A taxonomy for medical image registration acceleration techniques

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

doi:10.1109/lssa.2007.4400909

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…To address this aspect many researches have independently attempted to accelerate intensity-based image registration. Classification of these acceleration attempts has been reported by Plishker et al [114]. We briefly summarize these attempts here.…”

Section: Acceleration Of Image Registrationmentioning

confidence: 84%

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: Acceleration Of Image Registrationmentioning

confidence: 84%

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

Dandekar¹

2008

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“…Based on these results, we discuss the possibilities and the challenges of combining acceleration approaches that utilize complementary types of parallelism. This article expands on preliminary work on this subject, which is presented in [1] and [2].…”

Section: In This Issue Of Ieee Signal Processing Magazinementioning

confidence: 90%

Utilizing Hierarchical Multiprocessing for Medical Image Registration

Plishker

Dandekar

Bhattacharyya

et al. 2010

IEEE Signal Process. Mag.

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dvances in medical imaging technologies have enabled medical diagnoses and procedures that were simply not possible a decade ago. The accelerating speed of acquisition and the increasing resolution of images have given doctors more information, which is taken less invasively about their patients. However, because of the multitude of imaging modalities [e.g., computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI), and ultrasound (US)] and the sheer volume of data being acquired, utilizing this new data effectively has become problematic. One way to tap into the potential of this raw data is to merge these images into one integrated view through a procedure called image registration. COMPUTE-ENABLED MEDICINEFor the past decade, improving performance and accuracy has been a driving force of innovation in automated medical image registration. 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 processing units (GPUs) and general-purpose multicore systems, but there has yet to be a solution fast enough and effective enough to gain widespread clinical use.

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“…Image registration algorithms have the potential to be mapped onto a variety of parallel platforms [25] for efficient execution. Clusters accelerate highly coarse forms of parallelism [26] and single chip multiprocessors can exploit coarse parallelism and often vector level parallelism.…”

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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A taxonomy for medical image registration acceleration techniques

Cited by 8 publications

References 16 publications

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

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

Utilizing Hierarchical Multiprocessing for Medical Image Registration

Model-based mapping of reconfigurable image registration on FPGA platforms

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