bettina Baeßler, Christian Baues, Ronald Boellaard, Peter Borchmann, Andreas Buck, Irène Buvat, Björn Chapuy, Bruce D Cheson, Robert Chrzan, Ann-Segolene Cottereau, Ulrich Dührsen, Live Eikenes, Martin Hutchings, Wojciech Jurczak, Françoise Kraeber-Bodéré, Egesta Lopci, Stefano Luminari, Steven MacLennan, N George Mikhaeel, Marcel Nijland, Paula Rodríguez-Otero, Giorgio Treglia, Nadia Withofs, Elena Zamagni, Pier Luigi Zinzani, Josée M Zijlstra, Ken Herrmann, Jolanta Kunikowska
Lancet Haematology, 2023
Influence of Image Processing on Radiomic Features from Magnetic Resonance Imaging
Barbara D. Wichtmann, Felix N. Harder, Kilian Weiss, Stefan O. Schönberg, Ulrike I. Attenberger, Hatem Alkadhi, Daniel Pinto dos Santos, Bettina Baeßler
Investigative Radiology, 2023
Opportunities instead of deficits - This is how inclusion works in radiology!
Rofo Fortschritte Auf Dem Gebiet Der Rontgenstrahlen Und Der Bildgebenden Verfahren, 2023
Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective
Bettina Baeßler, Michael Götz, Charalambos Antoniades, Julius F. Heidenreich, Tim Leiner, Meinrad Beer
Frontiers in Cardiovascular Medicine, 2023
Automated Kidney and Liver Segmentation in MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease: A Multicenter Study
Piotr Woznicki, Florian Siedek, Maatje D.A. van Gastel, Daniel Pinto dos Santos, Sita Arjune, Larina Karner, Franziska Meyer, Thorsten Persigehl, Ronald T. Gansevoort, Franziska Grundmann, Bettina Baessler, Roman-Ulrich Müller
Kidney360, 2022
Semi-automated volumetry of pulmonary nodules: Intra-individual comparison of standard dose and chest X-ray equivalent ultralow dose chest CT scans
Thorsten Ottilinger, Katharina Martini, Bettina Baessler, Thomas Sartoretti, Ralf W. Bauer, Sebastian Leschka, Elisabeth Sartoretti, Joan E. Walter, Thomas Frauenfelder, Simon Wildermuth, Hatem Alkadhi, Michael Messerli
European Journal of Radiology, 2022
Correction: Impact of myocardial injury on regional left ventricular function in the course of acute myocarditis with preserved ejection fraction: insights from segmental feature tracking strain analysis using cine cardiac MRI (The International Journal of Cardiovascular Imaging, (2022), 38, 8, (1851-1861), 10.1007/s10554-022-02601-3)
L. Weber, J. M. Sokolska, T. Nadarevic, M. Karolyi, B. Baessler, X. Fischer, M. Sokolski, J. von Spiczak, M. Polacin, I. Matziris, H. Alkadhi, R. Manka
International Journal of Cardiovascular Imaging, 2022
Impact of myocardial injury on regional left ventricular function in the course of acute myocarditis with preserved ejection fraction: insights from segmental feature tracking strain analysis using cine cardiac MRI
L. Weber, J. M. Sokolska, T. Nadarevic, M. Karolyi, B. Baessler, X. Fischer, M. Sokolski, J. von Spiczak, M. Polacin, I. Matziris, H. Alkadhi, M. Robert
International Journal of Cardiovascular Imaging, 2022
Influence of CT Image Matrix Size and Kernel Type on the Assessment of HRCT in Patients with SSC-ILD
Bettina D. Balmer, Christian Blüthgen, Bettina Bässler, Katharina Martini, Florian A. Huber, et al.
Diagnostics, 2022
Value of Radiomics of Perinephric Fat for Prediction of Intraoperative Complexity in Renal Tumor Surgery
Julia Mühlbauer, Maximilian C. Kriegmair, Lale Schöning, Luisa Egen, Karl-Friedrich Kowalewski, Niklas Westhoff, Philipp Nuhn, Fabian C. Laqua, Bettina Baessler
Urologia Internationalis, 2022
Comparison of detection of trauma-related injuries using combined “all-in-one” fused images and conventionally reconstructed images in acute trauma CT
Kai Higashigaito, Gioia Fischer, Lisa Jungblut, Christian Blüthgen, Moritz Schwyzer, Matthias Eberhard, Daniel Pinto dos Santos, Bettina Baessler, Pieter Vuylsteke, Joris A. M. Soons, Thomas Frauenfelder
European Radiology, 2022
Challenges in ensuring the generalizability of image quantitation methods for MRI
Kathryn E. Keenan, Jana G. Delfino, Kalina V. Jordanova, Megan E. Poorman, Prathyush Chirra, Akshay S. Chaudhari, Bettina Baessler, Jessica Winfield, Satish E. Viswanath, Nandita M. deSouza
Medical Physics, 2022
Robustness of dual-energy CT-derived radiomic features across three different scanner types
Simon Lennartz, Aileen O’Shea, Anushri Parakh, Thorsten Persigehl, Bettina Baessler, Avinash Kambadakone
European Radiology, 2022
Radiomics for detecting prostate cancer bone metastases invisible in CT: a proof-of-concept study
Ricarda Hinzpeter, Livia Baumann, Roman Guggenberger, Martin Huellner, Hatem Alkadhi, Bettina Baessler
European Radiology, 2022
Development, validation, qualification, and dissemination of quantitative MR methods: Overview and recommendations by the ISMRM quantitative MR study group
Sebastian Weingärtner, Kimberly L. Desmond, Nancy A. Obuchowski, Bettina Baessler, Yuxin Zhang, Emma Biondetti, Dan Ma, Xavier Golay, Michael A. Boss, Jeffrey L. Gunter, Kathryn E. Keenan, Diego Hernando, and
Magnetic Resonance in Medicine, 2022
The Future of Spectral CT: Radiomics and Beyond
Bettina Baessler, Davide Cester
Medical Radiology, 2022
AutoRadiomics: A Framework for Reproducible Radiomics Research
Piotr Woznicki, Fabian Laqua, Thorsten Bley, Bettina Baeßler
Frontiers in Radiology, 2022
Segmental strain analysis for the detection of chronic ischemic scars in non-contrast cardiac MRI cine images
M. Polacin, M. Karolyi, M. Eberhard, A. Gotschy, B. Baessler, H. Alkadhi, S. Kozerke, R. Manka
Scientific Reports, 2021
Computed tomography radiomics for the rediction of thymic epithelial tumor histology, TNM stage and myasthenia gravis
Christian Blüthgen, Miriam Patella, André Euler, Bettina Baessler, Katharina Martini, Jochen von Spiczak, Didier Schneiter, Isabelle Opitz, Thomas Frauenfelder
Plos One, 2021
k-t accelerated multi-VENC 4D flow MRI improves vortex assessment in pulmonary hypertension
Jan Robert Kroeger, Marc Stackl, Kilian Weiss, Bettina Baeßler, Felix Gerhardt, Stephan Rosenkranz, David Maintz, Daniel Giese, Alexander Christian Bunck
European Journal of Radiology, 2021
Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks
Andra-Iza Iuga, Heike Carolus, Anna J. Höink, Tom Brosch, Tobias Klinder, David Maintz, Thorsten Persigehl, Bettina Baeßler, Michael Püsken
BMC Medical Imaging, 2021
Virtual monoenergetic images of dual-energy CT—impact on repeatability, reproducibility, and classification in radiomics
André Euler, Fabian Christopher Laqua, Davide Cester, Niklas Lohaus, Thomas Sartoretti, Daniel Pinto dos Santos, Hatem Alkadhi, Bettina Baessler
Cancers, 2021
How COVID-19 kick-started online learning in medical education-The DigiMed study
Fabian Stoehr, Lukas Müller, Adrian Brady, Antoni Trilla, Aline Mähringer-Kunz, Felix Hahn, Christoph Düber, Nicole Becker, Marcus-Alexander Wörns, Julius Chapiro, Jan Bernd Hinrichs, Deniz Akata, Stephan Ellmann, Merel Huisman, David Koff, Sebastian Brinkmann, Fabian Bamberg, Oscar Zimmermann, Nikoleta I. Traikova, Jens U. Marquardt, D.-H. Chang, Fabian Rengier, Timo A. Auer, Tilman Emrich, Felix Muehler, Heinz Schmidberger, Bettina Baeßler, Daniel Pinto dos Santos, Roman Kloeckner
Plos One, 2021
Can magnetic resonance imaging radiomics of the pancreas predict postoperative pancreatic fistula?
Stephan M. Skawran, Patryk Kambakamba, Bettina Baessler, Jochen von Spiczak, Michael Kupka, Philip C. Müller, Beat Moeckli, Michael Linecker, Henrik Petrowsky, Caecilia S. Reiner
European Journal of Radiology, 2021
Dual-Energy CT-Based Iodine Quantification in Liver Tumors – Impact of Scan-, Patient-, and Position-Related Factors
Cynthia Schmidt, Bettina Baessler, Dominik Nakhostin, Arijit Das, Matthias Eberhard, Hatem Alkadhi, André Euler
Academic Radiology, 2021
Machine learning in cardiovascular radiology: ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges
Thomas Weikert, Marco Francone, Suhny Abbara, Bettina Baessler, Byoung Wook Choi, Matthias Gutberlet, Elizabeth M. Hecht, Christian Loewe, Elie Mousseaux, Luigi Natale, Konstantin Nikolaou, Karen G. Ordovas, Charles Peebles, Claudia Prieto, Rodrigo Salgado, Birgitta Velthuis, Rozemarijn Vliegenthart, Jens Bremerich, Tim Leiner
European Radiology, 2021
Velocity quantification in 44 healthy volunteers using accelerated multi-VENC 4D flow CMR
Jan Robert Kroeger, Francesca Claudia Pavesio, Richard Mörsdorf, Kilian Weiss, Alexander Christian Bunck, Bettina Baeßler, David Maintz, Daniel Giese
European Journal of Radiology, 2021
Practical applications of deep learning: classifying the most common categories of plain radiographs in a PACS using a neural network
Thomas Dratsch, Michael Korenkov, David Zopfs, Sebastian Brodehl, Bettina Baessler, Daniel Giese, Sebastian Brinkmann, David Maintz, Daniel Pinto dos Santos
European Radiology, 2021
Applicability of radiomics in interstitial lung disease associated with systemic sclerosis: proof of concept
K. Martini, B. Baessler, M. Bogowicz, C. Blüthgen, M. Mannil, S. Tanadini-Lang, J. Schniering, B. Maurer, T. Frauenfelder
European Radiology, 2021
Radiomics in renal cell carcinoma—a systematic review and meta-analysis
Julia Mühlbauer, Luisa Egen, Karl-Friedrich Kowalewski, Maurizio Grilli, Margarete T. Walach, Niklas Westhoff, Philipp Nuhn, Fabian C. Laqua, Bettina Baessler, Maximilian C. Kriegmair
Cancers, 2021
Artificial intelligence in radiology - Definition, potential and challenges
Bettina Baessler
Praxis, 2021
A decade of radiomics research: are images really data or just patterns in the noise?
Daniel Pinto dos Santos, Matthias Dietzel, Bettina Baessler
European Radiology, 2021
Comparison of 3D and 2D late gadolinium enhancement magnetic resonance imaging in patients with acute and chronic myocarditis
M. Polacin, I. Kapos, M. Gastl, C. Blüthgen, M. Karolyi, J. von Spiczak, M. Eberhard, B. Baessler, H. Alkadhi, S. Kozerke, R. Manka
International Journal of Cardiovascular Imaging, 2021
Radiomics in medical imaging—“how-to” guide and critical reflection
Janita E. van Timmeren, Davide Cester, Stephanie Tanadini-Lang, Hatem Alkadhi, Bettina Baessler
Insights into Imaging, 2020
Artificial Intelligence and Texture Analysis in Cardiac Imaging
Manoj Mannil, Matthias Eberhard, Jochen von Spiczak, Walter Heindel, Hatem Alkadhi, Bettina Baessler
Current Cardiology Reports, 2020
Radiomics for liver tumours
Constantin Dreher, Philipp Linde, Judit Boda-Heggemann, Bettina Baessler
Strahlentherapie Und Onkologie, 2020
Noncontrast Quantitative Imaging Biomarkers Reflecting Myocardial Tissue Heterogeneity: The Future of Cardiac Magnetic Resonance Imaging?
Bettina Baessler
Jacc Cardiovascular Imaging, 2020
Re: Radiomics Allows for Detection of Benign and Malignant Histopathology in Patients with Metastatic Testicular Germ Cell Tumors prior to Post-Chemotherapy Retroperitoneal Lymph Node Dissection
Jerome P. Richie
Journal of Urology, 2020
Endovascular simulation training: a tool to increase enthusiasm for interventional radiology among medical students
Fabian Stoehr, Sebastian Schotten, Michael B. Pitton, Christoph Dueber, Franziska Schmidt, Nienke L. Hansen, Bettina Baeßler, Roman Kloeckner, Daniel Pinto dos Santos
European Radiology, 2020
1024-pixel image matrix for chest CT – Impact on image quality of bronchial structures in phantoms and patients
André Euler, Katharina Martini, Bettina Baessler, Matthias Eberhard, Friederike Schoeck, Hatem Alkadhi, Thomas Frauenfelder
Plos One, 2020
Radiomics allows for detection of benign and malignant histopathology in patients with metastatic testicular germ cell tumors prior to post-chemotherapy retroperitoneal lymph node dissection
Bettina Baessler, Tim Nestler, Daniel Pinto dos Santos, Pia Paffenholz, Vikram Zeuch, David Pfister, David Maintz, Axel Heidenreich
European Radiology, 2020
Magnetic Resonance Kidney Parenchyma-T2 as a Novel Imaging Biomarker for Autosomal Dominant Polycystic Kidney Disease
Florian Siedek, Franziska Grundmann, Kilian Weiss, Daniel Pinto dos Santos, Sita Arjune, Stefan Haneder, Thorsten Persigehl, Roman-Ulrich Müller, Bettina Baessler
Investigative Radiology, 2020
Image-Based Cardiac Diagnosis With Machine Learning: A Review
Carlos Martin-Isla, Victor M. Campello, Cristian Izquierdo, Zahra Raisi-Estabragh, Bettina Baeßler, Steffen E. Petersen, Karim Lekadir
Frontiers in Cardiovascular Medicine, 2020
Peripheral Vascular Anomalies - Essentials in Periinterventional Imaging
Maliha Sadick, Daniel Overhoff, Bettina Baessler, Naema von Spangenberg, Lena Krebs, Walter A. Wohlgemuth
Rofo Fortschritte Auf Dem Gebiet Der Rontgenstrahlen Und Der Bildgebenden Verfahren, 2020
Structured report data can be used to develop deep learning algorithms: a proof of concept in ankle radiographs
Daniel Pinto dos Santos, Sebastian Brodehl, Bettina Baeßler, Gordon Arnhold, Thomas Dratsch, Seung-Hun Chon, Peter Mildenberger, Florian Jungmann
Insights into Imaging, 2019
Accuracy of Radiomics-Based Feature Analysis on Multiparametric Magnetic Resonance Images for Noninvasive Meningioma Grading
Kai Roman Laukamp, Georgy Shakirin, Bettina Baeßler, Frank Thiele, David Zopfs, Nils Große Hokamp, Marco Timmer, Christoph Kabbasch, Michael Perkuhn, Jan Borggrefe
World Neurosurgery, 2019
Multitasking T1 mapping in acute myocardial infarction
Rofo Fortschritte Auf Dem Gebiet Der Rontgenstrahlen Und Der Bildgebenden Verfahren, 2019
Machine learning in cardiovascular magnetic resonance: Basic concepts and applications
Tim Leiner, Daniel Rueckert, Avan Suinesiaputra, Bettina Baeßler, Reza Nezafat, Ivana Išgum, Alistair A. Young
Journal of Cardiovascular Magnetic Resonance, 2019
Systematic prostate biopsy still matters: A comprehensive analysis of MRI/TRUS-fusion targeted prostate biopsies across different indications
Niklas Westhoff, Bettina Baeßler, Jost von Hardenberg, Svetlana Hetjens, Stefan Porubsky, Fabian Siegel, Thomas Martini, Maurice Stephan Michel, Ulrike Attenberger, Manuel Ritter
Urologic Oncology Seminars and Original Investigations, 2019
Precision, reproducibility and applicability of an undersampled multi-venc 4D flow MRI sequence for the assessment of cardiac hemodynamics
Richard Moersdorf, Melanie Treutlein, Jan Robert Kroeger, Bram Ruijsink, James Wong, David Maintz, Kilian Weiss, Alexander C. Bunck, Bettina Baeßler, Daniel Giese
Magnetic Resonance Imaging, 2019
Robustness and Reproducibility of Radiomics in Magnetic Resonance Imaging: A Phantom Study
Bettina Baeßler, Kilian Weiss, Daniel Pinto dos Santos
Investigative Radiology, 2019
Medical students' attitude towards artificial intelligence: a multicentre survey
D. Pinto dos Santos, D. Giese, S. Brodehl, S. H. Chon, W. Staab, R. Kleinert, D. Maintz, B. Baeßler
European Radiology, 2019
Knowledge-based iterative reconstructions for imaging of coronary artery stents: first in-vitro experience and comparison of different radiation dose levels and kernel settings
Tilman Hickethier, Bettina Baeßler, Jan Robert Kroeger, Dirk Müller, David Maintz, Guido Michels, Alexander C Bunck
Acta Radiologica, 2019
Cardiac MRI and texture analysis of myocardial T1 and T2 maps in myocarditis with acute versus chronic symptoms of heart failure
Bettina Baessler, Christian Luecke, Julia Lurz, Karin Klingel, Arijit Das, et al.
Radiology, 2019
Big data, artificial intelligence, and structured reporting
Daniel Pinto dos Santos, Bettina Baeßler
European Radiology Experimental, 2018
Software-automated multidetector computed tomography-based prosthesis-sizing in transcatheter aortic valve replacement: Inter-vendor comparison and relation to patient outcome
Bettina Baeßler, Victor Mauri, Alexander C. Bunck, Daniel Pinto dos Santos, Kai Friedrichs, et al.
International Journal of Cardiology, 2018
Cardiac MRI texture analysis of T1 and T2 maps in patients with infarctlike acute myocarditis
Bettina Baessler, Christian Luecke, Julia Lurz, Karin Klingel, Maximilian von Roeder, et al.
Radiology, 2018
Myocardial T1 and T2 mapping in severe aortic stenosis: Potential novel insights into the pathophysiology of myocardial remodelling
Ana Fehrmann, Melanie Treutlein, Tanja Rudolph, Volker Rudolph, Kilian Weiss, et al.
European Journal of Radiology, 2018
The role of cardiovascular magnetic resonance imaging in rheumatic heart disease
Clinical and Experimental Rheumatology, 2018
Incremental value of cardiovascular magnetic resonance feature tracking derived atrial and ventricular strain parameters in a comprehensive approach for the diagnosis of acute myocarditis
Jonas Doerner, Alexander C. Bunck, Guido Michels, David Maintz, Bettina Baeßler
European Journal of Radiology, 2018
Whole-body computed tomography in trauma patients: Optimization of the patient scanning position significantly shortens examination time while maintaining diagnostic image quality
Tilman Hickethier, Kamal Mammadov, Bettina Baeßler, Thorsten Lichtenstein, Jochen Hinkelbein, et al.
Therapeutics and Clinical Risk Management, 2018
Texture analysis and machine learning of non-contrast T1-weighted MR images in patients with hypertrophic cardiomyopathy—Preliminary results
Bettina Baeßler, Manoj Mannil, David Maintz, Hatem Alkadhi, Robert Manka
European Journal of Radiology, 2018
In vitro evaluation of flow patterns and turbulent kinetic energy in trans-catheter aortic valve prostheses
Daniel Giese, Kilian Weiss, Bettina Baeßler, Navid Madershahian, Yeong-Hoon Choi, et al.
Magnetic Resonance Materials in Physics Biology and Medicine, 2018
Subacute and chronic left ventricular myocardial scar: Accuracy of texture analysis on nonenhanced cine MR images
Bettina Baessler, Manoj Mannil, Sabrina Oebel, David Maintz, Hatem Alkadhi, et al.
Radiology, 2018
Effect of resistance training with vibration and compression on the formation of muscle and bone
Christoph Zinner, Bettina Baessler, Kilian Weiss, Jasmine Ruf, Guido Michels, et al.
Muscle and Nerve, 2017
Re-evaluation of a novel approach for quantitative myocardial oedema detection by analysing tissue inhomogeneity in acute myocarditis using T2-mapping
Bettina Baeßler, Frank Schaarschmidt, Melanie Treutlein, Christian Stehning, Bernhard Schnackenburg, et al.
European Radiology, 2017
Magnetic resonance T2 mapping and diffusion-weighted imaging for early detection of cystogenesis and response to therapy in a mouse model of polycystic kidney disease
Mareike Franke, Bettina Baeßler, Jan Vechtel, Claudia Dafinger, Martin Höhne, et al.
Kidney International, 2017
A novel multiparametric imaging approach to acute myocarditis using T2-mapping and CMR feature tracking
Bettina Baeßler, Melanie Treutlein, Frank Schaarschmidt, Christian Stehning, Bernhard Schnackenburg, et al.
Journal of Cardiovascular Magnetic Resonance, 2017
Left and right atrial feature tracking in acute myocarditis: A feasibility study
Anastasia Dick, Björn Schmidt, Guido Michels, Alexander C. Bunck, David Maintz, et al.
European Journal of Radiology, 2017
Intra- and inter-observer reproducibility of global and regional magnetic resonance feature tracking derived strain parameters of the left and right ventricle
Björn Schmidt, Anastasia Dick, Melanie Treutlein, Petra Schiller, Alexander C. Bunck, et al.
European Journal of Radiology, 2017
Monoenergetic reconstructions for imaging of coronary artery stents using spectral detector CT: In-vitro experience and comparison to conventional images
Tilman Hickethier, Bettina Baeßler, Jan Robert Kroeger, Jonas Doerner, Gregor Pahn, et al.
Journal of Cardiovascular Computed Tomography, 2017
Reproducibility of three different cardiac T2-mapping sequences at 1.5T
Bettina Baeßler, Frank Schaarschmidt, Christian Stehning, Bernhard Schnackenburg, Agathe Giolda, et al.
Journal of Magnetic Resonance Imaging, 2016
63-year old man with dyspnea and right heart failure
Guido Michels, Heike Göbel, Bettina Baeßler, Roman Pfister
Deutsche Medizinische Wochenschrift 1946, 2016
Imaging procedures for colorectal cancer
Bettina Baeßler, David Maintz, Thorsten Persigehl
Visceral Medicine, 2016
Diagnostic implications of magnetic resonance feature tracking derived myocardial strain parameters in acute myocarditis
Bettina Baeßler, Frank Schaarschmidt, Anastasia Dick, Guido Michels, David Maintz, et al.
European Journal of Radiology, 2016
Cardiovascular magnetic resonance feature tracking derived strain parameters in patients with acute myocarditis and preserved ejection fraction: A validation study
Bettina Baessler, Frank Schaarschmidt, Laura Quellhorst, Anastasia Dick, Guido Michels, et al.
Journal of Cardiovascular Magnetic Resonance, 2016
Non-invasive imaging of bioresorbable coronary scaffolds using CT and MRI: First in vitro experience
Tilman Hickethier, Jan Robert Kröger, Jochen Von Spiczak, Bettina Baessler, Roman Pfister, et al.
International Journal of Cardiology, 2016
A novel analytical approach to quantitative myocardial EDEMA imaging in acute myocarditis using T2-mapping
Bettina Baessler, Frank Schaarschmidt, Bernhard Schnackenburg, Christian Stehning, Melanie Treutlein, et al.
Journal of Cardiovascular Magnetic Resonance, 2016
Mapping tissue inhomogeneity in acute myocarditis: A novel analytical approach to quantitative myocardial edema imaging by T2-mapping
Bettina Baeßler, Frank Schaarschmidt, Anastasia Dick, Christian Stehning, Bernhard Schnackenburg, et al.
Journal of Cardiovascular Magnetic Resonance, 2015
A systematic evaluation of three different cardiac T2-mapping sequences at 1.5 and 3T in healthy volunteers
Bettina Baeßler, Frank Schaarschmidt, Christian Stehning, Bernhard Schnackenburg, David Maintz, et al.
European Journal of Radiology, 2015
Cardiac T2-mapping using a fast gradient echo spin echo sequence - First in vitro and in vivo experience
Bettina Baeßler, Frank Schaarschmidt, Christian Stehning, Bernhard Schnackenburg, David Maintz, et al.
Journal of Cardiovascular Magnetic Resonance, 2015
Abnormal gadolinium kinetics in cardiac MRI - Always an indication of cardiac amyloidosis?
B. Baeßler, H. Reuter, M. Huntgeburth, A. Bunck
Rofo Fortschritte Auf Dem Gebiet Der Rontgenstrahlen Und Der Bildgebenden Verfahren, 2015
Biventricular myocardial strain analysis in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC) using cardiovascular magnetic resonance feature tracking
Philipp Heermann, Dennis M Hedderich, Matthias Paul, Christoph Schülke, Jan Robert Kroeger, et al.
Journal of Cardiovascular Magnetic Resonance, 2014
Case of myocardial relapse of a T-cell lymphoma after hematopoietic stem cell transplantation demonstrated by cardiovascular magnetic resonance and endomyocardial biopsy
Bettina Baeßler, Volker Rudolph, Nicolaus Friedrichs, David Maintz, Alexander C. Bunck
Circulation, 2014
Lymphocyte apoptosis in sepsis and potential anti-apoptotic strategies
S. Weber, B. Baessler, S. Schroeder
Yearbook of Intensive Care and Emergency Medicine 2009, 2007

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