ArticleProceedings of SPIE--the International Society for Optical Engineering2024
Motion-compensated 4DCT reconstruction from single-beat cardiac CT scans using convolutional networks.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- An end-to-end neural network for 4D cardiac CT reconstruction using single-beat scans.Physics in medicine and biology · 2025Article
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4 authors.
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Abstract
We proposed a deep learning-based method for single-heartbeat 4D cardiac CT reconstruction, where a single cardiac cycle was split into multiple phases for reconstruction. First, we pre-reconstruct each phase using the projection data from itself and the neighboring phases. The pre-reconstructions are fed into a supervised registration network to generate the deformation fields between different phases. The deformation fields are trained so that it can match the ground truth images from the corresponding phases. The deformation fields are then used in the FBP-and-wrap method for motion-compensated reconstruction, where a subsequent network is used to remove residual artifacts. The proposed method was validated with simulation data from 40 4D cardiac CT scans and demonstrated improved RMSE and SSIM and less blurring compared to FBP and PICCS.
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