Segmentation of the left atrial appendage based on fusion attention.

In clinical practice, the morphology of the left atrial appendage (LAA) plays an important role in the selection of LAA closure devices for LAA closure procedures. The morphology determination is influenced by the segmentation results. The LAA occupies only a small part of the entire 3D medical image, and the segmentation results are more likely to be biased towards the background region, making the segmentation of the LAA challenging.

Deep-Learning-Based Metal Artefact Reduction With Unsupervised Domain Adaptation Regularization for Practical CT Images.

CT metal artefact reduction (MAR) methods based on supervised deep learning are often troubled by domain gap between simulated training dataset and real-application dataset, i.e., methods trained on simulation cannot generalize well to practical data.

Method of sparse-view coded-aperture x-ray diffraction tomography.

X-ray diffraction (XRD) has been considered as a valuable diagnostic technology providing material specific 'finger-print' information i.e. XRD pattern to distinguish different biological tissues.

Reciprocal-FDK reconstruction for x-ray diffraction computed tomography.

X-ray diffraction (XRD) technology uses x-ray small-angle scattering signal for material analysis, which is highly sensitive to material inter-molecular structure. To meet the high spatial resolution requirement in applications such as medical imaging, XRD computed tomography (XRDCT) has been proposed to provide XRD intensity with improved spatial resolution from point-wise XRD scan. In XRDCT, 2D spatial tomography corresponds to a 3D reconstruction problem with the third dimension being the XRD spectrum dimension, i.

A CT image feature space (CTIS) loss for restoration with deep learning-based methods.

Deep learning-based methods have been widely used in medical imaging field such as detection, segmentation and image restoration. For supervised learning methods in CT image restoration, different loss functions will lead to different image qualities which may affect clinical diagnosis. In this paper, to compare commonly used loss functions and give a better alternative, we studied a widely generalizable framework for loss functions which are defined in the feature space extracted by neural networks.

Metal artifact reduction for practical dental computed tomography by improving interpolation-based reconstruction with deep learning.

Purpose: Metal artifact is a quite common problem in diagnostic dental computed tomography (CT) images. Due to the high attenuation of heavy materials such as metal, severe global artifacts can occur in reconstructions. Typical metal artifact reduction (MAR) techniques segment out the metal regions and estimate the corrupted projection data by various interpolation methods.

Assessment of 16S rRNA gene primers for studying bacterial community structure and function of aging flue-cured tobaccos.

Selection of optimal primer pairs in 16S rRNA gene sequencing is a pivotal issue in microorganism diversity analysis. However, limited effort has been put into investigation of specific primer sets for analysis of the bacterial diversity of aging flue-cured tobaccos (AFTs), as well as prediction of the function of the bacterial community. In this study, the performance of four primer pairs in determining bacterial community structure based on 16S rRNA gene sequences in AFTs was assessed, and the functions of genes were predicted using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt).