A scalable open-source MATLAB toolbox for reconstruction and analysis of multispectral optoacoustic tomography data

AbstractMultispectral photoacoustic tomography enables the resolution of spectral components of a tissue or sample at high spatiotemporal resolution. With the availability of commercial instruments, the acquisition of data using this modality has become consistent and standardized. However, the analysis of such data is often hampered by opaque processing algorithms, which are challenging to verify and validate from a user perspective. Furthermore, such tools are inflexible, often locking users into a restricted set of processing motifs, which may not be able to accommodate the demands of diverse experiments. To address these needs, we have developed a Reconstruction, Analysis, and Filtering Toolbox to support the analysis of photoacoustic imaging data. The toolbox includes several algorithms to improve the overall quantification of photoacoustic imaging, including non-negative constraints and multispectral filters. We demonstrate various use cases, including dynamic imaging challenges and quantification of drug effect, and describe the ability of the toolbox to be parallelized on a high performance computing cluster.

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Rapidly Reconfigurable High Performance Computing Cluster

10.21236/ada438586 ◽

2005 ◽

Author(s):

Mark A. Richards ◽

Daniel P. Campbell

Keyword(s):

High Performance Computing ◽

High Performance ◽

High Performance Computing Cluster ◽

Performance Computing ◽

Computing Cluster

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Fast Playback Framework for Analysis of Ground-Based Doppler Radar Observations Using MapReduce Technology

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-15-0118.1 ◽

2016 ◽

Vol 33 (4) ◽

pp. 621-634 ◽

Cited By ~ 4

Author(s):

Jingyin Tang ◽

Corene J. Matyas

Keyword(s):

Spatial Analysis ◽

High Performance ◽

Large Scale ◽

Doppler Radar ◽

Weather Events ◽

Data Architecture ◽

Research Grade ◽

High Performance Computing Cluster ◽

Time Systems ◽

Performance Computing

AbstractThe creation of a 3D mosaic is often the first step when using the high-spatial- and temporal-resolution data produced by ground-based radars. Efficient yet accurate methods are needed to mosaic data from dozens of radar to better understand the precipitation processes in synoptic-scale systems such as tropical cyclones. Research-grade radar mosaic methods of analyzing historical weather events should utilize data from both sides of a moving temporal window and process them in a flexible data architecture that is not available in most stand-alone software tools or real-time systems. Thus, these historical analyses require a different strategy for optimizing flexibility and scalability by removing time constraints from the design. This paper presents a MapReduce-based playback framework using Apache Spark’s computational engine to interpolate large volumes of radar reflectivity and velocity data onto 3D grids. Designed as being friendly to use on a high-performance computing cluster, these methods may also be executed on a low-end configured machine. A protocol is designed to enable interoperability with GIS and spatial analysis functions in this framework. Open-source software is utilized to enhance radar usability in the nonspecialist community. Case studies during a tropical cyclone landfall shows this framework’s capability of efficiently creating a large-scale high-resolution 3D radar mosaic with the integration of GIS functions for spatial analysis.

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META-pipe cloud setup and execution

F1000Research ◽

10.12688/f1000research.13204.1 ◽

2017 ◽

Vol 6 ◽

pp. 2060

Author(s):

Aleksandr Agafonov ◽

Kimmo Mattila ◽

Cuong Duong Tuan ◽

Lars Tiede ◽

Inge Alexander Raknes ◽

...

Keyword(s):

Functional Annotation ◽

High Performance ◽

Sequence Data ◽

Metagenomic Data ◽

Taxonomic Profiling ◽

Geographically Distributed ◽

Computationally Intensive ◽

High Performance Computing Cluster ◽

And Storage ◽

Performance Computing

META-pipe is a complete service for the analysis of marine metagenomic data. It provides assembly of high-throughput sequence data, functional annotation of predicted genes, and taxonomic profiling. The functional annotation is computationally demanding and is therefore currently run on a high-performance computing cluster in Norway. However, additional compute resources are necessary to open the service to all ELIXIR users. We describe our approach for setting up and executing the functional analysis of META-pipe on additional academic and commercial clouds. Our goal is to provide a powerful analysis service that is easy to use and to maintain. Our design therefore uses a distributed architecture where we combine central servers with multiple distributed backends that execute the computationally intensive jobs. We believe our experiences developing and operating META-pipe provides a useful model for others that plan to provide a portal based data analysis service in ELIXIR and other organizations with geographically distributed compute and storage resources.

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