@inproceedings{8fcd64d27bb74115baff63d5e4bda848,
title = "Motion-compensated 4DCT reconstruction from single-beat cardiac CT scans using convolutional networks",
abstract = "We proposed a deep lea rning-ba sed method for single-hea rtbea t 4D ca rdia c CT reconstruction, where a single ca rdiac cycle wa s split into multiple pha ses for reconstruction. First, we pre-reconstruct ea ch pha se using the projection da ta from itself a nd the neighboring pha ses. The pre-reconstructions a re fed into a supervised registra tion network to genera te the deforma tion fields between different pha ses. The deformation fields a re tra ined so tha t it ca n match the ground truth images from the corresponding pha ses. The deforma tion fields a re then used in the FBP-a nd-wra p method for motion-compensated reconstruction, where a subsequent network is used to remove residua l a rtifa cts. The proposed method wa s va lida ted with simula tion da ta from 40 4D ca rdia c CT sca ns a nd demonstra ted improved RMSE a nd SSIM a nd less blurring compared to FBP a nd PICCS.",
keywords = "4DCT, cardiac, CNN, deformable image registration, motion-compensated reconstruction",
author = "Zhenyao Yan and Zhennong Chen and Li Zhang and Quanzheng Li and Dufan Wu",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; Medical Imaging 2024: Physics of Medical Imaging ; Conference date: 19-02-2024 Through 22-02-2024",
year = "2024",
doi = "10.1117/12.3005368",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Rebecca Fahrig and Sabol, \{John M.\} and Ke Li",
booktitle = "Medical Imaging 2024",
}