Lesion discrimination with breath-hold hepatic diffusionweighted imaging:A meta-analysis

AIM:To investigate the diagnostic capability of breathhold diffusion-weighted imaging(DWI) for differentiation between malignant and benign hepatic lesions.METHODS:A total of 614 malignant liver lesions(132 hepatocellular carcinomas,468 metastases and 14 intrahepatic cholangiocarcinomas) and 291 benign liver lesions(102 hemangiomas,158 cysts,24 focal nodular hyperplasia,1 angiomyolipoma and 6 hepatic adenomas) were included from seven studies(eight sets of data).RESULTS:The pooled sensitivity and specificity of breath-hold DWI were 0.93 [95% confidence interval(CI):0.91-0.95] and 0.87(95%CI:0.83-0.91),respectively.The positive likelihood ratio and negative likelihood ratio were 7.28(95%CI:4.51-11.76) and 0.09(95%CI:0.05-0.17),respectively.The P value for χ2 heterogeneity for all pooled estimates was < 0.05.From the fitted summary receiver operating characteristic curve,the area under the curve and Q * index were 0.96 and 0.91,respectively.Publication bias was not present(t = 0.49,P = 0.64).The meta-regression analysis indicated that evaluated covariates including magnetic resonance imaging modality,echo time,mean age,maximum b factor,and number of b factors were not sources of heterogeneity(all P > 0.05).CONCLUSION:Breath-hold DWI is useful for differentiating between malignant and benign hepatic lesions.The diffusion characteristics of benign lesions that mimic malignant ones have rarely been investigated.

Design and Implementation of the Tianhe-2 Data Storage and Management System

With the convergence of high-performance computing(HPC),big data and artificial intelligence(AI),the HPC community is pushing for"triple use"systems to expedite scientific discoveries.However,supporting these converged applications on HPC systems presents formidable challenges in terms of storage and data management due to the explosive growth of scientific data and the fundamental differences in I/O characteristics among HPC,big data and AI workloads.In this paper,we discuss the driving force behind the converging trend,highlight three data management challenges,and summarize our efforts in addressing these data management challenges on a typical HPC system at the parallel file system,data management middleware,and user application levels.As HPC systems are approaching the border of exascale computing,this paper sheds light on how to enable application-driven data management as a preliminary step toward the deep convergence of exascale computing ecosystems,big data,and AI.

A GPU-Accelerated In-Memory Metadata Management Scheme forLarge-Scale Parallel File Systems

Driven by the increasing requirements of high-performance computing applications,supercomputers are prone to containing more and more computing nodes.Applications running on such a large-scale computing system are likely to spawn millions of parallel processes,which usually generate a burst of I/O requests,introducing a great challenge into the metadata management of underlying parallel file systems.The traditional method used to overcome such a challenge is adopting multiple metadata servers in the scale-out manner,which will inevitably confront with serious network and consistence problems.This work instead pursues to enhance the metadata performance in the scale-up manner.Specifically,we propose to improve the performance of each individual metadata server by employing GPU to handle metadata requests in parallel.Our proposal designs a novel metadata server architecture,which employs CPU to interact with file system clients,while offloading the computing tasks about metadata into GPU.To take full advantages of the parallelism existing in GPU,we redesign the in-memory data structure for the name space of file systems.The new data structure can perfectly fit to the memory architecture of GPU,and thus helps to exploit the large number of parallel threads within GPU to serve the bursty metadata requests concurrently.We implement a prototype based on BeeGFS and conduct extensive experiments to evaluate our proposal,and the experimental results demonstrate that our GPU-based solution outperforms the CPU-based scheme by more than 50%under typical metadata operations.The superiority is strengthened further on high concurrent scenarios,e.g.,the high-performance computing systems supporting millions of parallel threads.