Hardware/software 2D-3D backprojection on a SoPC platform

International audienceThe reduction of image reconstruction time is needed to spread the use of PET for research and routine clinical practice. In this purpose, this article presents a hardware/software architecture for the acceleration of 3D backprojection based upon an efficient 2D backprojection. This architecture has been designed in order to provide a high level of parallelism thanks to an efficient management of the memory accesses which would have been otherwise strongly slowed by the external memory. The reconstruction system is embedded in a SoPC platform (System on Programmable Chip), the new generation of reconfigurable circuit. The originality of this architecture comes from the design of a 2D Adaptative and Predictive Cache (2D-AP Cache) which has proved to be an efficient way to overcome the memory access bottleneck. Thanks to a hierarchical use of this cache, several backprojection operators can run in parallel, accelerating in this manner noteworthy the reconstruction process. This 2D reconstruction system will next be used to speed up 3D image reconstruction

Multi GPU parallelization of 3D bayesian CT algorithm and its application on real foam reonconstruction with incomplete data set

International audienceA great number of image reconstruction algorithms, based on analytical filtered backprojection, are implemented for X-ray Computed Tomography (CT) [1,2]. The limits of these methods appear when the number of projections is small, and/or not equidistributed around the object. That's the case in the context of dynamic study of fluids in foams for example, the data set are not complete due to the limited acquistion time. In this specific context, iterative algebraic methods are a solution to this lack of data. A great number of them are mainly based on least square criterion. Recently, we proposed a regularized version based on Bayesian estimation approach. The main problem that appears when using such methods as well as any iterative algebraic methods is the computation time and especially for projection and backprojection steps. In this study, first we show how we implemented some main steps of such algorithms which are the forward projection and backward backprojection steps on multi-GPU hardware, and then we show some results on real application of the 3D tomographic reconstruction of metallic foams from a small number of projections. Through this application, we also show the good quality of results as well as a significant speed up of the computation with GPU implementation (300 acceleration factor)

Multi-streaming and multi-GPU optimization for a matched pair of Projector and Backprojector

International audienceIterative reconstruction methods are used in X-ray Computed Tomography in order to improve the quality of reconstruction compared to filtered backprojection methods. However, these methods are computationally expensive due to repeated projection and backprojection operations. Among the possible pairs of projector and backprojector, the Separable Footprint (SF) pair has the advantage to be matched in order to ensure the convergence of the reconstruction algorithm. Nevertheless, this pair implies more computations compared to unmatched pairs commonly used in order to reduce the computation time. In order to speed up this pair, the projector and the backprojector can be parallelized on GPU. Following one of our previous work, in this paper, we propose a new implementation which takes benefits from the factorized calculations of the SF pair in order to increase the number of data handled by each thread. We also describe the adaptation of this implementation for multi-streaming computations. The method is tested on large volumes of size 1024 3 and 2048 3 voxels

High Speed 3D Tomography on CPU, GPU, and FPGA

12 pages; 50% d'acceptationInternational audienceBack-projection (BP) is a costly computational step in tomography image reconstruction such as positron emission tomography (PET). To reduce the computation time, this paper presents a pipelined, prefetch, and parallelized architecture for PET BP (3PA-PET). The key feature of this architecture is its original memory access strategy, masking the high latency of the external memory. Indeed, the pattern of the memory references to the data acquired hinders the processing unit. The memory access bottleneck is overcome by an efficient use of the intrinsic temporal and spatial locality of the BP algorithm. A loop reordering allows an efficient use of general purpose processor's caches, for software implementation, as well as the 3D predictive and adaptive cache (3D-AP cache), when considering hardware implementations. Parallel hardware pipelines are also efficient thanks to a hierarchical 3D-AP cache: each pipeline performs a memory reference in about one clock cycle to reach a computational throughput close to 100%. The 3PA-PET architecture is prototyped on a system on programmable chip (SoPC) to validate the system and to measure its expected performances. Time performances are compared with a desktop PC, a workstation, and a graphic processor unit (GPU)

Convolution Models with Shift-invariant kernel based on Matlab-GPU platform for Fast Acoustic Imaging

International audienceAcoustic imaging is an advanced technique for acoustic source localization and power reconstruc-tion from limited noisy measurements at microphone sensors. This technique not only involves in a forward model of acoustic propagation from sources to sensors, but also its numerical solution of an ill-posed inverse problem. Nowadays, the Bayesian inference methods in inverse methods have been widely investigated for robust acoustic imaging, but most of Bayesian methods are time-consuming, and one of the reasons is that the forward model causes heavy matrix multiplication. In this paper, we focus on the acceleration of the forward model by using a 2D-invariant convo-lution and a separable convolution respectively; For hardware acceleration, the Matlab-Graphics Processing Unit application are discussed. For method validation, we use the simulated and real data from the wind tunnel experiment in automobile industry

2D convolution model using (in)variant kernels for fast acoustic imaging

International audienceAcoustic imaging is an advanced technique for acoustic source localization and power reconstruction using limited measurements at microphone sensors. The acoustic imaging methods often involve in two aspects: one is to build up a forward model of acoustic power propagation which requires tremendous matrix multiplications due to large dimension of the power propagation matrix; the other is to solve an inverse problem which is usually ill-posed and time consuming. In this paper, our main contribution is to propose to use 2D convolution model for fast acoustic imaging. We find out that power propagation ma-trix seems to be a quasi-Symmetric Toeplitz Block Toeplitz (STBT) matrix in the far-field condition, so that the (in)variant convolution kernels (sizes and values) can be well derived from this STBT matrix. For method validation, we use simulated and real data from the wind tunnel S2A (France) experiment for acoustic imaging on vehicle surface

A robust super-resolution approach with sparsity constraint in acoustic imaging

International audienceAcoustic imaging is a standard technique for mapping acoustic source powers and positions from limited observations on microphone sensors, which often causes an ill-conditioned inverse problem. In this article, we firstly improve the forward model of acoustic power propagation by considering background noises at the sensor array, and the propagation uncertainty caused by wind tunnel effects. We then propose a robust super-resolution approach via sparsity constraint for acoustic imaging in strong background noises. The sparsity parameter is adaptively derived from the sparse distribution of source powers. The proposed approach can jointly reconstruct source powers and positions, as well as the background noise power. Our approach is compared with the conventional beamforming, deconvolution and sparse regularization methods by simulated, wind tunnel data and hybrid data respectively. It is feasible to apply the proposed approach for effectively mapping monopole sources in wind tunnel tests

An efficient variational Bayesian inference approach via Studient's-t priors for acoustic imaging in colored noises

International audienceAcoustic imaging is a powerful tool to localize and reconstruct source powers using microphone array. However, it often involves the ill-posed inversions and becomes too time-consuming to obtain high spatial resolutions. In this paper, we firstly propose a shift-invariant convolution model to approximate the forward model of acoustic power propagation. The convolution kernel is derived from the Symmetric Toepliz Block Toepliz (STBT) structure of propagation matrix. Then we propose a hierarchical Bayesian inference approach via Variational Bayesian Approximation (VBA) criterion in order to achieve robust acoustic imaging in colored background noises. For super spatial resolution and wide dynamic power range, we explore the Student's-t prior on the acoustic power distribution thanks to the sparsity and heavy tail of prior model. Colored noise distributions are also modeled by the Student's-t prior, and this does not excessively penalize large model errors as the Gaussian white prior does. Finally proposed 2D convolution model and VBA approach are validated through simulations and real data from wind tunnel compared to classical methods

Parallélisation sur GPU d'un algorithme de reconstruction 3D bayésien en tomographie X.

An important number of image reconstruction algorithms are implemented in the literature on X-ray CT (Computed Tomography) data. The main family of methods are analytical, mainly filtered backprojection which are implemented generally in medical imaging for their fast reconstruction time. The limits of these methods appear when the number of projections is small, and/or not equidistributed around the object. In this specific context, iterative algebraic methods are implemented. A great number of them are mainly based on least square criterion. We propose a regularized version of iterative algorithms to improve results. The main problem that appears when using iterative algebraic methods is the computation time and especially for projection and backprojection steps. We propose to implement some steps of the iterations on GPU hardware. We present an original method based on a Bayesian statistical method for 3D tomographic reconstructions. The main interest is to apply it in a context of non-consistent data sets, for example with a small number of projections. We show a good quality of results and a significant speed up of the calculation with GPU implementation

GPU implementation of a 3D bayesian CT algorithm and its application on real foam reconstruction

5 pagesInternational audienceA great number of image reconstruction algorithms, based on analytical filtered backprojection, are implemented for X-ray Computed Tomography (CT) [1, 3]. The limits of these methods appear when the number of projections is small, and/or not equidistributed around the object. In this specific context, iterative algebraic methods are implemented. A great number of them are mainly based on least square criterion. Recently, we proposed a regularized version based on Bayesian estimation approach. The main problem that appears when using such methods as well as any iterative algebraic methods is the computation time and especially for projection and backprojection steps. In this paper, first we show how we implemented some main steps of such algorithems which are the forward projection and backward backprojection steps on GPU hardware, and then we show some results on real application of the 3D tomographic reconstruction of metallic foams from a small number of projections. Through this application, we also show the good quality of results as well as a significant speed up of the computation with GPU implementation