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  • 1
    In: Bioelectromagnetics, Wiley, Vol. 42, No. 1 ( 2021-01), p. 37-50
    Abstract: Exposure to radiofrequency (RF) power deposition during magnetic resonance imaging (MRI) induces elevated body‐tissue temperatures and may cause changes in heart and breathing rates, disturbing thermoregulation. Eleven temperature sensors were placed in muscle tissue and one sensor in the rectum (measured in 10 cm depth) of 20 free‐breathing anesthetized pigs to verify temperature curves during RF exposure. Tissue temperatures and heart and breathing rates were measured before, during, and after RF exposure. Pigs were placed into a 60‐cm diameter whole‐body resonator of a 3 T MRI system. Nineteen anesthetized pigs were divided into four RF exposure groups: sham (0 W/kg), low‐exposure (2.7 W/kg, mean exposure time 56 min), moderate‐exposure (4.8 W/kg, mean exposure time 31 min), and high‐exposure (4.4 W/kg, mean exposure time 61 min). One pig was exposed to a whole‐body specific absorption rate (wbSAR) of 11.4 W/kg (extreme‐exposure). Hotspot temperatures, measured by sensor 2, increased by mean 5.0 ± 0.9°C, min 3.9; max 6.3 (low), 7.0 ± 2.3°C, min 4.6; max 9.9 (moderate), and 9.2 ± 4.4°C, min 6.1, max 17.9 (high) compared with 0.3 ± 0.3°C in the sham‐exposure group (min 0.1, max 0.6). Four time‐temperature curves were identified: sinusoidal, parabolic, plateau, and linear. These curve shapes did not correlate with RF intensity, rectal temperature, breathing rate, or heart rate. In all pigs, rectal temperatures increased (2.1 ± 0.9°C) during and even after RF exposure, while hotspot temperatures decreased after exposure. When rectal temperature increased by 1°C, hotspot temperature increased up to 42.8°C within 37 min (low‐exposure) or up to 43.8°C within 24 min (high‐exposure). Global wbSAR did not correlate with maximum hotspot. Bioelectromagnetics. 2021;42:37–50. © 2020 The Authors. Bioelectromagnetics published by Wiley Periodicals LLC on behalf of Bioelectromagnetics Society
    Type of Medium: Online Resource
    ISSN: 0197-8462 , 1521-186X
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2021
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    SSG: 12
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  • 2
    Online Resource
    Online Resource
    American Association for the Advancement of Science (AAAS) ; 1988
    In:  Science Vol. 241, No. 4867 ( 1988-08-12), p. 832-835
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 241, No. 4867 ( 1988-08-12), p. 832-835
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
    RVK:
    RVK:
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 1988
    detail.hit.zdb_id: 128410-1
    detail.hit.zdb_id: 2066996-3
    detail.hit.zdb_id: 2060783-0
    SSG: 11
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  • 3
    In: Bioinformatics, Oxford University Press (OUP), Vol. 36, No. 21 ( 2021-01-29), p. 5255-5261
    Abstract: The development of deep, bidirectional transformers such as Bidirectional Encoder Representations from Transformers (BERT) led to an outperformance of several Natural Language Processing (NLP) benchmarks. Especially in radiology, large amounts of free-text data are generated in daily clinical workflow. These report texts could be of particular use for the generation of labels in machine learning, especially for image classification. However, as report texts are mostly unstructured, advanced NLP methods are needed to enable accurate text classification. While neural networks can be used for this purpose, they must first be trained on large amounts of manually labelled data to achieve good results. In contrast, BERT models can be pre-trained on unlabelled data and then only require fine tuning on a small amount of manually labelled data to achieve even better results. Results Using BERT to identify the most important findings in intensive care chest radiograph reports, we achieve areas under the receiver operation characteristics curve of 0.98 for congestion, 0.97 for effusion, 0.97 for consolidation and 0.99 for pneumothorax, surpassing the accuracy of previous approaches with comparatively little annotation effort. Our approach could therefore help to improve information extraction from free-text medical reports. Availability  and implementation We make the source code for fine-tuning the BERT-models freely available at https://github.com/fast-raidiology/bert-for-radiology. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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