On the simulation of ultra-sparse-view and ultra-low-dose computed tomography with maximum a posteriori reconstruction using a progressive flow-based deep generative model

HIGHLIGHTS

• who: Hisaichi Shibata and colleagues from the The Department of Radiology, The University of Tokyo Hospital, Hongo, Bunkyo-ku, Tokyo, Japan have published the research work: On the Simulation of Ultra-Sparse-View and Ultra-Low-Dose Computed Tomography with Maximum a Posteriori Reconstruction Using a Progressive Flow-Based Deep Generative Model, in the Journal: Tomography 2022, 8, 2129-2152. of /2022/
• what: The authors propose for the maximum a posteriori (MAP) reconstruction of a three-dimensional (3D) chest CT image from a single or a few two-dimensional (2D) projection images using a generative . . .
  

   

  

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