Ke Li

TL;DR: Deep learning method with a common network architecture, termed iCT-Net, was developed and trained to accurately reconstruct images for previously solved and unsolved CT reconstruction problems with high quantitative accuracy, and accurate reconstructions were achieved for the case when the sparse view reconstruction problem is entangled with the classical interior tomographic problems.

TL;DR: Clinical CT scan protocols that had been optimized based on the classical CT noise properties need to be carefully re-evaluated for systems equipped with MBIR in order to maximize the method's potential clinical benefits in dose reduction and/or in CT image quality improvement.

TL;DR: It is demonstrated that multicontrast computed tomography (CT) imaging can be performed using a Talbot-Lau interferometer without phase stepping, thus allowing for an acquisition scheme like that used for standard absorption CT.

TL;DR: CV19-Net was able to differentiate coronavirus disease 2019–related pneumonia from other types of pneumonia, with performance exceeding that of experienced thoracic radiologists.

TL;DR: A systematic investigation of the potential trade-off between spatial resolution and locally defined noise and their joint impact on the overall image quality, which was quantified by the image domain-based channelized Hotelling observer (CHO) detectability index d'.