In computed tomographic (CT) image reconstruction, image prior design and parameter
tuning are important to improving the image reconstruction quality from noisy or …

Purpose Deep neural network‐based image reconstruction has demonstrated promising
performance in medical imaging for undersampled and low‐dose scenarios. However, it …

Filtered back projection (FBP) reconstruction is simple and computationally efficient and is
used in many commercial CT (tomography) imaging products. However, higher Poisson …

High radiation dose in CT imaging is a major concern, which could result in increased
lifetime risk of cancers. Therefore, to reduce the radiation dose at the same time maintaining …

Background With the rapid development of deep learning technology, deep neural networks
can effectively enhance the performance of computed tomography (CT) reconstructions. One …

Neural network related methods, due to their unprecedented success in image processing,
have emerged as a new set of tools in CT reconstruction with the potential to change the …

Innovations in CT have been impressive among imaging and medical technologies in both
the hardware and software domain. The range and speed of CT scanning improved from the …

Deep learning (DL)-based methods show great potential in computed tomography (CT)
imaging field. The DL-based reconstruction methods are usually evaluated on the training …

An increasing use of computed tomography (CT) in modern medicine has raised radiation
dose issues. Strategies for low-dose CT imaging are necessary in order to prevent side …

Few-view or sparse-view computed tomography has been recently introduced as a great
potential to speed up data acquisition and alleviate the amount of patient radiation dose …