Towards a Heterogeneous Medical Image Registration Acceleration Platform

Plishker, William; Dandekar, Omkar; Bhattacharyya, Shuvra S.; Shekhar, Raj

doi:10.1109/biocas.2007.4463351

Cited by 9 publications

(8 citation statements)

References 16 publications

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“…If we were just dealing with individual platforms, we could simply implement each acceleration technique into the code base as necessary. But since we are experimenting with combined approaches (estimated to have high performance synergy [11]), we start by expressing the different types of parallelism in a structured fashion (see Fig. 1).…”

Section: A Image Registration Implementation Frameworkmentioning

confidence: 99%

Towards systematic exploration of tradeoffs for medical image registration on heterogeneous platforms

Plishker

Dandekar

Bhattacharyya

et al. 2008

2008 IEEE Biomedical Circuits and Systems Conference

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Abstract-For the past decade, improving the performance and accuracy of medical image registration has been a driving force of innovation in medical imaging. The ultimate goal of accurate, robust, real-time image registration will enhance diagnoses of patients and enable new image-guided intervention techniques. With such a computationally intensive and multifaceted problem, improvements have been found in high performance platforms such as graphics processors (GPUs) and general purpose clusters, but there has yet to be a solution fast enough and effective enough to gain widespread clinical use. In this study, we examine the differences in accuracy and speed of implementations of the same image registration algorithm on a general purpose uniprocessor, a GPU, and a cluster of GPUs. We utilize a novel domain specific framework that allows us to simultaneously exploit parallelism on a heterogeneous platform. Using a set of representative images, we examine implementations with speedups of up to two orders of magnitude and accuracy varying from sub-millimeter to 2.6 millimeters of average error.

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Section: A Image Registration Implementation Frameworkmentioning

confidence: 99%

Towards systematic exploration of tradeoffs for medical image registration on heterogeneous platforms

Plishker

Dandekar

Bhattacharyya

et al. 2008

2008 IEEE Biomedical Circuits and Systems Conference

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“…This multi-FPGA implementation will likely provide near-linear speedup. This architecture can also be incorporated in a broader, heterogeneous computing framework such as the one described by Plishker et al [121]. All these strategies, in combination, can further reduce the execution time of deformable image registration and ultimately achieve near-real time performance for its seamless integration into IGI applications.…”

Section: Future Workmentioning

confidence: 99%

“…Although, this implementation offered moderate speedup for rigid registration, the performance achieved for 3D deformable image registration was poor. Plishker et al [121] have employed GPUs for applying transformations to images during rigid registration. This implementation achieved 3-fold improvement in execution time over a CPU-based implementation.…”

Section: Graphics Processor (Gpu)-based Approachesmentioning

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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“…GPU architectures are able to achieve such a dense computational power through simple processing elements, structured memory resources, and restricted communication channels. Indeed, graphics processors have proved effective for gradient flow based registration [27], for 3D non-rigid registration using sum-of-squared differences [29], and even examined for heterogeneous platforms tailored to image registration [30]. But to properly exploit the GPU architecture, application parallelism must match the concurrency in the GPU's pixel processing datapaths while accommodating computation, memory, and IO restrictions.…”

Section: Fpga Technologymentioning

confidence: 99%

Model-based mapping of reconfigurable image registration on FPGA platforms

Sen

Hemaraj

Plishker

et al. 2008

J Real-Time Image Proc

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Image registration is a computationally intensive application in the medical imaging domain that places stringent requirements on performance and memory management efficiency. This paper develops techniques for mapping rigid image registration applications onto configurable hardware under real-time performance constraints. Building on the framework of homogeneous parameterized dataflow, which provides an effective formal model of design and analysis of hardware and software for signal processing applications, we develop novel methods for representing and exploring the hardware design space when mapping image registration algorithms onto configurable hardware. Our techniques result in an efficient framework for trading off performance and configurable hardware resource usage based on the constraints of a given application. Based on trends that we have observed when applying these techniques, we also present a novel architecture that enables dynamically-reconfigurable image registration. This proposed architecture has the ability to tune its parallel processing structure adaptively based on relevant characteristics of the input images.

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Towards a Heterogeneous Medical Image Registration Acceleration Platform

Cited by 9 publications

References 16 publications

Towards systematic exploration of tradeoffs for medical image registration on heterogeneous platforms

Towards systematic exploration of tradeoffs for medical image registration on heterogeneous platforms

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

Model-based mapping of reconfigurable image registration on FPGA platforms

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