GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration

Sharp, G; Kandasamy, Nagarajan; Singh, Harbinder; Folkert, Michael R.

doi:10.1088/0031-9155/52/19/003

Cited by 178 publications

(152 citation statements)

References 23 publications

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“…7 All these factors in the measurement and reconstruction process introduce artefacts into the cone beam datasets. 12,29,30 The technique to determine the MTFs for CT or CBCT scanners resembles the technique used for X-ray detectors. Initially, a measurement must be performed to obtain either the point spread function or the LSF.…”

Section: Resolution Of the Two-dimensional Detectormentioning

confidence: 99%

Spatial resolution in CBCT machines for dental/maxillofacial applications—what do we know today?

Brüllmann

Schulze²

2015

Dentomaxillofacial Radiology

152

127

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Spatial resolution is one of the most important parameters objectively defining image quality, particularly in dental imaging, where fine details often have to be depicted. Here, we review the current status on assessment parameters for spatial resolution and on published data regarding spatial resolution in CBCT images. The current concepts of visual [line-pair (lp) measurements] and automated [modulation transfer function (MTF)] assessment of spatial resolution in CBCT images are summarized and reviewed. Published measurement data on spatial resolution in CBCT are evaluated and analysed. Effective (i.e. actual) spatial resolution available in CBCT images is being influenced by the two-dimensional detector, the threedimensional reconstruction process, patient movement during the scan and various other parameters. In the literature, the values range between 0.6 and 2.8 lp mm 21 (visual assessment; median, 1.7 lp mm ). Spatial resolution of CBCT images is approximately one order of magnitude lower than that of intraoral radiographs. Considering movement, scatter effects and other influences in real-world scans of living patients, a realistic spatial resolution of just above 1 lp mm 21 could be expected.

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Section: Resolution Of the Two-dimensional Detectormentioning

confidence: 99%

Spatial resolution in CBCT machines for dental/maxillofacial applications—what do we know today?

Brüllmann

Schulze²

2015

Dentomaxillofacial Radiology

152

127

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“…To yield a satisfactory efficiency, key components of FBS, including FDK reconstruction F, 54 forward projection calculation P, 55,56 and DISC, 52 have been implemented under CUDA programming environment on a GPU platform with an NVIDIA GTX590 card.…”

Section: A3 Implementationmentioning

confidence: 99%

A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging

et al. 2014

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Purpose: 4D cone beam CT (4D-CBCT) has been utilized in radiation therapy to provide 4D image guidance in lung and upper abdomen area. However, clinical application of 4D-CBCT is currently limited due to the long scan time and low image quality. The purpose of this paper is to develop a new 4D-CBCT reconstruction method that restores volumetric images based on the 1-min scan data acquired with a standard 3D-CBCT protocol. Methods: The model optimizes a deformation vector field that deforms a patient-specific planning CT (p-CT), so that the calculated 4D-CBCT projections match measurements. A forward-backward splitting (FBS) method is invented to solve the optimization problem. It splits the original problem into two well-studied subproblems, i.e., image reconstruction and deformable image registration. By iteratively solving the two subproblems, FBS gradually yields correct deformation information, while maintaining high image quality. The whole workflow is implemented on a graphic-processing-unit to improve efficiency. Comprehensive evaluations have been conducted on a moving phantom and three real patient cases regarding the accuracy and quality of the reconstructed images, as well as the algorithm robustness and efficiency. Results: The proposed algorithm reconstructs 4D-CBCT images from highly under-sampled projection data acquired with 1-min scans. Regarding the anatomical structure location accuracy, 0.204 mm average differences and 0.484 mm maximum difference are found for the phantom case, and the maximum differences of 0.3-0.5 mm for patients 1-3 are observed. As for the image quality, intensity errors below 5 and 20 HU compared to the planning CT are achieved for the phantom and the patient cases, respectively. Signal-noise-ratio values are improved by 12.74 and 5.12 times compared to results from FDK algorithm using the 1-min data and 4-min data, respectively. The computation time of the algorithm on a NVIDIA GTX590 card is 1-1.5 min per phase. Conclusions: High-quality 4D-CBCT imaging based on the clinically standard 1-min 3D CBCT scanning protocol is feasible via the proposed hybrid reconstruction algorithm.

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“…In 2007, the work by Xu and colleagues was further extended to real-time reconstruction (Xu and Mueller, 2007), such that interactive 3D image generation could be performed for flat-panel x-ray detectors and C-arm gantries. During the same year, Scherl et al (2007) were one of the first to use the CUDA programming language for FBP, while Yan et al (2008) still used OpenGL and Sharp et al (2007) used the Brook programming environment. Zhao et al (2009) focused on the problem of reconstruction of large volumes (e.g.…”

Section: Ctmentioning

confidence: 99%

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

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

344

193

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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 streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration

Cited by 178 publications

References 23 publications

Spatial resolution in CBCT machines for dental/maxillofacial applications—what do we know today?

Spatial resolution in CBCT machines for dental/maxillofacial applications—what do we know today?

A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging

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

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