Deep-learning-based image reconstruction in dynamic contrast-enhanced abdominal CT: image quality and lesion detection among reconstruction strength levels

Impact of model-based iterative reconstruction on image quality of contrast-enhanced neck CT.

BACKGROUND AND PURPOSE Improved image quality is clinically desired for contrast-enhanced CT of the neck. We compared 30% adaptive statistical iterative reconstruction and model-based iterative reconstruction algorithms for the assessment of image quality of contrast-enhanced CT of the neck. MATERIALS AND METHODS Neck contrast-enhanced CT data from 64 consecutive patients were reconstructed r...

Image Reconstruction from Incomplete Data

Image Reconstruction from Incomplete Data

Phase-contrast CT: Fundamental theorem and fast image reconstruction algorithms

Phase-contrast x-ray computed tomography (CT) is an emerging imaging technique that can be implemented at third generation synchrotron radiation sources or by using a microfocus x-ray tube. Promising experimental results have recently been obtained in material science and biological applications. At the same time, the lack of a mathematical theory comparable to that of conventional absorption-b...

CT Image Reconstruction Based on GPUs

In X-ray computed tomography (CT) iterative methods are more suitable for the reconstruction of images with high contrast and precision in noisy conditions and from a small number of projections. However, in practice, these methods are not widely used due to the high computational cost of their implementation. Nowadays technology provides the possibility to reduce effectively this drawback. It ...