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Showing 133 to 141 of 141 entries
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Free-breathing radial 3D fat-suppressed T1-weighted gradient echo (r-VIBE) sequence for assessment of pulmonary lesions: a prospective comparison of CT and MRI.

Cancer imaging : the official publication of the International Cancer Imaging Society

Yu N, Duan H, Yang C, Yu Y, Dang S.
PMID: 34930463
Cancer Imaging. 2021 Dec 20;21(1):68. doi: 10.1186/s40644-021-00441-3.

PURPOSE: To determine whether the pulmonary MR imaging with free-breathing radial 3D fat-suppressed T1-weighted gradient echo (r-VIBE) sequence can detect lung lesions and display lesion profiles with an accuracy comparable to that of computed tomography (CT), which is the...

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Yu N, Duan H, Yang C, et al. Free-breathing radial 3D fat-suppressed T1-weighted gradient echo (r-VIBE) sequence for assessment of pulmonary lesions: a prospective comparison of CT and MRI. Cancer Imaging. 2021;21(1):68doi: 10.1186/s40644-021-00441-3.
Yu, N., Duan, H., Yang, C., Yu, Y., & Dang, S. (2021). Free-breathing radial 3D fat-suppressed T1-weighted gradient echo (r-VIBE) sequence for assessment of pulmonary lesions: a prospective comparison of CT and MRI. Cancer imaging : the official publication of the International Cancer Imaging Society, 21(1), 68. https://doi.org/10.1186/s40644-021-00441-3
Yu, Nan, et al. "Free-breathing radial 3D fat-suppressed T1-weighted gradient echo (r-VIBE) sequence for assessment of pulmonary lesions: a prospective comparison of CT and MRI." Cancer imaging : the official publication of the International Cancer Imaging Society vol. 21,1 (2021): 68. doi: https://doi.org/10.1186/s40644-021-00441-3
Yu N, Duan H, Yang C, Yu Y, Dang S. Free-breathing radial 3D fat-suppressed T1-weighted gradient echo (r-VIBE) sequence for assessment of pulmonary lesions: a prospective comparison of CT and MRI. Cancer Imaging. 2021 Dec 20;21(1):68. doi: 10.1186/s40644-021-00441-3. PMID: 34930463.
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Visual lung damage CT score at hospital admission of COVID-19 patients and 30-day mortality.

European radiology

Charpentier E, Soulat G, Fayol A, Hernigou A, Livrozet M, Grand T, Reverdito G, Al Haddad J, Dang Tran KD, Charpentier A, Clement O, Hulot JS, Mousseaux E.
PMID: 33914118
Eur Radiol. 2021 Nov;31(11):8354-8363. doi: 10.1007/s00330-021-07938-2. Epub 2021 Apr 29.

OBJECTIVES: Chest CT has been widely used to screen and to evaluate the severity of COVID-19 disease in the early stages of infection without severe acute respiratory syndrome, but no prospective data are available to study the relationship between...

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Charpentier E, Soulat G, Fayol A, et al. Visual lung damage CT score at hospital admission of COVID-19 patients and 30-day mortality. Eur Radiol. 2021;31(11):8354-8363doi: 10.1007/s00330-021-07938-2.
Charpentier, E., Soulat, G., Fayol, A., Hernigou, A., Livrozet, M., Grand, T., Reverdito, G., Al Haddad, J., Dang Tran, K. D., Charpentier, A., Clement, O., Hulot, J. S., & Mousseaux, E. (2021). Visual lung damage CT score at hospital admission of COVID-19 patients and 30-day mortality. European radiology, 31(11), 8354-8363. https://doi.org/10.1007/s00330-021-07938-2
Charpentier, Etienne, et al. "Visual lung damage CT score at hospital admission of COVID-19 patients and 30-day mortality." European radiology vol. 31,11 (2021): 8354-8363. doi: https://doi.org/10.1007/s00330-021-07938-2
Charpentier E, Soulat G, Fayol A, Hernigou A, Livrozet M, Grand T, Reverdito G, Al Haddad J, Dang Tran KD, Charpentier A, Clement O, Hulot JS, Mousseaux E. Visual lung damage CT score at hospital admission of COVID-19 patients and 30-day mortality. Eur Radiol. 2021 Nov;31(11):8354-8363. doi: 10.1007/s00330-021-07938-2. Epub 2021 Apr 29. PMID: 33914118; PMCID: PMC8083100.
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Early Warning Information for Severe and Critical Patients With COVID-19 Based on Quantitative CT Analysis of Lung Segments.

Frontiers in public health

Yuyun X, Lexi Y, Haochu W, Zhenyu S, Xiangyang G.
PMID: 34055706
Front Public Health. 2021 May 13;9:596938. doi: 10.3389/fpubh.2021.596938. eCollection 2021.

No abstract available.

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Yuyun X, Lexi Y, Haochu W, et al. Early Warning Information for Severe and Critical Patients With COVID-19 Based on Quantitative CT Analysis of Lung Segments. Front Public Health. 2021;9:596938doi: 10.3389/fpubh.2021.596938.
Yuyun, X., Lexi, Y., Haochu, W., Zhenyu, S., & Xiangyang, G. (2021). Early Warning Information for Severe and Critical Patients With COVID-19 Based on Quantitative CT Analysis of Lung Segments. Frontiers in public health, 9596938. https://doi.org/10.3389/fpubh.2021.596938
Yuyun, Xu, et al. "Early Warning Information for Severe and Critical Patients With COVID-19 Based on Quantitative CT Analysis of Lung Segments." Frontiers in public health vol. 9 (2021): 596938. doi: https://doi.org/10.3389/fpubh.2021.596938
Yuyun X, Lexi Y, Haochu W, Zhenyu S, Xiangyang G. Early Warning Information for Severe and Critical Patients With COVID-19 Based on Quantitative CT Analysis of Lung Segments. Front Public Health. 2021 May 13;9:596938. doi: 10.3389/fpubh.2021.596938. eCollection 2021. PMID: 34055706; PMCID: PMC8155286.
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Lung Fissural Integrity: It's Written in the Genes.

American journal of respiratory and critical care medicine

Garner JL, Desai SR.
PMID: 34352193
Am J Respir Crit Care Med. 2021 Oct 01;204(7):750-752. doi: 10.1164/rccm.202106-1526ED.

No abstract available.

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Garner JL, Desai SR. Lung Fissural Integrity: It's Written in the Genes. Am J Respir Crit Care Med. 2021;204(7):750-752doi: 10.1164/rccm.202106-1526ED.
Garner, J. L., & Desai, S. R. (2021). Lung Fissural Integrity: It's Written in the Genes. American journal of respiratory and critical care medicine, 204(7), 750-752. https://doi.org/10.1164/rccm.202106-1526ED
Garner, Justin L, and Desai, Sujal R. "Lung Fissural Integrity: It's Written in the Genes." American journal of respiratory and critical care medicine vol. 204,7 (2021): 750-752. doi: https://doi.org/10.1164/rccm.202106-1526ED
Garner JL, Desai SR. Lung Fissural Integrity: It's Written in the Genes. Am J Respir Crit Care Med. 2021 Oct 01;204(7):750-752. doi: 10.1164/rccm.202106-1526ED. PMID: 34352193; PMCID: PMC8528520.
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Coronavirus Disease 2019: Hysteresis Effect of Chest CT and the Correlation with its Severity.

Current medical imaging

Li K, Wang W, Wu L, Zhao C, Jiang Z, Liu S, Li T.
PMID: 34931984
Curr Med Imaging. 2021;17(12):1487-1495. doi: 10.2174/1573405617666210712162247.

PURPOSE: The purpose of this study was to investigate the influencing factors for chest CT hysteresis and severity of coronavirus disease 2019 (COVID-19).METHODS: The chest CT data of patients with confirmed COVID-19 in 4 hospitals were retrospectively analyzed. An...

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Li K, Wang W, Wu L, et al. Coronavirus Disease 2019: Hysteresis Effect of Chest CT and the Correlation with its Severity. Curr Med Imaging. 2021;17(12):1487-1495doi: 10.2174/1573405617666210712162247.
Li, K., Wang, W., Wu, L., Zhao, C., Jiang, Z., Liu, S., & Li, T. (2021). Coronavirus Disease 2019: Hysteresis Effect of Chest CT and the Correlation with its Severity. Current medical imaging, 17(12), 1487-1495. https://doi.org/10.2174/1573405617666210712162247
Li, Kui, et al. "Coronavirus Disease 2019: Hysteresis Effect of Chest CT and the Correlation with its Severity." Current medical imaging vol. 17,12 (2021): 1487-1495. doi: https://doi.org/10.2174/1573405617666210712162247
Li K, Wang W, Wu L, Zhao C, Jiang Z, Liu S, Li T. Coronavirus Disease 2019: Hysteresis Effect of Chest CT and the Correlation with its Severity. Curr Med Imaging. 2021;17(12):1487-1495. doi: 10.2174/1573405617666210712162247. PMID: 34931984.
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LGAN: Lung segmentation in CT scans using generative adversarial network.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

Tan J, Jing L, Huo Y, Li L, Akin O, Tian Y.
PMID: 33278767
Comput Med Imaging Graph. 2021 Jan;87:101817. doi: 10.1016/j.compmedimag.2020.101817. Epub 2020 Nov 16.

Lung segmentation in Computerized Tomography (CT) images plays an important role in various lung disease diagnosis. Most of the current lung segmentation approaches are performed through a series of procedures with manually empirical parameter adjustments in each step. Pursuing...

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Tan J, Jing L, Huo Y, et al. LGAN: Lung segmentation in CT scans using generative adversarial network. Comput Med Imaging Graph. 2020;87:101817doi: 10.1016/j.compmedimag.2020.101817.
Tan, J., Jing, L., Huo, Y., Li, L., Akin, O., & Tian, Y. (2021). LGAN: Lung segmentation in CT scans using generative adversarial network. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 87101817. https://doi.org/10.1016/j.compmedimag.2020.101817
Tan, Jiaxing, et al. "LGAN: Lung segmentation in CT scans using generative adversarial network." Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society vol. 87 (2021): 101817. doi: https://doi.org/10.1016/j.compmedimag.2020.101817
Tan J, Jing L, Huo Y, Li L, Akin O, Tian Y. LGAN: Lung segmentation in CT scans using generative adversarial network. Comput Med Imaging Graph. 2021 Jan;87:101817. doi: 10.1016/j.compmedimag.2020.101817. Epub 2020 Nov 16. PMID: 33278767; PMCID: PMC8477299.
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Ground-glass opacity (GGO): a review of the differential diagnosis in the era of COVID-19.

Japanese journal of radiology

Cozzi D, Cavigli E, Moroni C, Smorchkova O, Zantonelli G, Pradella S, Miele V.
PMID: 33900542
Jpn J Radiol. 2021 Aug;39(8):721-732. doi: 10.1007/s11604-021-01120-w. Epub 2021 Apr 26.

Thoracic imaging is fundamental in the diagnostic route of Coronavirus disease 2019 (COVID-19) especially in patients admitted to hospitals. In particular, chest computed tomography (CT) has a key role in identifying the typical features of the infection. Ground-glass opacities...

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Cozzi D, Cavigli E, Moroni C, et al. Ground-glass opacity (GGO): a review of the differential diagnosis in the era of COVID-19. Jpn J Radiol. 2021;39(8):721-732doi: 10.1007/s11604-021-01120-w.
Cozzi, D., Cavigli, E., Moroni, C., Smorchkova, O., Zantonelli, G., Pradella, S., & Miele, V. (2021). Ground-glass opacity (GGO): a review of the differential diagnosis in the era of COVID-19. Japanese journal of radiology, 39(8), 721-732. https://doi.org/10.1007/s11604-021-01120-w
Cozzi, Diletta, et al. "Ground-glass opacity (GGO): a review of the differential diagnosis in the era of COVID-19." Japanese journal of radiology vol. 39,8 (2021): 721-732. doi: https://doi.org/10.1007/s11604-021-01120-w
Cozzi D, Cavigli E, Moroni C, Smorchkova O, Zantonelli G, Pradella S, Miele V. Ground-glass opacity (GGO): a review of the differential diagnosis in the era of COVID-19. Jpn J Radiol. 2021 Aug;39(8):721-732. doi: 10.1007/s11604-021-01120-w. Epub 2021 Apr 26. PMID: 33900542; PMCID: PMC8071755.
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Alleviating Class-Wise Gradient Imbalance for Pulmonary Airway Segmentation.

IEEE transactions on medical imaging

Zheng H, Qin Y, Gu Y, Xie F, Yang J, Sun J, Yang GZ.
PMID: 33970858
IEEE Trans Med Imaging. 2021 Sep;40(9):2452-2462. doi: 10.1109/TMI.2021.3078828. Epub 2021 Aug 31.

Automated airway segmentation is a prerequisite for pre-operative diagnosis and intra-operative navigation for pulmonary intervention. Due to the small size and scattered spatial distribution of peripheral bronchi, this is hampered by a severe class imbalance between foreground and background...

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Zheng H, Qin Y, Gu Y, et al. Alleviating Class-Wise Gradient Imbalance for Pulmonary Airway Segmentation. IEEE Trans Med Imaging. 2021;40(9):2452-2462doi: 10.1109/TMI.2021.3078828.
Zheng, H., Qin, Y., Gu, Y., Xie, F., Yang, J., Sun, J., & Yang, G. Z. (2021). Alleviating Class-Wise Gradient Imbalance for Pulmonary Airway Segmentation. IEEE transactions on medical imaging, 40(9), 2452-2462. https://doi.org/10.1109/TMI.2021.3078828
Zheng, Hao, et al. "Alleviating Class-Wise Gradient Imbalance for Pulmonary Airway Segmentation." IEEE transactions on medical imaging vol. 40,9 (2021): 2452-2462. doi: https://doi.org/10.1109/TMI.2021.3078828
Zheng H, Qin Y, Gu Y, Xie F, Yang J, Sun J, Yang GZ. Alleviating Class-Wise Gradient Imbalance for Pulmonary Airway Segmentation. IEEE Trans Med Imaging. 2021 Sep;40(9):2452-2462. doi: 10.1109/TMI.2021.3078828. Epub 2021 Aug 31. PMID: 33970858.
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Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation.

Sensors (Basel, Switzerland)

Souza LFF, Silva ICL, Marques AG, Silva FHDS, Nunes VX, Hassan MM, Albuquerque VHC, Filho PPR.
PMID: 33255308
Sensors (Basel). 2020 Nov 24;20(23). doi: 10.3390/s20236711.

Several pathologies have a direct impact on society, causing public health problems. Pulmonary diseases such as Chronic obstructive pulmonary disease (COPD) are already the third leading cause of death in the world, leaving tuberculosis at ninth with 1.7 million...

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Souza LFF, Silva ICL, Marques AG, et al. Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation. Sensors (Basel). 2020;20(23)doi: 10.3390/s20236711.
Souza, L. F. F., Silva, I. C. L., Marques, A. G., Silva, F. H. D. S., Nunes, V. X., Hassan, M. M., Albuquerque, V. H. C., & Filho, P. P. R. (2020). Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation. Sensors (Basel, Switzerland), 20(23), . https://doi.org/10.3390/s20236711
Souza, Luís Fabrício de Freitas, et al. "Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation." Sensors (Basel, Switzerland) vol. 20,23 (2020). doi: https://doi.org/10.3390/s20236711
Souza LFF, Silva ICL, Marques AG, Silva FHDS, Nunes VX, Hassan MM, Albuquerque VHC, Filho PPR. Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation. Sensors (Basel). 2020 Nov 24;20(23). doi: 10.3390/s20236711. PMID: 33255308; PMCID: PMC7727680.
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Showing 133 to 141 of 141 entries