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Physics in Medicine

ORAL · D08 · ID: 49961

Presentations

  • In Silico Prototyping for Intranasally Administered Agents for COVID-19 and Other Respiratory Pathogens

    ORAL

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    Publication: Lao, Y., Joseph-McCarthy, D., Chakravarty, A., Balivada, P. A., Ato, P., Ka, N. K., & Basu, S. (2020). Identifying the optimal parameters for sprayed and inhaled drug particulates for intranasal targeting of SARS-CoV-2 infection sites. arXiv preprint:2010.16325.

    Presenters

    • Zachary E Silfen

      Boston University

    Authors

    • Zachary E Silfen

      Boston University

    • Mohammad M.H. Akash

      South Dakota State University

    • Mark G Cherepashensky

      Boston University

    • Arijit Chakravarty

      Fractal Therapeutics, Cambridge, MA

    • Saikat Basu

      South Dakota State University

    • Diane Joseph-McCarthy

      Boston University

    View abstract →

  • Cost-Effective Depth-Encoding Methods for Time-of-Flight PET Scanners

    ORAL

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    Presenters

    • William J Matava

      UT Austin, University of Texas at Austin

    Authors

    • William J Matava

      UT Austin, University of Texas at Austin

    • Kyle T Klein

      The University of Texas at Austin, University of Texas at Austin

    • Firas Abouzahr

      University of Texas at Austin

    • Christopher Layden

      University of Texas at Austin

    • Akhil Sadam

      University of Texas at Austin

    • John Cesar

      University of Texas at Austin

    • Shawn Park

      University of Texas at Austin

    • Trang Do

      University of Texas at Austin

    • Victoria Koptelova

      University of Texas at Austin

    • Tri Truong

      University of Texas at Austin

    • Stefaan Tavernier

      Vrije Universiteit Brussel, PETsys Electronics

    • Marek Proga

      University of Texas at Austin

    • Karol Lang

      University of Texas at Austin

    View abstract →

  • An Inexpensive Polyvinyltoulene Barrel PET Scanner Design

    ORAL

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    Presenters

    • Akhil Sadam

      University of Texas at Austin

    Authors

    • Akhil Sadam

      University of Texas at Austin

    • Christopher Layden

      University of Texas at Austin

    • Kyle T Klein

      The University of Texas at Austin, University of Texas at Austin

    • William J Matava

      UT Austin, University of Texas at Austin

    • Karol Lang

      University of Texas at Austin

    View abstract →

  • DEEP LEARNING TECHNIQUES FOR KNEE MR IMAGES RECONSTRUCTION

    ORAL

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    Publication: Knoll, F., Zbontar, J., Sriram, A., Muckley, M. J., Bruno, M., Defazio, A., ... & Lui, Y. W. (2020). fastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning. Radiology: Artificial Intelligence, 2(1), e190007.<br>"Fastmri 2019 challenge leaderboard." https: //fastmri.org/leaderboards/challenge/2019/. Accessed: 2021-12-14.<br>Zbontar, J., Knoll, F., Sriram, A., Murrell, T., Huang, Z., Muckley, M. J., ... & Lui, Y. W. (2018). fastMRI: An open dataset and benchmarks for accelerated MRI. arXiv preprint arXiv:1811.08839.

    Presenters

    • María Margarita López-Titla

      Instituto Mexicano del Seguro Social

    Authors

    • María Margarita López-Titla

      Instituto Mexicano del Seguro Social

    • Héctor Gómez-Morales

      Georgia Institute of Technology

    • Kelvin Lin

      Georgia Institute of Technology

    • Sarmad Malik

      Georgia Institute of Technology

    • Zheng Cheng

      Georgia Institute of Technology

    View abstract →