Direct Multi-Material Reconstruction via Iterative Proximal Adaptive Descent for Spectral CT Imaging.

Spectral computed tomography (spectral CT) is a promising medical imaging technology because of its ability to provide information on material characterization and quantification. However, with an increasing number of basis materials, the nonlinearity of measurements causes difficulty in decomposition. In addition, noise amplification and beam hardening further reduce image quality.

Low-dose spectral reconstruction with global, local, and nonlocal priors based on subspace decomposition.

Background: Multienergy computed tomography (MECT) is a promising imaging modality for material decomposition, lesion detection, and other clinical applications. However, there is an urgent need to design efficient and accurate algorithms to solve the inverse problems related to spectral reconstruction and improve image quality, especially under low-dose and incomplete datasets. The key issue for MECT reconstruction is how to efficiently describe the interchannel and intrachannel priors in multichannel images.

Super-resolution image reconstruction from sparsity regularization and deep residual-learned priors.

Background: Computed tomography (CT) plays an important role in the field of non-destructive testing. However, conventional CT images often have blurred edge and unclear texture, which is not conducive to the follow-up medical diagnosis and industrial testing work.

Objective: This study aims to generate high-resolution CT images using a new CT super-resolution reconstruction method combining with the sparsity regularization and deep learning prior.

Projection domain processing for low-dose CT reconstruction based on subspace identification.

Purpose: Low-dose computed tomography (LDCT) has promising potential for dose reduction in medical applications, while suffering from low image quality caused by noise. Therefore, it is in urgent need for developing new algorithms to obtain high-quality images for LDCT.

Methods: This study tries to exploit the sparse and low-rank properties of images and proposes a new algorithm based on subspace identification.

Total variation combining nonlocal means filtration for image reconstruction in X-ray computed tomography.

Background: Image reconstruction for realistic medical images under incomplete observation is still one of the core tasks for computed tomography (CT). However, the stair-case artifacts of Total variation (TV) based ones have restricted the usage of the reconstructed images.

Objective: This work aims to propose and test an accurate and efficient algorithm to improve reconstruction quality under the idea of synergy between local and nonlocal regularizations.

Framelet tensor sparsity with block matching for spectral CT reconstruction.

Purpose: Spectral computed tomography (CT) based on the photon-counting detection system has the capability to produce energy-discriminative attenuation maps of objects with a single scan. However, the insufficiency of photons collected into the narrow energy bins results in high quantum noise levels causing low image quality. This work aims to improve spectral CT image quality by developing a novel regularization based on framelet tensor prior.

One half-scan dual-energy CT imaging using the Dual-domain Dual-way Estimated Network (DoDa-Net) model.

Background: Compared with single-energy computed tomography (CT), dual-energy CT (DECT) can distinguish materials better. However, most DECT reconstruction theories require two full-scan projection datasets of different energies, and this requirement is hard to meet, especially for cases where a physical blockage disables a full circular rotation. Thus, it is critical to relax the requirements of data acquisition to promote the application of DECT.

Perfect pair, scopes unite - laparoscopic-assisted transumbilical gastroscopy for gallbladder-preserving polypectomy: A case report.

Background: Gallbladder polyps are one indication for cholecystectomy, but this procedure carries some disadvantages, including the potential for severe injury and high risk of post-operative complications. Laparoscopy combined with endoscopic surgery is a minimally invasive treatment option. We herein report a young patient with a gallbladder polyp who was successfully discharged from the hospital after laparoscopic-assisted endoscopy.