Poster + Presentation + Paper
15 February 2021 GAN-based sinogram completion for slow triple kVp switching CT
Author Affiliations +
Conference Poster
Abstract
The feasibility of acquiring multi-energy CT data through slow modulation of the kVp as an alternative to photon-counting detectors (PCDs) is currently under exploration. A low kVp-switching rate can be enabled with a conventional CT system but raises challenges due to missing sinogram views. Our previous work used a CNN-based method for sinogram completion by generating full-sampled images from undersampled sinograms, providing an acceptable image quality at a 22°/kVp switching rate. The purpose of this study was to investigate a GAN-based spectral sinogram completion method for enabling a lower kVp switching rate. A Pix2Pix GAN model with paired undersampled sinogram of 45° or 120° projections/kVp and its corresponding full-sampled sinogram was implemented and trained. The completed data was subsequently used to perform sinogram domain material decomposition. Our results on a simulated FORBILD abdomen phantom dataset showed that the GAN-based method can further lower the kVp switching rate to 45° projections/kVp. The proposed GAN-based sinogram completion method facilitates slow-kVp switching acquisitions and thus further relaxes hardware requirements.
Conference Presentation
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Wenchao Cao, Nadav Shapira, and Peter B. Noël "GAN-based sinogram completion for slow triple kVp switching CT", Proc. SPIE 11595, Medical Imaging 2021: Physics of Medical Imaging, 115952O (15 February 2021); https://doi.org/10.1117/12.2580735
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Cited by 1 patent.
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KEYWORDS
Switching

Computer simulations

Gallium nitride

Image quality

Modulation

Photodetectors

Sensors

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