Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction

To obtain high-quality positron emission tomography (PET) scans while reducing radiation exposure to the human body, various approaches have been proposed to reconstruct standard-dose PET (SPET) images from low-dose PET (LPET) images. One widely adopted technique is the generative adversarial networks (GANs), yet recently, diffusion probabilistic models (DPMs) have emerged as a compelling alternative due to their improved sample quality and higher log-likelihood scores compared to GANs. Despite this, DPMs suffer from two major drawbacks in real clinical settings, i.e., the computationally expensive sampling process and the insufficient preservation of correspondence between the conditioning LPET image and the reconstructed PET (RPET) image. To address the above limitations, this paper presents a coarse-to-fine PET reconstruction framework that consists of a coarse prediction module (CPM) and an iterative refinement module (IRM). The CPM generates a coarse PET image via a deterministic process, and the IRM samples the residual iteratively. By delegating most of the computational overhead to the CPM, the overall sampling speed of our method can be significantly improved. Furthermore, two additional strategies, i.e., an auxiliary guidance strategy and a contrastive diffusion strategy, are proposed and integrated into the reconstruction process, which can enhance the correspondence between the LPET image and the RPET image, further improving clinical reliability. Extensive experiments on two human brain PET datasets demonstrate that our method outperforms the state-of-the-art PET reconstruction methods. The source code is available at \url{https://github.com/Show-han/PET-Reconstruction}.Comment: Accepted and presented in MICCAI 2023. To be published in Proceeding

Multiscale and Multitopic Sparse Representation for Multisensor Infrared Image Superresolution

Methods based on sparse coding have been successfully used in single-image superresolution (SR) reconstruction. However, the traditional sparse representation-based SR image reconstruction for infrared (IR) images usually suffers from three problems. First, IR images always lack detailed information. Second, a traditional sparse dictionary is learned from patches with a fixed size, which may not capture the exact information of the images and may ignore the fact that images naturally come at different scales in many cases. Finally, traditional sparse dictionary learning methods aim at learning a universal and overcomplete dictionary. However, many different local structural patterns exist. One dictionary is inadequate in capturing all of the different structures. We propose a novel IR image SR method to overcome these problems. First, we combine the information from multisensors to improve the resolution of the IR image. Then, we use multiscale patches to represent the image in a more efficient manner. Finally, we partition the natural images into documents and group such documents to determine the inherent topics and to learn the sparse dictionary of each topic. Extensive experiments validate that using the proposed method yields better results in terms of quantitation and visual perception than many state-of-the-art algorithms

An Efficient Image Enlargement Method for Image Sensors of Mobile in Embedded Systems

Main challenges for image enlargement methods in embedded systems come from the requirements of good performance, low computational cost, and low memory usage. This paper proposes an efficient image enlargement method which can meet these requirements in embedded system. Firstly, to improve the performance of enlargement methods, this method extracts different kind of features for different morphologies with different approaches. Then, various dictionaries based on different kind of features are learned, which represent the image in a more efficient manner. Secondly, to accelerate the enlargement speed and reduce the memory usage, this method divides the atoms of each dictionary into several clusters. For each cluster, separate projection matrix is calculated. This method reformulates the problem as a least squares regression. The high-resolution (HR) images can be reconstructed based on a few projection matrixes. Numerous experiment results show that this method has advantages such as being efficient and real-time and having less memory cost. These advantages make this method easy to implement in mobile embedded system

Survival and morbidity in very preterm infants in Shenzhen: a multi-center study

ObjectiveTo analyze survival and morbidity among very preterm infants (VPIs) in Shenzhen and explore factors associated with survival without major morbidity.MethodsBetween January 2022 and December 2022, 797 infants were admitted to 25 neonatal intensive care units in Shenzhen with gestational age (GA) < 32 weeks, excluded discharged against medical advice, insufficient information, and congenital malformation, 742 VPIs were included. Comparison of maternal and neonate characteristics, morbidities, survival, and survival without major morbidities between groups used Mann Whitney U test and X2 test, multivariate logistic regression was used to analyze of risk factors of survival without major morbidities.ResultsThe median GA was 29.86 weeks (interquartile range [IQR], 28.0–31.04), and the median birth weight was 1,250 g (IQR, 900–1,500). Of the 797 VPIs, 721 (90.46%) survived, 53.52% (38 of 71) at 25 weeks’ or less GA, 86.78% (105 of 121) at 26 to 27 weeks' GA, 91.34% (211 of 230) at 28 to 29 weeks' GA, 97.86% (367 of 375) at 30 to 31 weeks' GA. The incidences of the major morbidities were moderate-to-severe bronchopulmonary dysplasia,16.52% (113 of 671); severe intraventricular hemorrhage and/or periventricular leukomalacia, 2.49% (17 of 671); severe necrotizing enterocolitis, 2.63% (18 of 671); sepsis, 2.34% (16 of 671); and severe retinopathy of prematurity, 4.55% (27 of 593), 65.79% (450 of 671) survived without major morbidities. After adjustment for GA, birth weight, and 5-min Apgar score, antenatal steroid administration (OR = 2.397), antenatal magnesium sulfate administration (OR =  1.554) were the positivity factors to survival without major morbidity of VPIs, however, surfactant therapy (OR = 0.684,), and delivery room resuscitation (OR = 0.626) that were the negativity factors.ConclusionsThe present results indicate that survival and the incidence of survival without major morbidities increased with GA. Further, antenatal administration of steroids and magnesium sulfate, surfactant therapy, and delivery room resuscitation were pronounced determinants of survival without morbidities

Lightweight Dual Mutual-Feedback Network for Artificial Intelligence in Medical Image Super-Resolution

As a result of hardware resource constraints, it is difficult to obtain medical images with a sufficient resolution to diagnose small lesions. Recently, super-resolution (SR) was introduced into the field of medicine to enhance and restore medical image details so as to help doctors make more accurate diagnoses of lesions. High-frequency information enhances the accuracy of the image reconstruction, which is demonstrated by deep SR networks. However, deep networks are not applicable to resource-constrained medical devices because they have too many parameters, which requires a lot of memory and higher processor computing power. For this reason, a lightweight SR network that demonstrates good performance is needed to improve the resolution of medical images. A feedback mechanism enables the previous layers to perceive high-frequency information of the latter layers, but no new parameters are introduced, which is rarely used in lightweight networks. Therefore, in this work, a lightweight dual mutual-feedback network (DMFN) is proposed for medical image super-resolution, which contains two back-projection units that operate in a dual mutual-feedback manner. The features generated by the up-projection unit are fed back into the down-projection unit and, simultaneously, the features generated by the down-projection unit are fed back into the up-projection unit. Moreover, a contrast-enhanced residual block (CRB) is proposed as each cell block used in projection units, which enhances the pixel contrast in the channel and spatial dimensions. Finally, we designed a unity feedback to down-sample the SR result as the inverse process of SR. Furthermore, we compared it with the input LR to narrow the solution space of the SR function. The final ablation studies and comparison results show that our DMFN performs well without utilizing a large amount of computing resources. Thus, it can be used in resource-constrained medical devices to obtain medical images with better resolutions

Multi-Scale Mixed Attention Network for CT and MRI Image Fusion

Recently, the rapid development of the Internet of Things has contributed to the generation of telemedicine. However, online diagnoses by doctors require the analyses of multiple multi-modal medical images, which are inconvenient and inefficient. Multi-modal medical image fusion is proposed to solve this problem. Due to its outstanding feature extraction and representation capabilities, convolutional neural networks (CNNs) have been widely used in medical image fusion. However, most existing CNN-based medical image fusion methods calculate their weight maps by a simple weighted average strategy, which weakens the quality of fused images due to the effect of inessential information. In this paper, we propose a CNN-based CT and MRI image fusion method (MMAN), which adopts a visual saliency-based strategy to preserve more useful information. Firstly, a multi-scale mixed attention block is designed to extract features. This block can gather more helpful information and refine the extracted features both in the channel and spatial levels. Then, a visual saliency-based fusion strategy is used to fuse the feature maps. Finally, the fused image can be obtained via reconstruction blocks. The experimental results of our method preserve more textual details, clearer edge information and higher contrast when compared to other state-of-the-art methods

Lightweight Dual Mutual-Feedback Network for Artificial Intelligence in Medical Image Super-Resolution

As a result of hardware resource constraints, it is difficult to obtain medical images with a sufficient resolution to diagnose small lesions. Recently, super-resolution (SR) was introduced into the field of medicine to enhance and restore medical image details so as to help doctors make more accurate diagnoses of lesions. High-frequency information enhances the accuracy of the image reconstruction, which is demonstrated by deep SR networks. However, deep networks are not applicable to resource-constrained medical devices because they have too many parameters, which requires a lot of memory and higher processor computing power. For this reason, a lightweight SR network that demonstrates good performance is needed to improve the resolution of medical images. A feedback mechanism enables the previous layers to perceive high-frequency information of the latter layers, but no new parameters are introduced, which is rarely used in lightweight networks. Therefore, in this work, a lightweight dual mutual-feedback network (DMFN) is proposed for medical image super-resolution, which contains two back-projection units that operate in a dual mutual-feedback manner. The features generated by the up-projection unit are fed back into the down-projection unit and, simultaneously, the features generated by the down-projection unit are fed back into the up-projection unit. Moreover, a contrast-enhanced residual block (CRB) is proposed as each cell block used in projection units, which enhances the pixel contrast in the channel and spatial dimensions. Finally, we designed a unity feedback to down-sample the SR result as the inverse process of SR. Furthermore, we compared it with the input LR to narrow the solution space of the SR function. The final ablation studies and comparison results show that our DMFN performs well without utilizing a large amount of computing resources. Thus, it can be used in resource-constrained medical devices to obtain medical images with better resolutions

Taraxerol Induces Cell Apoptosis through A Mitochondria-Mediated Pathway in HeLa Cells

Objective: Taraxerol acetate has potent anti-cancer effects via the induction of apoptosis, autophagy, cell cycle arrest, and inhibition of cell migration. However, whether taraxerol induced apoptosis and its underlying mechanisms of action is not clear. In the present study, we assess the effects of taraxerol on the mitochondrial apoptotic pathway and determine the release of cytochrome c to the cytosol and activation of caspases. Materials and Methods: In this experimental study, we mainly investigated the effect of taraxerol on HeLa cells. We tested cell viability by the MTT assay and morphologic changes, analyzed apoptosis by DAPI staining and flow cytometry. We also determined reactive oxygen species (ROS) and mitochondrial membrane potential (MMP) using a Microplate Reader. In addition, the apoptotic proteins were tested by Western blot. Results: Taraxerol enhanced ROS levels and attenuated the MMP (Δψm) in HeLa cells. Taraxerol induced apoptosis mainly via the mitochondrial pathway including the release of cytochrome c to the cytosol and activation of caspases 9 and 3, and anti-poly (ADPribose) polymerase (PARP). Taraxerol could induce the down-regulation of the anti-apoptotic protein Bcl-2 and up-regulation of pro-apoptotic protein Bax. It suppressed the PI3K/ Akt signaling pathway. Conclusion: These results demonstrated that taraxerol induced cell apoptosis through a mitochondria-mediated pathway in HeLa cells. Thus, taraxerol might be a potential anticervical cancer candidate