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(Make every 2nd pixel in upper layer thinner)
(NFT and DL segmentation)
 
(5 dazwischenliegende Versionen desselben Benutzers werden nicht angezeigt)
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* NFTs for medical images (track ownership, track distribution)
* For deep learning segmentation use images reconstructed twice with different filters (edge preserving, smoothing) in training and application
* 4-eye mode for ML based diagnosis
* DLCT: Make every 2nd pixel in upper layer thinner
* DLCT: Make every 2nd pixel in upper layer thinner
* Use 5G in medical imaging
* Use 5G in medical imaging
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== Submitted ==
== Submitted ==
* Use technetium for measurement of iodine contrast in ventricle
* Cardiac CT: Frequency split
* HD: Dual-Layer detector with different pixel sizes in each layer
* HD: Dual-Layer detector with different pixel sizes in each layer
* Use survey scan for detection of patient parts outside of SFOV
* Use survey scan for detection of patient parts outside of SFOV

Aktuelle Version vom 22. Februar 2022, 09:06 Uhr

  • NFTs for medical images (track ownership, track distribution)
  • For deep learning segmentation use images reconstructed twice with different filters (edge preserving, smoothing) in training and application
  • 4-eye mode for ML based diagnosis
  • DLCT: Make every 2nd pixel in upper layer thinner
  • Use 5G in medical imaging
  • Kassenbon wird automatisch auf Handy übertragen
  • Tantalum-Filter on tube for switching contrast of tantalum contrast agent
  • Region-of-Interest MLIR
  • 2D stereo anti scatter grid (smaller lamellae for z-direction)
  • Stereo CT where detector is located asymmetrical along z
  • Twin-beam CT: Use x-deflection for different spectra at central detector rows
  • Use stereo for motion estimation
  • Stereo Tube at beginning/end of helix

Submitted

  • Use technetium for measurement of iodine contrast in ventricle
  • Cardiac CT: Frequency split
  • HD: Dual-Layer detector with different pixel sizes in each layer
  • Use survey scan for detection of patient parts outside of SFOV
  • Stereo-Tube with inplane shift of focal-spots
  • x-ray tube with oscillating anode speed
  • DART for EFOV
  • Stereo CT with central collimator for full-scan acquisitions (intersection point at rotation axis)
  • Use helical scout scan as start image for axial MLIR / detect motion (based on bone segmentation)
  • Different slice thicknesses in SFOV and EFOV
  • Use thresholded start image for MLIR