课题基金 / 基金详情

PFI:AIR - TT: Cost Effective Solutions for Storage and Access of Massive Imagery

PFI:AIR - TT: Cost Effective Solutions for Storage and Access of Massive Imagery
PFI:AIR - TT:海量图像存储和访问的经济高效解决方案
批准号:
1602127
负责人:
Valerio Pascucci
金额:
$19.94万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-05-01 至 2017-10-31
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项目摘要

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中文摘要
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英文摘要
This PFI: AIR Technology Translation project focuses on the potential to revolutionize how microscopy and medical devices are used and the science questions that they can answer. When image data size is no longer a restricting factor, new domains of study become possible relating the micro-scale to macro-scale, such as understanding the neural connectomics of the visual cortex. By removing the barrier of time, effort, and expertise to use large imagery, VisStore will enable scientists to scale their existing workflows. Such capability would open new investigations into fundamental biological processes, the origin and progression of diseases, and ultimately the drugs and procedures for curing them. Although initially tailored to life sciences applications, VisStore can be integrated in microscopy for new devices and emerging disciplines, such as precision medicine, material sciences and semiconductors. Furthermore, VisStore has the potential to ease the transition from current workflows to fully online cloud-based ones. Furthermore, VisStore and the hierarchical streaming infrastructure have the potential to become the de-facto standard for large volumetric images. This Accelerating Innovation Technology Translation project will support R&D to build a prototype of VisStore, a plug-and-play device for easy storing, archiving, accessing, distributing, and processing massive volumetric images coming from microscopy or medical devices. It translates research discovery toward commercial applications in the microscopy market which continues to grow, topping $4.1 billion in 2014 with an anticipated CAGR growth of 7.1%, while the addressed cyber-infrastructures to reliably store, easily access, and efficiently process such data have not kept pace. This has led to a discrepancy between the quality of data that could be produced, and what actually is used, as scientists unnecessarily restrict image sizes to match computational capabilities. Brute force solutions for scaling to massive images are expensive, difficult to maintain, and require expertise usually out of reach for smaller institutions. VisStore is a combined software/hardware/cloud solution that enables ease of use for image data of any size. No more complicated than a USB drive, VisStore allows users to easily access, process, and distribute giga and terapixel 2D and 3D images within a workgroup, a company, or even globally distributed environments. The technology behind VisStore enhances the state-of-the-art for handing massive image volumes. Modern software tools often stop scaling when data size exceeds main memory, and this has been a limiting factor for microscopy imagery. When dealing with image data, the hierarchical streaming software infrastructure implemented in VisStore essentially extends the memory hierarchy of a workstation to both an external network-attached hard drive (NAS), and even cloud-based storage. With each component acting as a cache, VisStore achieves performance through on-demand data access to proprietary file layout that minimizes the amount of data transferred between levels, enabling efficient scaling to images of any size. This will support development for: (i) automated ingestion and conversion of images coming from microscopy or medical devices; (ii) a simple user interface and tool to manage local and remote storage of data; and (iii) a tool to select and export data for integration with existing workflows. The project engages the Moran Eye Center, the Associated Regional and University Pathologists, Inc. (ARUP) laboratories and the Oregon Health & Science University to develop and tests a prototype acquiring giga- and teravoxel images and test its commercial value to translate this technology from research discovery towards a commercial reality. In particular, the graduate and undergraduate students supported by the project will be directly involved in these entrepreneurial activities. They will cooperate directly with the early adopters of the technology at the collaborating institutions and receive hands-on experience in how to identify and resolve their pain points and ultimately translate the raw technology into a product with commercial value.
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OAC: Piloting the National Science Data Fabric: A Platform Agnostic Testbed for Democratizing Data Delivery
  • 批准号:
    2138811
  • 项目类别:
    Standard Grant
  • 资助金额:
    $560.93万
  • 财政年份:
    2021
  • 负责人:
    Valerio Pascucci
  • 依托单位:
EAGER: The Next Generation of Smart Cyberinfrastructure: Efficiency and Productivity Through Artificial Intelligence
  • 批准号:
    1941085
  • 项目类别:
    Standard Grant
  • 资助金额:
    $29.97万
  • 财政年份:
    2019
  • 负责人:
    Valerio Pascucci
  • 依托单位:
Computational Infrastructure for Brain Research: EAGER: A Scalable Solution for Processing High Resolution Brain Connectomics Data
  • 批准号:
    1649923
  • 项目类别:
    Standard Grant
  • 资助金额:
    $30.0万
  • 财政年份:
    2016
  • 负责人:
    Valerio Pascucci
  • 依托单位:
CGV: Large: Collaborative Research: Coupling Simulation and Mesh Generation using Computational Topology
  • 批准号:
    1314896
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $118.7万
  • 财政年份:
    2013
  • 负责人:
    Valerio Pascucci
  • 依托单位:
国内基金
海外基金
湍流和化学交互作用对H2-Air-H2O微混燃烧中NO生成的影响研究
  • 批准号:
    51976048
  • 项目类别:
    面上项目
  • 资助金额:
    61.0万元
  • 批准年份:
    2019
  • 负责人:
    邱朋华
  • 依托单位: