Simulation and experimental studies of three-dimensional (3D) image reconstruction from insufficient sampling data based on compressed-sensing theory for potential applications to dental cone-beam CT

Je, Uikyu; Lee, M.S.; Cho, H.S.; Hong, Dae-Ki; Park, Y.O.; Park, Chulkyu; Choi, Seokjin; Woo, Tae Ho

doi:10.1016/j.nima.2014.12.079

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…Research continues in this field, for both medical imaging and mathematical data processing. For example, a new method called compressed sensing (CS) has recently been proven [Je et al (2014)] which suggests that far fewer projections can be used to create images of a resolution on par with images produced by current methods. The CS method works on the basis of optimising signals that are sparse using data about how sparse the signal is.…”

Section: Discussionmentioning

confidence: 99%

The use of ionising radiation to image nuclear fuel: A review

Parker

Joyce

2015

Progress in Nuclear Energy

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a b s t r a c tImaging of nuclear fuel using radiation has been carried out for decades for a variety of reasons. Two important reasons are Physical Invertory Verification (PIV) and Quality Assurance (QA). The work covered in this review focuses on the imaging of nuclear fuel using ionising radiation. The fuels investigated are both fresh and spent, composed of assorted materials, and in various physical forms. The radiations used to characterise the nuclear fuel include g, a, b, muons, neutrons and X-rays. The research covered in this review, spans the past four decades and show how the technology has developed over that time. The advancement of computing technology has greatly helped with the progression of the images that are produced. The field began with 2D images in black and white showing the density profiles of g rays from within an object, culminating in 2013 when a pebble bed fuel element was reproduced in 3D showing each 0.5 mm UO 2 globule within it. With the ever increasing computing technology available to the industry, this can only mean an increase in the rate of development of imaging technologies like those covered in this review.

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Section: Discussionmentioning

confidence: 99%

The use of ionising radiation to image nuclear fuel: A review

Parker

Joyce

2015

Progress in Nuclear Energy

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“…a b theory for solving inverse problems, which exploits the sparsity of the image with substantially high accuracy. Details on the mathematical description of the CS-based framework can be found elsewhere in our previous papers [12,13]. We implemented a CSbased reconstruction algorithm for the proposed geometry and performed a systematic experiment to investigate the imaging characteristics.…”

Section: Methodsmentioning

confidence: 99%

Experimental study on the 3D image reconstruction in a truncated Archimedean-like spiral geometry with a long-rectangular detector and its image characteristics

Hong

Cho

et al. 2015

Optics Communications

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“…The convex optimazation problem can be solved efficiently using the accelerated gradient-projection Barzilai-Borwein formulation [12]. More descriptions of the CS-based CT reconstruction process can be found in our previous paper [7].…”

Section: Proposed Sparse-view Vmct Processmentioning

confidence: 99%

“…Thus, advanced algorithms for sparse-view CT reconstruction need to be developed. In a previous study [7], we investigated dental sparse-view CT reconstruction using a compressed-sensing (CS) algorithm showing high image quality. The key to the success of the CS scheme is the sparsity of the signal under study, and it has been demonstrated in literature to be capable of yielding very high accurate reconstruction even under imaging conditions such as sparse-view and limited-angle scan as in digital tomosynthesis [8,9].…”

Section: Introductionmentioning

confidence: 99%

A projection-based sparse-view virtual monochromatic computed tomography method based on a compressed-sensing algorithm

et al. 2019

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A: Computed tomography (CT) images obtained at different monochromatic X-ray beam energies can be synthesized from conventional dual-energy CT scans. This approach to synthesizing monochromatic CT images is based on basis material decomposition and the knowledge of attenuation of basis materials. The main benefits of virtual monochromatic CT (VMCT) images include reduction of beam-hardening artifacts and provision of accurate atteuation measurements. Despite the VMCT's benefits, main concerns in the use of VMCT in clinics may be high radiation dose the patient is exposed to. In this study, we investigated a projection-based sparse-view VMCT method in an attempt to overcome these difficulties. We performed a computational simulation and evaluated the feasibility of using the VMCT method in sparse-view CT. Two polychromatic data sets of 90 projections, far less than what is required by the Nyquist sampling theory, were simulated at 80 kV p and 140 kV p and used to synthesize VMCT images at a monochromatic energy range of 40-140 keV. VMCT image characteristics were quantitatively evaluated in terms of intensity profile, the contrast-to-noise ratio, and the signal-to-noise ratio. Our results indicate that the CS-based algorithm produced high-quality sparse-view CT images, and thereby the proposed VMCT method yielded CT image results of improved beam-hardening artifacts and quantitative measurements. K: Image reconstruction in medical imaging; Simulation methods and programs 1Corresponding author.

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Simulation and experimental studies of three-dimensional (3D) image reconstruction from insufficient sampling data based on compressed-sensing theory for potential applications to dental cone-beam CT

Cited by 9 publications

References 7 publications

The use of ionising radiation to image nuclear fuel: A review

The use of ionising radiation to image nuclear fuel: A review

Experimental study on the 3D image reconstruction in a truncated Archimedean-like spiral geometry with a long-rectangular detector and its image characteristics

A projection-based sparse-view virtual monochromatic computed tomography method based on a compressed-sensing algorithm

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