Dixon-vibe deep learning (divide) pseudo-CT synthesis for pelvis PET/MR attenuation correction Articles uri icon

authors

  • TORRADO-CARVAJAL, ANGEL
  • VERA-OLMOS, JAVIER
  • IZQUIERDO GARCÍA, DAVID
  • CATALANO, ONOFRIO A.
  • MORALES, MANUEL A.
  • MARGOLIN, JUSTIN
  • SORICELLI, ANDREA
  • SALVATORE, MARCO
  • MALPICA, NORBERTO
  • CATANA, CIPRIAN

publication date

  • March 2019

start page

  • 429

end page

  • 435

issue

  • 3

volume

  • 60

International Standard Serial Number (ISSN)

  • 0161-5505

Electronic International Standard Serial Number (EISSN)

  • 1535-5667

abstract

  • Whole-body attenuation correction (AC) is still challenging in combined PET/MR scanners. We describe Dixon-VIBE Deep Learning (DIVIDE), a deep-learning network that allows synthesizing pelvis pseudo-CT maps based only on the standard Dixon volumetric interpolated breath-hold examination (Dixon-VIBE) images currently acquired for AC in some commercial scanners. [...]

subjects

  • Biology and Biomedicine

keywords

  • pseudo-ct; image synthesis; pet/mr; attenuation correction; deep learning