Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 1 to 4 of 4 entries
Sorted by: Best Match Show Resources per page
Magnetic Resonance-Guided Drug Delivery.

Magnetic resonance imaging clinics of North America

Mikhail AS, Partanen A, Yarmolenko P, Venkatesan AM, Wood BJ.
PMID: 26499281
Magn Reson Imaging Clin N Am. 2015 Nov;23(4):643-55. doi: 10.1016/j.mric.2015.05.012. Epub 2015 Jul 09.

The use of clinical imaging modalities for the guidance of targeted drug delivery systems, known as image-guided drug delivery (IGDD), has emerged as a promising strategy for enhancing antitumor efficacy. MR imaging is particularly well suited for IGDD applications...

Cite
Mikhail AS, Partanen A, Yarmolenko P, et al. Magnetic Resonance-Guided Drug Delivery. Magn Reson Imaging Clin N Am. 2015;23(4):643-55doi: 10.1016/j.mric.2015.05.012.
Mikhail, A. S., Partanen, A., Yarmolenko, P., Venkatesan, A. M., & Wood, B. J. (2015). Magnetic Resonance-Guided Drug Delivery. Magnetic resonance imaging clinics of North America, 23(4), 643-55. https://doi.org/10.1016/j.mric.2015.05.012
Mikhail, Andrew S, et al. "Magnetic Resonance-Guided Drug Delivery." Magnetic resonance imaging clinics of North America vol. 23,4 (2015): 643-55. doi: https://doi.org/10.1016/j.mric.2015.05.012
Mikhail AS, Partanen A, Yarmolenko P, Venkatesan AM, Wood BJ. Magnetic Resonance-Guided Drug Delivery. Magn Reson Imaging Clin N Am. 2015 Nov;23(4):643-55. doi: 10.1016/j.mric.2015.05.012. Epub 2015 Jul 09. PMID: 26499281; PMCID: PMC5580964.
Copy Download .nbib Format: NLM
Federated Learning used for predicting outcomes in SARS-COV-2 patients.

Research square

Flores M, Dayan I, Roth H, Zhong A, Harouni A, Gentili A, Abidin A, Liu A, Costa A, Wood B, Tsai CS, Wang CH, Hsu CN, Lee CK, Ruan C, Xu D, Wu D, Huang E, Kitamura F, Lacey G, Corradi GCA, Shin HH, Obinata H, Ren H, Crane J, Tetreault J, Guan J, Garrett J, Park JG, Dreyer K, Juluru K, Kersten K, Rockenbach MABC, Linguraru M, Haider M, AbdelMaseeh M, Rieke N, Damasceno P, Silva PMCE, Wang P, Xu S, Kawano S, Sriswa S, Park SY, Grist T, Buch V, Jantarabenjakul W, Wang W, Tak WY, Li X, Lin X, Kwon F, Gilbert F, Kaggie J, Li Q, Quraini A, Feng A, Priest A, Turkbey B, Glicksberg B, Bizzo B, Kim BS, Tor-Diez C, Lee CC, Hsu CJ, Lin C, Lai CL, Hess C, Compas C, Bhatia D, Oermann E, Leibovitz E, Sasaki H, Mori H, Yang I, Sohn JH, Murthy KNK, Fu LC, de Mendonça MRF, Fralick M, Kang MK, Adil M, Gangai N, Vateekul P, Elnajjar P, Hickman S, Majumdar S, McLeod S, Reed S, Graf S, Harmon S, Kodama T, Puthanakit T, Mazzulli T, Lavor VL, Rakvongthai Y, Lee YR, Wen Y.
PMID: 33442676
Res Sq. 2021 Jan 08; doi: 10.21203/rs.3.rs-126892/v1.

'Federated Learning' (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a...

Cite
Flores M, Dayan I, Roth H, et al. Federated Learning used for predicting outcomes in SARS-COV-2 patients. Res Sq. 2021;doi: 10.21203/rs.3.rs-126892/v1.
Flores, M., Dayan, I., Roth, H., Zhong, A., Harouni, A., Gentili, A., Abidin, A., Liu, A., Costa, A., Wood, B., Tsai, C. S., Wang, C. H., Hsu, C. N., Lee, C. K., Ruan, C., Xu, D., Wu, D., Huang, E., Kitamura, F., Lacey, G., Corradi, G. C. A., Shin, H. H., Obinata, H., Ren, H., Crane, J., Tetreault, J., Guan, J., Garrett, J., Park, J. G., Dreyer, K., Juluru, K., Kersten, K., Rockenbach, M. A. B. C., Linguraru, M., Haider, M., AbdelMaseeh, M., Rieke, N., Damasceno, P., Silva, P. M. C. E., Wang, P., Xu, S., Kawano, S., Sriswa, S., Park, S. Y., Grist, T., Buch, V., Jantarabenjakul, W., Wang, W., Tak, W. Y., Li, X., Lin, X., Kwon, F., Gilbert, F., Kaggie, J., Li, Q., Quraini, A., Feng, A., Priest, A., Turkbey, B., Glicksberg, B., Bizzo, B., Kim, B. S., Tor-Diez, C., Lee, C. C., Hsu, C. J., Lin, C., Lai, C. L., Hess, C., Compas, C., Bhatia, D., Oermann, E., Leibovitz, E., Sasaki, H., Mori, H., Yang, I., Sohn, J. H., Murthy, K. N. K., Fu, L. C., de Mendonça, M. R. F., Fralick, M., Kang, M. K., Adil, M., Gangai, N., Vateekul, P., Elnajjar, P., Hickman, S., Majumdar, S., McLeod, S., Reed, S., Graf, S., Harmon, S., Kodama, T., Puthanakit, T., Mazzulli, T., Lavor, V. L., Rakvongthai, Y., Lee, Y. R., & Wen, Y. (2021). Federated Learning used for predicting outcomes in SARS-COV-2 patients. Research square, . https://doi.org/10.21203/rs.3.rs-126892/v1
Flores, Mona, et al. "Federated Learning used for predicting outcomes in SARS-COV-2 patients." Research square vol. (2021). doi: https://doi.org/10.21203/rs.3.rs-126892/v1
Flores M, Dayan I, Roth H, Zhong A, Harouni A, Gentili A, Abidin A, Liu A, Costa A, Wood B, Tsai CS, Wang CH, Hsu CN, Lee CK, Ruan C, Xu D, Wu D, Huang E, Kitamura F, Lacey G, Corradi GCA, Shin HH, Obinata H, Ren H, Crane J, Tetreault J, Guan J, Garrett J, Park JG, Dreyer K, Juluru K, Kersten K, Rockenbach MABC, Linguraru M, Haider M, AbdelMaseeh M, Rieke N, Damasceno P, Silva PMCE, Wang P, Xu S, Kawano S, Sriswa S, Park SY, Grist T, Buch V, Jantarabenjakul W, Wang W, Tak WY, Li X, Lin X, Kwon F, Gilbert F, Kaggie J, Li Q, Quraini A, Feng A, Priest A, Turkbey B, Glicksberg B, Bizzo B, Kim BS, Tor-Diez C, Lee CC, Hsu CJ, Lin C, Lai CL, Hess C, Compas C, Bhatia D, Oermann E, Leibovitz E, Sasaki H, Mori H, Yang I, Sohn JH, Murthy KNK, Fu LC, de Mendonça MRF, Fralick M, Kang MK, Adil M, Gangai N, Vateekul P, Elnajjar P, Hickman S, Majumdar S, McLeod S, Reed S, Graf S, Harmon S, Kodama T, Puthanakit T, Mazzulli T, Lavor VL, Rakvongthai Y, Lee YR, Wen Y. Federated Learning used for predicting outcomes in SARS-COV-2 patients. Res Sq. 2021 Jan 08; doi: 10.21203/rs.3.rs-126892/v1. PMID: 33442676; PMCID: PMC7805458.
Copy Download .nbib Format: NLM
Feasibility and Acute Safety Study of Radiofrequency Energy Delivery to the Vena Caval Wall via an IVC Filter in Swine.

Journal of engineering and science in medical diagnostics and therapy

Seifabadi R, Pritchard WF, Leonard S, Bakhutashvili I, Woods DL, Esparza-Trujillo JA, Karanian JW, Wood BJ.
PMID: 33134858
J Eng Sci Med Diagn Ther. 2019 Aug;2(3). doi: 10.1115/1.4043901. Epub 2019 Jun 12.

Retrievable inferior vena cava (IVC) filters are self-expanding metallic devices implanted in the IVC to prevent migration of thrombi from the deep veins of the legs and pelvis to the lungs. The risk of complications from the filters increases...

Cite
Seifabadi R, Pritchard WF, Leonard S, et al. Feasibility and Acute Safety Study of Radiofrequency Energy Delivery to the Vena Caval Wall via an IVC Filter in Swine. J Eng Sci Med Diagn Ther. 2019;2(3)doi: 10.1115/1.4043901.
Seifabadi, R., Pritchard, W. F., Leonard, S., Bakhutashvili, I., Woods, D. L., Esparza-Trujillo, J. A., Karanian, J. W., & Wood, B. J. (2019). Feasibility and Acute Safety Study of Radiofrequency Energy Delivery to the Vena Caval Wall via an IVC Filter in Swine. Journal of engineering and science in medical diagnostics and therapy, 2(3), . https://doi.org/10.1115/1.4043901
Seifabadi, Reza, et al. "Feasibility and Acute Safety Study of Radiofrequency Energy Delivery to the Vena Caval Wall via an IVC Filter in Swine." Journal of engineering and science in medical diagnostics and therapy vol. 2,3 (2019). doi: https://doi.org/10.1115/1.4043901
Seifabadi R, Pritchard WF, Leonard S, Bakhutashvili I, Woods DL, Esparza-Trujillo JA, Karanian JW, Wood BJ. Feasibility and Acute Safety Study of Radiofrequency Energy Delivery to the Vena Caval Wall via an IVC Filter in Swine. J Eng Sci Med Diagn Ther. 2019 Aug;2(3). doi: 10.1115/1.4043901. Epub 2019 Jun 12. PMID: 33134858; PMCID: PMC7595439.
Copy Download .nbib Format: NLM
A Clinically Driven Task-Based Comparison of Photon Counting and Conventional Energy Integrating CT for Soft Tissue, Vascular, and High-Resolution Tasks.

IEEE transactions on radiation and plasma medical sciences

Rajagopal JR, Sahbaee P, Farhadi F, Solomon JB, Ramirez-Giraldo JC, Pritchard WF, Wood BJ, Jones EC, Samei E.
PMID: 34250326
IEEE Trans Radiat Plasma Med Sci. 2021 Jul;5(4):588-595. doi: 10.1109/trpms.2020.3019954. Epub 2020 Aug 27.

Photon-counting CT detectors are the next step in advancing CT system development and will replace the current energy integrating detectors (EID) in CT systems in the near future. In this context, the performance of PCCT was compared to EID...

Cite
Rajagopal JR, Sahbaee P, Farhadi F, et al. A Clinically Driven Task-Based Comparison of Photon Counting and Conventional Energy Integrating CT for Soft Tissue, Vascular, and High-Resolution Tasks. IEEE Trans Radiat Plasma Med Sci. 2020;5(4):588-595doi: 10.1109/trpms.2020.3019954.
Rajagopal, J. R., Sahbaee, P., Farhadi, F., Solomon, J. B., Ramirez-Giraldo, J. C., Pritchard, W. F., Wood, B. J., Jones, E. C., & Samei, E. (2021). A Clinically Driven Task-Based Comparison of Photon Counting and Conventional Energy Integrating CT for Soft Tissue, Vascular, and High-Resolution Tasks. IEEE transactions on radiation and plasma medical sciences, 5(4), 588-595. https://doi.org/10.1109/trpms.2020.3019954
Rajagopal, Jayasai R, et al. "A Clinically Driven Task-Based Comparison of Photon Counting and Conventional Energy Integrating CT for Soft Tissue, Vascular, and High-Resolution Tasks." IEEE transactions on radiation and plasma medical sciences vol. 5,4 (2021): 588-595. doi: https://doi.org/10.1109/trpms.2020.3019954
Rajagopal JR, Sahbaee P, Farhadi F, Solomon JB, Ramirez-Giraldo JC, Pritchard WF, Wood BJ, Jones EC, Samei E. A Clinically Driven Task-Based Comparison of Photon Counting and Conventional Energy Integrating CT for Soft Tissue, Vascular, and High-Resolution Tasks. IEEE Trans Radiat Plasma Med Sci. 2021 Jul;5(4):588-595. doi: 10.1109/trpms.2020.3019954. Epub 2020 Aug 27. PMID: 34250326; PMCID: PMC8269971.
Copy Download .nbib Format: NLM
Showing 1 to 4 of 4 entries