FPGA-based real-time anisotropic diffusion filtering of 3D ultrasound images

Castro-Pareja, Carlos R.; Dandekar, Omkar; Shekhar, Raj

doi:10.1117/12.586198

Cited by 8 publications

(11 citation statements)

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“…2) N − ; where N is the size of anisotropic diffusion filtering kernel, the corresponding size of the embedded Gaussian filtering kernel is ( 2) N − , and can be implemented using only 10 multipliers (as opposed to 125) as we reported previously [143]. For this kernel, each individual slice ( 5 5 × plane of the kernel) has six isodistance regions and the whole 3D kernel has 10.…”

Section: Embedded Gaussian Filtering Modulementioning

confidence: 99%

High-Performance 3D Image Processing Architectures for Image-Guided Interventions

Dandekar¹

2008

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Minimally invasive image-guided interventions (IGIs) are time and cost efficient, minimize unintended damage to healthy tissues, and lead to faster patient recovery. Advanced three-dimensional (3D) image processing is a critical need for navigation during IGIs. However, achieving on-demand performance, as required by IGIs, for these image processing operations using software-only implementations is challenging because of the sheer size of the 3D images, and memory and compute intensive nature of the operations. This dissertation, therefore, is geared toward developing high-performance 3D image processing architectures, which will enable improved intraprocedural visualization and navigation capabilities during IGIs.In this dissertation we present an architecture for real-time implementation of 3D filtering operations that are commonly employed for preprocessing of medical images. This architecture is approximately two orders of magnitude faster than corresponding software implementations and is capable of processing 3D medical images at their acquisition speeds. Report Documentation PageCombining complementary information through registration between pre-and intraprocedural images is a fundamental need in the IGI workflow. Intensity-based deformable registration, which is completely automatic and locally accurate, is a promising approach to achieve this alignment. These algorithms, however, are extremely compute intensive, which has prevented their clinical use. We present an FPGA-based architecture for accelerated implementation of intensity-based deformable image registration. This high-performance architecture achieves over an order of magnitude speedup when compared with a corresponding software implementation and reduces the execution time of deformable registration from hours to minutes while offering comparable image registration accuracy.Furthermore, we present a framework for multiobjective optimization of finite-precision implementations of signal processing algorithms that takes into account multiple conflicting objectives such as implementation accuracy and hardware resource consumption. The evaluation that we have performed in the context of FPGA-based image registration demonstrates that such an analysis can be used to enhance automated hardware design processes, and efficiently identify a system configuration that meets given design constraints. In addition, we also outline two novel clinical applications that can directly benefit from these developments and demonstrate the feasibility of our approach in the context of these applications. These advances will ultimately enable integration of 3D image processing into clinical workflow.

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Section: Embedded Gaussian Filtering Modulementioning

confidence: 99%

High-Performance 3D Image Processing Architectures for Image-Guided Interventions

Dandekar¹

2008

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“…This task can be done with an FPGA-based architecture that allows performing anisotropic diffusion filtering of 3D images at acquisition rates coupled with a multigrid algorithm. This enables the use of filtering techniques in realtime applications, such as visualization, registration and volume rendering 13 . Therefore, in this sector, the application of FPGAs permits to deal with data at the rates needed.…”

Section: Ib Specific Purpose Hardware (Fpgas) Its Current Industrimentioning

confidence: 99%

“…• The output of the module is a vector that represents the value of the flux at point i + 1/2 as expressed in equation (13).…”

Section: Iiib3 Step 23: Development Of Specific Hardware For the mentioning

confidence: 99%

A Methodology for CFD Acceleration Through Reconfigurable Hardware

Carreras

Cafiarena

Molina

et al. 2008

46th AIAA Aerospace Sciences Meeting and Exhibit

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The long computation times required to simulate complete aircraft configurations remain as the main bottleneck in the design flow of new structures for the aeronautics industry. In this paper, the novel application of specific hardware (FPGAs) in conjunction with conventional processors to accelerate CFD is explored in detail. First, some general facts about application-specific hardware are presented, placing the focus on the feasibility of the development of hardware modules (FPGAs based) for the acceleration of most time-consuming algorithms in aeronautics analysis. Then, a practical methodology for developing an FPGA-based computing solution for the quasi 1D Euler equations is applied to the Sod's 'Shock Tube' problem. Results comparing CPU-based and FPGA-based solutions are presented, showing that speedups around two orders of magnitude can be expected from the FPGA-based implementation. Finally, some conclusions about this novel approach are drawn.

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“…We, therefore, determine this by performing multiobjective optimization using the FWL of each parameter as a design variable. In our experiments, we used the design range of [1,32] bits for FWLs of all the parameters. The optimization framework can support different wordlength ranges for different parameters, which can be used to account for additional design constraints, such as, for example, certain kinds of constraints imposed by third-party IP.…”

Section: Design Parametersmentioning

confidence: 99%

“…This is particularly necessary in applications where multiple kernels share data and feed results to each other. For example, in medical imaging it has been shown that both image preprocessing [1][2][3] and image registration [4][5][6] can achieve high levels of speedup through hardware acceleration. To maximize the performance of an application and to optimize the fabric resource utilization, the kernels must be designed to meet their application requirements while balancing their resource consumption on the fabric.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization for Reconfigurable Implementation of Medical Image Registration

Dandekar

Plishker

Bhattacharyya

et al. 2008

International Journal of Reconfigurable Computing

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In real-time signal processing, a single application often has multiple computationally intensive kernels that can benefit from acceleration using custom or reconfigurable hardware platforms, such as field-programmable gate arrays (FPGAs). For adaptive utilization of resources at run time, FPGAs with capabilities for dynamic reconfiguration are emerging. In this context, it is useful for designers to derive sets of efficient configurations that trade off application performance with fabric resources. Such sets can be maintained at run time so that the best available design tradeoff is used. Finding a single, optimized configuration is difficult, and generating a family of optimized configurations suitable for different run-time scenarios is even more challenging. We present a novel multiobjective wordlength optimization strategy developed through FPGA-based implementation of a representative computationally intensive image processing application: medical image registration. Tradeoffs between FPGA resources and implementation accuracy are explored, and Pareto-optimized wordlength configurations are systematically identified. We also compare search methods for finding Pareto-optimized design configurations and demonstrate the applicability of search based on evolutionary techniques for identifying superior multiobjective tradeoff curves. We demonstrate feasibility of this approach in the context of FPGA-based medical image registration; however, it may be adapted to a wide range of signal processing applications.

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FPGA-based real-time anisotropic diffusion filtering of 3D ultrasound images

Cited by 8 publications

References 0 publications

High-Performance 3D Image Processing Architectures for Image-Guided Interventions

High-Performance 3D Image Processing Architectures for Image-Guided Interventions

A Methodology for CFD Acceleration Through Reconfigurable Hardware

Multiobjective Optimization for Reconfigurable Implementation of Medical Image Registration

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