Reconstruction method suitable for fast CT imaging.

Reconstructing computed tomography (CT) images from an extremely limited set of projections is crucial in practical applications. As the available projections significantly decrease, traditional reconstruction and model-based iterative reconstruction methods become constrained. This work aims to seek a reconstruction method applicable to fast CT imaging when available projections are highly sparse. To minimize the time and cost associated with projections acquisition, we propose a deep learning model, X-CTReNet, which parameterizes a nonlinear mapping function from orthogonal projections to CT volumes for 3D reconstruction. The proposed model demonstrates effective capability in inferring CT volumes from two-view projections compared to baseline methods, highlighting the significant potential for drastically reducing projection acquisition in fast CT imaging.

MDST: multi-domain sparse-view CT reconstruction based on convolution and swin transformer.

Objective.Sparse-view computed tomography (SVCT), which can reduce the radiation doses administered to patients and hasten data acquisition, has become an area of particular interest to researchers. Most existing deep learning-based image reconstruction methods are based on convolutional neural networks (CNNs). Due to the locality of convolution and continuous sampling operations, existing approaches cannot fully model global context feature dependencies, which makes the CNN-based approaches less efficient in modeling the computed tomography (CT) images with various structural information.Approach.To overcome the above challenges, this paper develops a novel multi-domain optimization network based on convolution and swin transformer (MDST). MDST uses swin transformer block as the main building block in both projection (residual) domain and image (residual) domain sub-networks, which models global and local features of the projections and reconstructed images. MDST consists of two modules for initial reconstruction and residual-assisted reconstruction, respectively. The sparse sinogram is first expanded in the initial reconstruction module with a projection domain sub-network. Then, the sparse-view artifacts are effectively suppressed by an image domain sub-network. Finally, the residual assisted reconstruction module to correct the inconsistency of the initial reconstruction, further preserving image details.Main results. Extensive experiments on CT lymph node datasets and real walnut datasets show that MDST can effectively alleviate the loss of fine details caused by information attenuation and improve the reconstruction quality of medical images.Significance.MDST network is robust and can effectively reconstruct images with different noise level projections. Different from the current prevalent CNN-based networks, MDST uses transformer as the main backbone, which proves the potential of transformer in SVCT reconstruction.

Exercise Dosage in Reducing the Risk of Dementia Development: Mode, Duration, and Intensity-A Narrative Review.

Senile dementia, also known as dementia, is the mental deterioration which is associated with aging. It is characterized by a decrease in cognitive abilities, inability to concentrate, and especially the loss of higher cerebral cortex function, including memory, judgment, abstract thinking, and other loss of personality, even behavior changes. As a matter of fact, dementia is the deterioration of mental and intellectual functions caused by brain diseases in adults when they are mature, which affects the comprehensive performance of life and work ability. Most dementia cases are caused by Alzheimer's disease (AD) and multiple infarct dementia (vascular dementia, multi-infarct dementia). Alzheimer's disease is characterized by atrophy, shedding, and degenerative alterations in brain cells, and its occurrence is linked to age. The fraction of the population with dementia is smaller before the age of 65, and it increases after the age of 65. Since women live longer than men, the proportion of women with Alzheimer's disease is higher. Multiple infarct dementia is caused by a cerebral infarction, which disrupts blood supply in multiple locations and impairs cerebral cortex function. Researchers worldwide are investigating ways to prevent Alzheimer's disease; however, currently, there are no definitive answers for Alzheimer's prevention. Even so, research has shown that we can take steps to reduce the risk of developing it. Prospective studies have found that even light to moderate physical activity can lower the risk of dementia and Alzheimer's disease. Exercise has been proposed as a potential lifestyle intervention to help reduce the occurrence of dementia and Alzheimer's disease. Various workout modes will be introduced based on various physical conditions. In general, frequent exercise for 6-8 weeks lessens the risk of dementia development.

3D M-Net: Object-Specific 3D Segmentation Network Based on a Single Projection.

The internal assembly correctness of industrial products directly affects their performance and service life. Industrial products are usually protected by opaque housing, so most internal detection methods are based on X-rays. Since the dense structural features of industrial products, it is challenging to detect the occluded parts only from projections. Limited by the data acquisition and reconstruction speeds, CT-based detection methods do not achieve real-time detection. To solve the above problems, we design an end-to-end single-projection 3D segmentation network. For a specific product, the network adopts a single projection as input to segment product components and output 3D segmentation results. In this study, the feasibility of the network was verified against data containing several typical assembly errors. The qualitative and quantitative results reveal that the segmentation results can meet industrial assembly real-time detection requirements and exhibit high robustness to noise and component occlusion.