Purpose Deep-layer learning processing may improve contrast imaging with greater
precision in low-count acquisition. However, no data on noise reduction using super …

Rationale and Objectives Deep-learning-based super-resolution image reconstruction
(DLSRR) is a novel image reconstruction technique that is expected to contribute to …

Purpose To use a diffusion-based deep learning model to recover bone microstructure from
low-resolution images of the proximal femur, a common site of traumatic osteoporotic …

Purpose This study aimed to assess the noise characteristics of ultra-high-resolution
computed tomography (UHRCT) with deep learning-based reconstruction (DLR). Methods …

Background One of the limitations in leveraging the potential of artificial intelligence in X‐ray
imaging is the limited availability of annotated training data. As X‐ray and CT shares similar …

Because single-photon emission computed tomography (SPECT) is one of the widely used
nuclear medicine imaging systems, it is extremely important to acquire high-quality images …

Single image super-resolution (SR) method can generate a high-resolution (HR) image from
a low-resolution (LR) image by enhancing image resolution. In medical imaging, HR images …

The objective of this study is to develop a convolutional neural network (CNN) for computed
tomography (CT) image super-resolution. The network learns an end-to-end mapping …

Objectives: To demonstrate the effect of an improved deep learning-based reconstruction
(DLR) algorithm on Ultra-High-Resolution Computed Tomography (U-HRCT) scanners …

Purpose Clinical cone-beam computed tomography (CBCT) devices are limited to imaging
features of half a millimeter in size and cannot quantify the tissue microstructure. We …