Bridging the translational gap: Implementation of multimodal small animal imaging strategies for tumor burden assessment in a co-clinical trial

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Published Version (Please cite this version)

10.1371/journal.pone.0207555

Publication Info

Blocker, SJ, YM Mowery, MD Holbrook, Y Qi, DG Kirsch, GA Johnson and CT Badea (n.d.). Bridging the translational gap: Implementation of multimodal small animal imaging strategies for tumor burden assessment in a co-clinical trial. PLOS ONE, 14(4). pp. e0207555–e0207555. 10.1371/journal.pone.0207555 Retrieved from https://hdl.handle.net/10161/18307.

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Scholars@Duke

Blocker

Stephanie Blocker

Assistant Professor in Radiology
Mowery

Yvonne Marie Mowery

Adjunct Assistant Professor in the Department of Radiation Oncology
Johnson

G. Allan Johnson

Charles E. Putman University Distinguished Professor of Radiology

Dr. Johnson is the Charles E. Putman University Professor of Radiology, Professor of Physics, and Biomedical Engineering, and Director of the Duke Center for In Vivo Microscopy (CIVM). The CIVM is an NIH/NIBIB national Biomedical Technology Resource Center with a mission to develop novel technologies for preclinical imaging (basic sciences) and apply the technologies to critical biomedical questions. Dr. Johnson was one of the first researchers to bring Paul Lauterbur's vision of magnetic resonance (MR) microscopy to practice as described in his paper, "Nuclear magnetic resonance imaging at microscopic resolution" (J Magn Reson 68:129-137, 1986). Dr. Johnson is involved in both the engineering physics required to extend the resolution of MR imaging and in a broad range of applications in the basic sciences.

Badea

Cristian Tudorel Badea

Professor in Radiology

  • Our lab's research focus lies primarily in developing novel quantitative imaging systems, reconstruction algorithms and analysis methods.  My major expertise is in preclinical CT.
  • Currently, we are particularly interested in developing novel strategies for spectral CT imaging using nanoparticle-based contrast agents for theranostics (i.e. therapy and diagnostics).
  • We are also engaged in developing new approaches for multidimensional CT image reconstruction suitable to address difficult undersampling cases in cardiac and spectral CT (dual energy and photon counting) using compressed sensing and/or deep learning.



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