GPU-based cone beam computed tomography

Noël, Peter B.; Walczak, Alan M.; Xu, Jinhui; Corso, Jason J.; Hoffmann, Kenneth R.; Schäfer, Sebastian

doi:10.1016/j.cmpb.2009.08.006

Cited by 67 publications

(52 citation statements)

References 9 publications

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“…They demonstrated that a GeForce 8800 GTX GPU achieved two times higher reconstruction performance as compared to a CBE. A similar CUDA-based approach with similar results was presented by Noël et al [18].…”

Section: Related Worksupporting

confidence: 60%

Cache-Aware GPU Optimization for Out-of-Core Cone Beam CT Reconstruction of High-Resolution Volumes

Ino

Hagihara

2016

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper proposes a cache-aware optimization method to accelerate out-of-core cone beam computed tomography reconstruction on a graphics processing unit (GPU) device. Our proposed method extends a previous method by increasing the cache hit rate so as to speed up the reconstruction of high-resolution volumes that exceed the capacity of device memory. More specifically, our approach accelerates the well-known Feldkamp-Davis-Kress algorithm by utilizing the following three strategies: (1) a loop organization strategy that identifies the best tradeoff point between the cache hit rate and the number of off-chip memory accesses; (2) a data structure that exploits high locality within a layered texture; and (3) a fully pipelined strategy for hiding file input/output (I/O) time with GPU execution and data transfer times. We implement our proposed method on NVIDIA's latest Maxwell architecture and provide tuning guidelines for adjusting the execution parameters, which include the granularity and shape of thread blocks as well as the granularity of I/O data to be streamed through the pipeline, which maximizes reconstruction performance. Our experimental results show that it took less than three minutes to reconstruct a 2048 3 -voxel volume from 1200 2048 2 -pixel projection images on a single GPU; this translates to a speedup of approximately 1.47 as compared to the previous method.

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Section: Related Worksupporting

confidence: 60%

Cache-Aware GPU Optimization for Out-of-Core Cone Beam CT Reconstruction of High-Resolution Volumes

Ino

Hagihara

2016

IEICE Trans. Inf. & Syst.

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“…A common strategy to speed up the back-projection step involves the use of graphics processing units (GPUs). In recent years several implementations have resulted in processing time reduction factors of up to 40 [9][10][11][12][13][14][15], most of them using the compute unified device architecture (CUDA). The modularity in our architecture facilitates the substitution of any module for a GPU kernel.…”

Section: Discussionmentioning

confidence: 99%

“…proposed by Feldkamp et al (FDK) [6] are still widely used for solving the 3D reconstruction task because of their straightforward implementation and computational efficiency [4]. Almost every aspect of the reconstruction process has been studied: there is literature on algorithm variations for different trajectories [7,8], optimizations using graphic processing units (GPUs) [9][10][11][12][13][14][15], strategies to reduce cone beam artifacts [16,17], study of consistency conditions [18], optimization of the back-projection step [19], etc. However, in a real practical system, the implementation of a reconstruction algorithm core such as FDK is just an initial step of the process, and there 1…”

Section: Introductionmentioning

confidence: 99%

Software architecture for multi-bed FDK-based reconstruction in X-ray CT scanners

Abella

Vaquero

Sisniega

et al. 2012

Computer Methods and Programs in Biomedicine

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Cone-beam t r a c tMost small-animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory. Image reconstruction in these systems is usually performed by approximate methods based on the algorithm proposed by Feldkamp et al. (FDK). Besides the implementation of the reconstruction algorithm itself, in order to design a real system it is necessary to take into account numerous issues so as to obtain the best quality images from the acquired data. This work presents a comprehensive, novel software architecture for small-animal CT scanners based on cone-beam geometry with circular scanning trajectory. The proposed architecture covers all the steps from the system calibration to the volume reconstruction and conversion into Hounsfield units. It includes an efficient implementation of an FDK-based reconstruction algorithm that takes advantage of system symmetries and allows for parallel reconstruction using a multiprocessor computer. Strategies for calibration and artifact correction are discussed to justify the strategies adopted. New procedures for multi-bed misalignment, beam-hardening, and Housfield units calibration are proposed. Experiments with phantoms and real data showed the suitability of the proposed software architecture for an X-ray small animal CT based on cone-beam geometry. IntroductionMany small animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory [1][2][3][4]. This configuration presents advantages over other alternatives used in clinical and preclinical applications: reduction of acquisition time, large axial field of view (FOV) without geometrical distortions, and optimization of radiated dose [5]. proposed by Feldkamp et al. (FDK) [6] are still widely used for solving the 3D reconstruction task because of their straightforward implementation and computational efficiency [4]. Almost every aspect of the reconstruction process has been studied: there is literature on algorithm variations for different trajectories [7,8], optimizations using graphic processing units (GPUs) [9][10][11][12][13][14][15], strategies to reduce cone beam artifacts [16,17], study of consistency conditions [18], optimization of the back-projection step [19], etc. However, in a real practical system, the implementation of a reconstruction algorithm core such as FDK is just an initial step of the process, and there 1

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“…Both solved the problem by dividing the data into smaller blocks. Since these early works, the field of GPU-accelerated CT reconstruction has exploded (Noel et al, 2010;Okitsu et al, 2010;Xu et al, 2010a;Vintache et al, 2010). Recent examples include several papers on using multiple GPUs to further accelerate reconstruction (Jang et al, 2009;Liria et al, 2012;Zhang et al, 2012;Zhu et al, 2012), including the incorporation of scatter correction into the reconstruction process (Sisniega et al, 2011), SART based reconstruction combined with motion compensation (Pang et al, 2011), CT reconstruction with OpenCL ) and an up-to-date and thorough comparison of different hardware implementations (CPU, GPU, FPGA and the Cell Broadband Architecture) of FBP (Scherl et al, 2012).…”

Section: Ctmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

344

198

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Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.

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GPU-based cone beam computed tomography

Cited by 67 publications

References 9 publications

Cache-Aware GPU Optimization for Out-of-Core Cone Beam CT Reconstruction of High-Resolution Volumes

Cache-Aware GPU Optimization for Out-of-Core Cone Beam CT Reconstruction of High-Resolution Volumes

Software architecture for multi-bed FDK-based reconstruction in X-ray CT scanners

Medical image processing on the GPU – Past, present and future

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