Diffusion tensor imaging pipeline measures of cerebral white matter integrity: An overview of recent advances and prospects

Cerebral small vessel disease(CSVD)is a leading cause of age-related microvascular cognitive decline,resulting in significant morbidity and decreased quality of life.Despite a progress on its key pathophysiological bases and general acceptance of key terms from neuroimaging findings as observed on the magnetic resonance imaging(MRI),key questions on CSVD remain elusive.Enhanced relationships and reliable lesion studies,such as white matter tractography using diffusion-based MRI(dMRI)are necessary in order to improve the assessment of white matter architecture and connectivity in CSVD.Diffusion tensor imaging(DTI)and tractography is an application of dMRI that provides data that can be used to non-invasively appraise the brain white matter connections via fiber tracking and enable visualization of individual patient-specific white matter fiber tracts to reflect the extent of CSVD-associated white matter damage.However,due to a lack of standardization on various sets of software or image pipeline processing utilized in this technique that driven mostly from research setting,interpreting the findings remain contentious,especially to inform an improved diagnosis and/or prognosis of CSVD for routine clinical use.In this minireview,we highlight the advances in DTI pipeline processing and the prospect of this DTI metrics as potential imaging biomarker for CSVD,even for subclinical CSVD in at-risk individuals.

Screening of COVID-19 Patients Using Deep Learning and IoT Framework

In March 2020,the World Health Organization declared the coronavirus disease(COVID-19)outbreak as a pandemic due to its uncontrolled global spread.Reverse transcription polymerase chain reaction is a laboratory test that is widely used for the diagnosis of this deadly disease.However,the limited availability of testing kits and qualified staff and the drastically increasing number of cases have hampered massive testing.To handle COVID19 testing problems,we apply the Internet of Things and artificial intelligence to achieve self-adaptive,secure,and fast resource allocation,real-time tracking,remote screening,and patient monitoring.In addition,we implement a cloud platform for efficient spectrum utilization.Thus,we propose a cloudbased intelligent system for remote COVID-19 screening using cognitiveradio-based Internet of Things and deep learning.Specifically,a deep learning technique recognizes radiographic patterns in chest computed tomography(CT)scans.To this end,contrast-limited adaptive histogram equalization is applied to an input CT scan followed by bilateral filtering to enhance the spatial quality.The image quality assessment of the CT scan is performed using the blind/referenceless image spatial quality evaluator.Then,a deep transfer learning model,VGG-16,is trained to diagnose a suspected CT scan as either COVID-19 positive or negative.Experimental results demonstrate that the proposed VGG-16 model outperforms existing COVID-19 screening models regarding accuracy,sensitivity,and specificity.The results obtained from the proposed system can be verified by doctors and sent to remote places through the Internet.

A parallel pipeline connected-component labeling method for on-orbit space target monitoring

The paper designs a peripheral maximum gray differ-ence(PMGD)image segmentation method,a connected-compo-nent labeling(CCL)algorithm based on dynamic run length(DRL),and a real-time implementation streaming processor for DRL-CCL.And it verifies the function and performance in space target monitoring scene by the carrying experiment of Tianzhou-3 cargo spacecraft(TZ-3).The PMGD image segmentation method can segment the image into highly discrete and simple point tar-gets quickly,which reduces the generation of equivalences greatly and improves the real-time performance for DRL-CCL.Through parallel pipeline design,the storage of the streaming processor is optimized by 55%with no need for external me-mory,the logic is optimized by 60%,and the energy efficiency ratio is 12 times than that of the graphics processing unit,62 times than that of the digital signal proccessing,and 147 times than that of personal computers.Analyzing the results of 8756 images completed on-orbit,the speed is up to 5.88 FPS and the target detection rate is 100%.Our algorithm and implementation method meet the requirements of lightweight,high real-time,strong robustness,full-time,and stable operation in space irradia-tion environment.

Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis

While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising,existing methods mostly rely on simple noise assumptions,such as additive white Gaussian noise(AWGN),JPEG compression noise and camera sensor noise,and a general-purpose blind denoising method for real images remains unsolved.In this paper,we attempt to solve this problem from the perspective of network architecture design and training data synthesis.Specifically,for the network architecture design,we propose a swin-conv block to incorporate the local modeling ability of residual convolutional layer and non-local modeling ability of swin transformer block,and then plug it as the main building block into the widely-used image-to-image translation UNet architecture.For the training data synthesis,we design a practical noise degradation model which takes into consideration different kinds of noise(including Gaussian,Poisson,speckle,JPEG compression,and processed camera sensor noises)and resizing,and also involves a random shuffle strategy and a double degradation strategy.Extensive experiments on AGWN removal and real image denoising demonstrate that the new network architecture design achieves state-of-the-art performance and the new degradation model can help to significantly improve the practicability.We believe our work can provide useful insights into current denoising research.The source code is available at https://github.com/cszn/SCUNet.

PopFlow:a novel flow management scheme for SDN switch of multiple flow tables based on flow popularity

Pipeline processing is applied to mutiple flow tables(MFT)in the switch of software-defined network(SDN)to increase the throughput of the flows.However,the processing time of each flow increases as the size or number of flow tables gets larger.In this paper we propose a novel approach called PopFlow where a table keeping popular flow entries is located up front in the pipeline,and an express path is provided for the flow matching the table.A Markov model is employed for the selection of popular entries considering the match latency and match frequency,and Queuing theory is used to model the flow processing time of the existing MFT-based schemes and the proposed scheme.Computer simulation reveals that the proposed scheme substantially reduces the flow processing time compared to the existing schemes,and the diference gets more significant as the flow arrival rate increases.