[FG] Medical Imaging Research Group

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Wetterauer, Christian et al. (2024) ‘Opportunistic Prostate Cancer Screening with Biparametric Magnetic Resonance Imaging (VISIONING)’, European Urology Focus. 16.07.2024, 10(2), pp. 332–338. Available at: https://doi.org/10.1016/j.euf.2024.02.006.

Lo Piccolo, Francesca et al. (2023) ‘Impact of retraining a deep learning algorithm for improving guideline-compliant aortic diameter measurements on non-gated chest CT’, European Journal of Radiology, 168, p. 111093. Available at: https://doi.org/10.1016/j.ejrad.2023.111093.

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Heye, Tobias et al. (2023) ‘Turn It Off! A Simple Method to Save Energy and CO 2 Emissions in a Hospital Setting with Focus on Radiology by Monitoring Nonproductive Energy-consuming Devices’, Radiology, 307. Available at: https://doi.org/10.1148/radiol.230162.

Lo Piccolo, Francesca et al. (2023) ‘Impact of retraining a deep learning algorithm for improving guideline-compliant aortic diameter measurements on non-gated chest CT’, European Journal of Radiology, 168. Available at: https://doi.org/10.1016/j.ejrad.2023.111093.

Merkle, Elmar M. (2023) ‘The potential of low-field MRI in abdominal imaging’, European Radiology, 33, pp. 6981–6983. Available at: https://doi.org/10.1007/s00330-023-09676-z.

Merkle, Elmar M., Bamberg, Fabian and Vosshenrich, Jan (2023) ‘The Impact of Modern Imaging Techniques on Carbon Footprints: Relevance and Outlook’, European Urology Focus, 9, pp. 891–893. Available at: https://doi.org/10.1016/j.euf.2023.09.009.

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Wasserthal, Jakob et al. (2023) ‘TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images’, Radiology: Artificial Intelligence, 5. Available at: https://doi.org/10.1148/ryai.230024.

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Rusche, Thilo et al. (2022) ‘More Space, Less Noise—New-generation Low-Field Magnetic Resonance Imaging Systems Can Improve Patient Comfort: A Prospective 0.55T–1.5T-Scanner Comparison’, Journal of Clinical Medicine, 11. Available at: https://doi.org/10.3390/jcm11226705.

Glessgen CG et al. (2022) ‘Respiratory anomalies associated with gadoxetate disodium and gadoterate meglumine: compressed sensing MRI revealing physiologic phenomena during the entire injection cycle’, EUR RADIOL , 32, pp. 346–354.

Pradella M et al. (2021) ‘Fully automated guideline-compliant diameter measurements of the thoracic aorta on ECG-gated CT angiography using deep learning’, Quantitative Imaging in Medicine and Surgery, 11(10), pp. 4245–4257. Available at: https://doi.org/10.21037/qims-21-142.

Tobler P et al. (2021) ‘AI-based detection and classification of distal radius fractures using low-effort data labeling: evaluation of applicability and effect of training set size.’, European radiology, 31(9), pp. 6816–6824. Available at: https://doi.org/10.1007/s00330-021-07811-2.

Antwi K et al. (2021) ‘Research article investigating difficult to detect pancreatic lesions: Characterization of benign pancreatic islet cell tumors using multiparametric pancreatic 3-T MRI’, PLoS ONE, 16(6 June 2021), p. e0253078. Available at: https://doi.org/10.1371/journal.pone.0253078.

Vosshenrich J et al. (2021) ‘Revealing the most common reporting errors through data mining of the report proofreading process’, European Radiology, 31(4), pp. 2115–2125. Available at: https://doi.org/10.1007/s00330-020-07306-6.

Vosshenrich J et al. (2021) ‘Quantifying radiology resident fatigue: Analysis of preliminary reports’, Radiology, 298(3), pp. 632–639. Available at: https://doi.org/10.1148/radiol.2021203486.

Anastasopoulos, Constantin et al. (2021) ‘Atri-U: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium’, Journal of Cardiovascular Magnetic Resonance (JCMR)e Society for Cardiovascular Magnetic Resonance, 23(1), p. 133. Available at: https://doi.org/10.1186/s12968-021-00791-8.

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Weikert T et al. (2020) ‘Assessment of a deep learning algorithm for the detection of rib fractures on whole-body trauma computed tomography’, Korean Journal of Radiology, 21(7), pp. 891–899. Available at: https://doi.org/10.3348/kjr.2019.0653.

Heye T et al. (2020) ‘The energy consumption of Radiology: Energy- And cost-saving opportunities for CT and MRI operation’, Radiology, 295(3), pp. 593–605. Available at: https://doi.org/10.1148/radiol.2020192084.

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Heilmaier C et al. (2019) ‘Correction to: National survey on dose data analysis in computed tomography.’, European radiology, 29(2), p. 1063. Available at: https://doi.org/10.1007/s00330-018-5649-y.

Glessgen CG et al. (2019) ‘Gadoxetate Disodium versus Gadoterate Meglumine: Quantitative respiratory and hemodynamic metrics by using compressed-sensing MRI’, Radiology, 293(2), pp. 317–326. Available at: https://doi.org/10.1148/radiol.2019190187.

Hirschmann A et al. (2019) ‘Artificial Intelligence in Musculoskeletal Imaging: Review of Current Literature, Challenges, and Trends’, Seminars in Musculoskeletal Radiology, 23(3), pp. 304–311. Available at: https://doi.org/10.1055/s-0039-1684024.

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Heye T et al. (2018) ‘JOURNAL CLUB: Structured Reporting: The Voice of the Customer in an Ongoing Debate About the Future of Radiology Reporting.’, AJR. American journal of roentgenology. 21.09.2018, 211(5), pp. 964–970. Available at: https://doi.org/10.2214/ajr.18.19714.

Knecht S et al. (2018) ‘Left atrial anatomy, atrial fibrillation burden, and P-wave duration-relationships and predictors for single-procedure success after pulmonary vein isolation.’, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology, 20(2), pp. 271–278. Available at: https://doi.org/10.1093/europace/euw376.

Euler A et al. (2017) ‘Erratum to: Impact of model-based iterative reconstruction on low-contrast lesion detection and image quality in abdominal CT: a 12-reader-based comparative phantom study with filtered back projection at different tube voltages.’, 27(12). Available at: https://doi.org/10.1007/s00330-017-4985-7.

Euler A et al. (2017) ‘Impact of model-based iterative reconstruction on low-contrast lesion detection and image quality in abdominal CT: a 12-reader-based comparative phantom study with filtered back projection at different tube voltages.’, European radiology. 03.04.2017, 27(12), pp. 5252–5259. Available at: https://doi.org/10.1007/s00330-017-4825-9.

Ho MJ et al. (2017) ‘Evaluation of Reproducibility of Diffusion Tensor Imaging in the Brachial Plexus at 3.0 T.’, Investigative radiology, 52(8), pp. 482–487. Available at: https://doi.org/10.1097/rli.0000000000000363.

Hainc N et al. (2017) ‘Experimental Texture Analysis in Glioblastoma: A Methodological Study.’, Investigative radiology, 52(6), pp. 367–373. Available at: https://doi.org/10.1097/rli.0000000000000354.

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Euler A, Stieltjes B and Schindera ST (2017) ‘Automatic tube current modulation for whole-body polytrauma CT with immobilization devices: is there an increase in radiation dose and degradation of image quality?’, Emergency radiology. 10.09.2016, 24(1), pp. 31–37. Available at: https://doi.org/10.1007/s10140-016-1442-x.

Hainc N et al. (2017) ‘The Bright, Artificial Intelligence-Augmented Future of Neuroimaging Reading.’, Frontiers in neurology. 21.09.2017, 8, p. 489. Available at: https://doi.org/10.3389/fneur.2017.00489.

Wolf RC et al. (2016) ‘Structural network changes in patients with major depression and schizophrenia treated with electroconvulsive therapy.’, European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology. 12.07.2016, 26(9), pp. 1465–1474. Available at: https://doi.org/10.1016/j.euroneuro.2016.06.008.

Heye T et al. (2016) ‘Portrayal of radiology in a major medical television series: How does it influence the perception of radiology among patients and radiology professionals?’, European radiology. 11.11.2015, 26(8), pp. 2863–9. Available at: https://doi.org/10.1007/s00330-015-4079-3.

Klauß M et al. (2016) ‘IVIM DW-MRI of autoimmune pancreatitis: therapy monitoring and differentiation from pancreatic cancer.’, European radiology. 08.10.2015, 26(7), pp. 2099–106. Available at: https://doi.org/10.1007/s00330-015-4041-4.

Klauss M et al. (2016) ‘IVIM-diffusion-MRI for the differentiation of solid benign and malign hypervascular liver lesions-Evaluation with two different MR scanners.’, European journal of radiology. 16.04.2016, 85(7), pp. 1289–94. Available at: https://doi.org/10.1016/j.ejrad.2016.04.011.

Hirjak D et al. (2016) ‘Neuroanatomical Markers of Neurological Soft Signs in Recent-Onset Schizophrenia and Asperger-Syndrome.’, Brain topography. 26.12.2015, 29(3), pp. 382–94. Available at: https://doi.org/10.1007/s10548-015-0468-9.

Neri E et al. (2016) ‘ESGAR consensus statement on liver MR imaging and clinical use of liver-specific contrast agents.’, pp. 921–31. Available at: https://doi.org/10.1007/s00330-015-3900-3.

Merkle E.M. et al. (2016) ‘Consensus report from the 7th International Forum for Liver Magnetic Resonance Imaging’, European Radiology, 26(3), pp. 674–682. Available at: https://doi.org/10.1007/s00330-015-3873-2.

Boeck, Lucas et al. (2016) ‘Single-Breath Washout Tests to Assess Small Airway Disease in COPD’, Chest, 150(5), pp. 1091–1100. Available at: https://doi.org/10.1016/j.chest.2016.05.019.

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Kim CY et al. (2015) ‘Respiratory-gated noncontrast SPACE MR angiography sequence at 3T for evaluation of the central veins of the chest: a feasibility study.’, Journal of magnetic resonance imaging : JMRI, 41(1), pp. 67–73. Available at: https://doi.org/10.1002/jmri.24540.

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Ringe KI et al. (2014) ‘Gadoxetate disodium in patients with primary sclerosing cholangitis: an analysis of hepatobiliary contrast excretion.’, Journal of magnetic resonance imaging : JMRI. 31.10.2013, 40(1), pp. 106–12. Available at: https://doi.org/10.1002/jmri.24381.

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