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BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods

PLOS COMPUTATIONAL BIOLOGY 2017年第3期13卷 e1005209-e1005209页

作者： Gorgolewski, Krzysztof J. Alfaro-Almagro, Fidel Auer, Tibor Bellec, Pierre Capota, Mihai Chakravarty, M. Mallar Churchill, Nathan W. Cohen, Alexander Li Craddock, R. Cameron Devenyi, Gabriel A. Eklund, Anders Esteban, Oscar Flandin, Guillaume Ghosh, Satrajit S. Guntupalli, J. Swaroop Jenkinson, Mark Keshavan, Anisha Kiar, Gregory Liem, Franziskus Raamana, Pradeep Reddy Raffelt, David Steele, Christopher J. Quirion, Pierre-Olivier Smith, Robert E. Strother, Stephen C. Varoquaux, Gael Wang, Yida Yarkoni, Tal Poldrack, Russell A. Stanford Univ Dept Psychol Stanford CA 94305 USA Univ Oxford Oxford Ctr Funct Magnet Resonance Imaging Brain F Oxford England Royal Holloway Univ London Dept Psychol Egham Surrey England Inst Univ Geriatr Montreal Ctr Rech Montreal PQ Canada Univ Montreal Dept Comp Sci & Operat Res Montreal PQ Canada Intel Corp Parallel Comp Lab Santa Clara CA USA Intel Corp Parallel Comp Lab Hillsboro OR 97124 USA McGill Univ Douglas Mental Hlth Univ Inst Montreal PQ Canada McGill Univ Dept Psychiat Montreal PQ Canada St Michaels Hosp Keenan Res Ctr Li Ka Shing Knowledge Inst Toronto ON Canada Boston Childrens Hosp Dept Neurol Boston MA USA Nathan S Kline Inst Psychiat Res Ctr Biomed Imaging & Neuromodulat Computat Neuroimaging Lab Orangeburg NY 10962 USA Child Mind Inst Ctr Developing Brain New York NY USA Linkoping Univ Dept Biomed Engn Linkoping Sweden Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden Linkoping Univ Ctr Med Image Sci & Visualizat CMIV Linkoping Sweden Wellcome Trust Ctr Neuroimaging London England MIT McGovern Inst Brain Res 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Med Sch Dept Otolaryngol Boston MA USA Dartmouth Coll Dept Psychol & Brain Sci Hanover NH 03755 USA UC Berkeley UCSF Grad Program Bioengn San Francisco CA USA Johns Hopkins Univ Ctr Imaging Sci Baltimore MD USA Johns Hopkins Univ Dept Biomed Engn Baltimore MD USA Univ Zurich Univ Res Prior Program Dynam Hlth Aging Zurich Switzerland Rotman Res Inst Baycrest Hlth Sci Toronto ON Canada Univ Toronto Dept Med Biophys Toronto ON Canada Florey Inst Neurosci & Mental Hlth Melbourne Vic Australia INRIA Saclay Ile De France Parietal Team Palaiseau France Univ Texas Austin Dept Psychol Austin TX 78712 USA

The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.

关键词： Biology and life sciences Brain mapping Brain morphometry Computer and information sciences Data processing Diagnostic medicine Diagnostic radiology Diffusion weighted imaging Engineering and technology Functional magnetic resonance imaging imaging techniques Information technology Magnetic resonance imaging Medicine and health sciences Neuroimaging Neuroscience Operating systems Radiology and imaging Research and analysis methods Research Article Software development Software engineering Software tools

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The open-source turing codec: Toward fast, flexible, and parallel HEVC encoding

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SMPTE Motion imaging Journal 2017年第9期126卷 1-8页

作者： Blasi, Saverio Naccari, Matteo Weerakkody, Rajitha Funnell, John Mrak, Marta

The Turing codec is an open-source software codec compliant with the High Efficiency Video Coding (HEVC) standard and specifically designed for speed, f lexibility, parallelization, and high coding efficiency. The Turing codec was designed starting from a completely novel backbone to comply with the Main and Main10 profiles of HEVC, and it has many desirable features for practical codecs such as very low memory consumption, advanced parallelization schemes, and fast encoding algorithms. This paper presents a technical description of the Turing codec as well as a comparison of its performance with other similar encoders. The codec is capable of cutting the encoding complexity by an average 87% with respect to the HEVC reference implementation for an average coding penalty of 11% higher rates in compression efficiency at the same peak-signalnoise-ratio level. Copyright © 2017 by SMPTE.

关键词： Open source software

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Novel parallel givens QR decomposition implementation on VLIW architecture with efficient memory access for real time image processing applications 17

Novel parallel givens QR decomposition implementation on VLI...

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2nd International Conference on Big Data Cloud and applications, BDCA 2017

作者： Najoui, Mohamed Hatim, Anas Belkouch, Said LGECOS Lab ENSA-Marrakech University of Cadi Ayyad Marrakech Morocco ENSA-Agadir Ibn Zohr University Agadir Morocco

ISBN: (纸本)9781450348522

Compressed Sensing (CS) methods impact is important in the health care systems. The acquisition and the processing speed of many medical imaging applications is highly improved using this technique. Orthogonal Matching Pursuit (OMP) is one of the widely used image reconstruction algorithms in CS techniques. Traditionally, OMP is divided on two main tasks: optimization stage and least square problem (LSP) resolution stage. The LSP resolution is the most complex and time consuming step in OMP. QRD is one of the main techniques used to solve the LSP in a very short time. However, the increasing image and video resolutions (UHDTV) makes the real time constraints very tight. In this paper, a novel implementation of QRD on VLIW DSP architecture with an efficient memory access approach is introduced. An instruction, data and loop levels parallelism (ILP, DLP and LLP) based Givens QRD kernel is designed. A robust memory access management based on intelligent loading/storing strategies and proficient data alignment techniques is adopted for the proposed parallel implementation. Therefore, memory read misses and CPU stalls are significantly diminished. As a final result the processing time is greatly reduced. The proposed scheme is implemented on C6678 DSP and reaches 2.22 GFLOPS. The performances achieved are 7.4, 3.83 and 2 times faster than standard Givens, Texas Instrument optimized QRD and state of the art, respectively. © 2017 Association for Computing Machinery.

关键词： Compressed sensing

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Architectural Support for Medical imaging

Architectural Support for Medical Imaging

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作者： Sampson, Richard A., ii University of Michigan

学位级别：Ph.D.

Advancements in medical imaging research are continuously providing doctors with better diagnostic information, removing the need for unnecessary surgeries and increasing accuracy in predicting life-threatening conditions. However, newly developed techniques are currently limited by the capabilities of existing computer hardware, restricting them to expensive, custom-designed machines that only the largest hospital systems can afford or even worse, precluding them entirely. Many of these issues are due to existing hardware being ill-suited for these types of algorithms and not designed with medical imaging in mind. In this thesis we discuss our efforts to motivate and democratize architectural support for advanced medical imaging tasks with MIRAQLE, a medical image reconstruction benchmark suite. In particular, MIRAQLE focuses on advanced image reconstruction techniques for 3D ultrasound, low-dose x-ray CT, and dynamic MRI. For each imaging modality we provide a detailed background and parallel implementations to enable future hardware development. In addition to providing baseline algorithms for these workloads, we also develop a unique analysis tool that provides image quality feedback for each simulation. This allows hardware designers to explore acceptable image quality trade-offs in algorithm-hardware co-design, potentially allowing for even more efficient solutions than hardware innovations alone could provide. We also motivate the need for such tools by discussing Sonic Millip3De, our low-power, highly parallel hardware for 3D ultrasound. Using Sonic Millip3De, we illustrate the orders-of-magnitude power efficiency improvement that better medical imaging hardware can provide, especially when developed with a hardware-software co-design. We also show validation of the design using a scaled-down FPGA proof-of-concept and discuss our further refinement of the hardware to support a wider range of applications and produce higher frame rates. Overall, with this the

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Architectural Support for Medical imaging. 1

Architectural Support for Medical Imaging. 1

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作者： Sampson, Richard A., ii.0 University of Michigan.bComputer Science and Engineering.0

学位级别：Ph.D.

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Reproducible, Accurately Rounded and Efficient BLAS 22nd

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22nd International European Conference on parallel and Distributed Computing (Euro-Par)

作者： Chohra, Chemseddine Langlois, Philippe Parello, David Univ Perpignan Digits Architectures & Logiciels Informat Via Domitia Perpignan 66860 France Univ Montpellier II Lab Informat Robot & Microelect Montpellier UMR 5506 Montpellier 34095 France CNRS Paris France

ISBN: (纸本)9783319589435;9783319589428

Numerical reproducibility failures rise in parallel computation because floating-point summation is non-associative. Massively parallel and optimized executions dynamically modify the floating-point operation order. Hence, numerical results may change from one run to another. We propose to ensure reproducibility by extending as far as possible the IEEE-754 correct rounding property to larger operation sequences. We introduce our RARE-BLAS (Reproducible, Accurately Rounded and Efficient BLAS) that benefits from recent accurate and efficient summation algorithms. Solutions for level 1 (asum, dot and nrm2) and level 2 (gemv) routines are presented. Their performance is studied compared to the Intel MKL library and other existing reproducible algorithms. For both shared and distributed memory parallel systems, we exhibit an extra-cost of 2x in the worst case scenario, which is satisfying for a wide range of applications. For Intel xeon Phi accelerator a larger extra-cost (4x to 6x) is observed, which is still helpful at least for debugging and validation steps.

关键词： Digital arithmetic

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parallel MLEM algorithm using GPU 14

Parallel MLEM algorithm using GPU

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14th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2017

作者： Valencia-Perez, T.A. Facultad de Ciencias Físico-Matemáticas Benemérita Universidad Autónoma de Puebla Puebla Mexico

ISBN: (纸本)9781538634059

The Computed Tomography (CT) is a imaging method based on x-rays to obtain cross-sectional images from an object. It is a widely used method in several areas, such as medicine, archeology or material sciences. Tomographic reconstruction techniques, use the projections of images from multiple directions. There are several algorithms for this purpose but can be classified according to their reconstruction method in two main classes: analytical and iterative methods. Within the analytical methods the Filtered Back Projection algorithm (FBP) is the most popular and the most used. As these algorithms pose a compromise between accuracy and computing time, its results can show some weakness. Therefore it necessary to look for alternative methods of tomographic image reconstruction. We propose an implementation of the Maximum Likelihood Estimation Method algorithm (MLEM) with the distribution of the load of numerical calculations in the NVIDIA GPU (Graphics processing Unit by Nvidia Corporation) using the Compute Unified Device Architecture (CUDA). The MLEM algorithm used for reconstruct tomographic images handle huge matrices whose computational cost is reflected in the performance of the algorithm, then two types of parallel algorithms are presented, the first, MLEM CUDA, with the standard storage format and the second, MLEM CRS, with the compressed row Storage (CRS) format. We present a comparison of the calculation time between the serial algorithm employing MATrix LABoratory (MATLAB) and parallel algorithms and metrics to determine the quality of the reconstructed image. The metrics were made using Open Source Computer Vision Library (OpenCV). Finally, reduced times in the process of image reconstruction have several potential applications across diverse areas of sciences. © 2017 IEEE.

关键词： Graphics processing unit

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A novel GPU-based sonar simulator for real-time applications

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COMPUTERS & GRAPHICS-UK 2017年 68卷 66-76页

作者： Cerqueira, Romulo Trocoli, Tiago Neves, Gustavo Joyeux, Sylvain Albiez, Jan Oliveira, Luciano Brazilian Inst Robot SENAI CIMATEC 1845 Orlando Gomes Ave BR-41650010 Salvador BA Brazil DFKI GmbH Robot Innovat Ctr Robert Hooke Str 1 D-28359 Bremen Germany Univ Fed Bahia Intelligent Vis Res IVIS Lab 2 Prof Aristides Novis St BR-40210630 Salvador BA Brazil

Mainly when applied in the underwater environment, sonar simulation requires great computational effort due to the complexity of acoustic physics. Simulation of sonar operation allows evaluating algorithms and control systems without going to the real underwater environment;that reduces the costs and risks of in-field experiments. This paper tackles with the problem of real-time underwater imaging sonar simulation by using the OpenGL shading language chain on GPU. Our proposed system is able to simulate two main types of acoustic devices: mechanical scanning imaging sonars and forward-looking sonars. The underwater scenario simulation is performed based on three frameworks: (i) OpenSceneGraph reproduces the ocean visual effects, (ii) Gazebo deals with physical forces, and (iii) the Robot Construction Kit controls the sonar in underwater environments. Our system exploits the rasterization pipeline in order to simulate the sonar devices, which are simulated by means of three parameters: the pulse distance, the echo intensity and the sonar field-of-view, being all calculated over observable objects shapes in the 3D rendered scene. Sonar-intrinsic operational parameters, speckle noise and object material properties are also considered as part of the acoustic image. Our evaluation demonstrated that the proposed system is able to operate close to or faster than the real-world devices. Also, our method generates visually realistic sonar images when compared with real-world sonar images of the same scenes. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Simulated sensor data Sonar imaging GPU-based processing Robot construction kit (Rock) Underwater robotics

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Simulation of continuously logical base cells (CL BC) with advanced functions for analog-to-digital converters and image processors 2

Simulation of continuously logical base cells (CL BC) with a...

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Conference on Emerging imaging and Sensing Technologies for Security and Defence ii

作者： Krasilenko, Vladimir G. Lazarev, Alexander A. Nikitovich, Diana V. Vinnitsa Social Econ Inst 86-131 Kelestskaya Str UA-21021 Vinnitsa Ukraine Vinnitsa Natl Tech Univ Vinnitsa Ukraine

ISBN: (数字)9781510613416

ISBN: (纸本)9781510613416;9781510613409

The paper considers results of design and modeling of continuously logical base cells (CL BC) based on current mirrors (CM) with functions of preliminary analogue and subsequent analogue-digital processing for creating sensor multichannel analog-to-digital converters (SMC ADCs) and image processors (IP). For such with vector or matrix parallel inputs-outputs IP and SMC ADCs it is needed active basic photosensitive cells with an extended electronic circuit, which are considered in paper. Such basic cells and ADCs based on them have a number of advantages: high speed and reliability, simplicity, small power consumption, high integration level for linear and matrix structures. We show design of the CL BC and ADC of photocurrents and their various possible implementations and its simulations. We consider CL BC for methods of selection and rank preprocessing and linear array of ADCs with conversion to binary codes and Gray codes. In contrast to our previous works here we will dwell more on analogue preprocessing schemes for signals of neighboring cells. Let us show how the introduction of simple nodes based on current mirrors extends the range of functions performed by the image processor. Each channel of the structure consists of several digital-analog cells (DC) on 15-35 CMOS. The amount of DC does not exceed the number of digits of the formed code, and for an iteration type, only one cell of DC, complemented by the device of selection and holding (SHD), is required. One channel of ADC with iteration is based on one DC-(G) and SHD, and it has only 35 CMOS transistors. In such ADCs easily parallel code can be realized and also serial-parallel output code. The circuits and simulation results of their design with OrCAD are shown. The supply voltage of the DC is 1.8 divided by 3.3V, the range of an input photocurrent is 0.1 divided by 24 mu A, the transformation time is 20 divided by 30nS at 6-8 bit binary or Gray codes. The general power consumption of the ADC with iterat

关键词： continuous logical ADC image processor multi-channel sensor systems current mirror equivalence (non-equivalence) functions methods of selection and rank preprocessing

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parallel processing over Spatial-Temporal Datasets from Geo, Bio, Climate and Social Science Communities: A Research Roadmap 6

Parallel Processing over Spatial-Temporal Datasets from Geo,...

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IEEE 6th International Congress on Big Data (BigData Congress)

作者： Prasad, Sushil K. Aghajarian, Danial McDermott, Michael Shah, Dhara Mokbel, Mohamed Puri, Satish Rey, Sergio J. Shekhar, Shashi xe, Yiqun Vatsavai, Ranga Raju Wang, Fusheng Liang, Yanhui Vo, Hoang Wang, Shaowen Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Univ Minnesota Comp Sci & Engn Minneapolis MN 55455 USA Marquette Univ Math Stat & Comp Sci Milwaukee WI 53233 USA Univ Calif Riverside Ctr Spatial Sci Riverside CA 92521 USA Univ Minnesota Dept Comp Sci Minneapolis MN 55455 USA North Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA SUNY Stony Brook Biomed Informat Dept Stony Brook NY 11794 USA Univ Illinois Geog & Geog Informat Sci Urbana IL USA

ISBN: (纸本)9781538619964

This vision paper reviews the current state-of art and lays out emerging research challenges in parallel processing of spatial-temporal large datasets relevant to a variety of scientific communities. The spatio-temporal data, whether captured through remote sensors (global earth observations), ground and ocean sensors (e.g., soil moisture sensors, buoys), social media and hand-held, traffic-related sensors and cameras, medical imaging (e.g., MRI), or large scale simulations (e.g., climate) have always been "big." A common thread among all these big collections of datasets is that they are spatial and temporal. processing and analyzing these datasets requires high-performance computing (HPC) infrastructures. Various agencies, scientific communities and increasingly the society at large rely on spatial data management, analysis, and spatial data mining to gain insights and produce actionable plans. Therefore, an ecosystem of integrated and reliable software infrastructure is required for spatial temporal big data management and analysis that will serve as crucial tools for solving a wide set of research problems from different scientific and engineering areas and to empower users with next-generation tools. This vision requires a multidisciplinary effort to significantly advance domain research and have a broad impact on the society. The areas of research discussed in this paper include (i) spatial data mining, (ii) data analytics over remote sensing data, (iii) processing medical images, (iv) spatial econometrics analyses, (v) Map-Reduce based systems for spatial computation and visualization, (vi) CyberGIS systems, and (vii) foundational parallel algorithms and data structures for polygonal datasets, and why HPC infrastructures, including harnessing graphics accelerators, are needed for time-critical applications.

关键词： High performance computing Spatial data mining Remote sensing data Medical images Spatial econometrics Map-reduce systems CyberGIS parallel algorithms and data structures

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