Campuses:

tomography

Wednesday, October 16, 2019 - 3:00pm - 3:45pm
Ge Wang (Rensselaer Polytechnic Institute)
Computer vision and image analysis are major application examples of deep learning. While computer vision and image analysis deal with existing images and produce features of these images (images to features), tomographic imaging produces images of multi-dimensional structures from experimentally measured “encoded” data as various tomographic features (integrals, harmonics, and so on, of underlying images) (features to images). Recently, deep learning is being actively developed worldwide for tomographic imaging, forming a new area of imaging research.
Thursday, September 7, 2017 - 2:55pm - 3:30pm
Jennifer Mueller (Colorado State University)
In Ultrasound Tomography (UST) transducers around the boundary of a medium measure the scattered acoustic waves arising from transmitted pulses emitted from the transducers. In medical applications, the possibilities for transducer placement are constrained by the geometry of the human body. In this talk, optimal transducer placement and excitation patterns will be discussed for several geometries, and their effects on image quality and computation time will be compared for simulated medical imaging data.
Friday, November 18, 2011 - 8:30am - 9:30am
Peter Doerschuk (Cornell University)
Single-particle cryo electron microscopy provides
images of biological macromolecular complexes with
spatial sampling on the order of 1-2 Angstrom.
Combining on the order of 100,000 such images can result
in 3-D reconstructions of the electron scattering
intensity of the complex with a spatial resolution as
fine as 4-5 Angstrom. Due to damage in the imaging
process, each complex is imaged only once and therefore
having a homogeneous ensemble of complexes is
important. Algorithms and results will be presented
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