A model based on Wasserstein generative adversarial networks for 2D CT slice image reconstruction from a small number of prediction images. Expert radiologists must confirm the model’s accuracy.
A U-net-based image reconstruction framework. It is superior to noise and angle artefacts in terms of visual structure preservation, but it is computationally costly and requires large training datasets.
A more relaxed variant of projected gradient descent (PGD) is used in this model. The results demonstrate that the new technique outperforms the previous one.
An approach for CT image reconstruction based on deep learning. When compared to other state-of-the-art approaches, the results show enhanced image quality with less image noise.
A lightweight framework for a few-view CT reconstruction approach. It learns an end-to-end mapping between a few-view picture optimization and a full-view image optimization.
For high-quality CT reconstructions, a deep learning architecture was developed. The framework is capable of distinguishing and removing noise from the input signal.
Iterative reconstructions of data from genuine CT systems using a TensorFlow framework. The drawback is that it necessitates the use of graphics processing units (GPUs).
During reconstruction, a CNN framework is used to remove streaks from CT images. To discriminate between objects and characteristics, the framework requires further training.
A deep learning model for reconstructing high-quality images from sinogram data. It reduces noise, improves spatial resolution, and is quick without sacrificing quality.
For CT reconstruction, there is a framework called LEARN. It boosts image quality as well as computational efficiency. The framework still has to be optimized for clinical applications.
