Tim Szczykutowicz, PhD has been granted a patent for his project, X-ray Imaging Device Providing Enhanced Spatial Resolution by Energy Encoding.
“In a nutshell, the idea is to divide each detector into sub-regions via energy weighting of the spectra on a length scale smaller than the detector element. This way, if you acquire multiple energy resolved measurements, you can decouple the detector signal at the length scale of the energy encoding, which is smaller than the detector size, allowing you to get resolution smaller than your detector! So, the resolution is determined by the energy encoding (i.e., a grid of filters placed over the detector) instead of the detector spacing. It would allow for cheap retrofitting of a detector, enabling higher spatial resolution,” explains Dr. Szczykutowicz.
In addition to his patent, Dr. Szczykutowicz had his research published in PLOS ONE.
Paul Laeseke, MD, PhD has been granted a patent for his project, System and method for motion-adjusted device guidance using vascular roadmaps.
The abstract of his patent reads, “A system and method is provided for creating motion-adjusted or motion-compensated images of a patient to guide an interventional medical procedure. The method includes displaying a static roadmap and a plurality of dynamic images to show the interventional medical device aligned on the static roadmap using a motion transformation. Alignment of the interventional medical device on the static roadmap is based on a user selection of one of motion compensation of the interventional medical device relative to the static roadmap to produce a plurality of images that do not show patient motion or motion adjustment of the static roadmap relative to the interventional medical device to produce a plurality of images that show patient motion.”
Guang-Hong Chen, PhD has been granted a patent for his project, System and method for multi-architecture computed tomography pipeline.
The abstract of his patent reads, “A system and method for reconstructing an image of a subject acquired using a tomographic imaging system includes at least one computer processor configured to form an image reconstruction pipeline. The reconstruction pipeline at least includes an automated correction module configured to receive imaging data acquired from a subject using ionizing radiation generated by the tomographic imaging system and generate corrected data using a first learning network. The reconstruction pipeline also includes an intelligent reconstruction module configured to receive at least one of the imaging data and the corrected data and reconstruct an image of the subject using a second learning network.”
Congratulations Drs. Szczykutowicz, Laeseke, and Chen!