• Progress in Medical Physics
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• v.26 no.1
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• pp.28-35
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• 2015

In this study, we investigated the effects of an iterative reconstruction algorithm and an automatic exposure control (AEC) technique on image quality and radiation dose through phantom experiments with coronary computed tomography (CT) angiography protocols. We scanned the AAPM CT performance phantom using 320 multi-detector-row CT. At the tube voltages of 80, 100, and 120 kVp, the scanning was repeated with two settings of the AEC technique, i.e., with the target standard deviations (SD) values of 33 (the higher tube current) and 44 (the lower tube current). The scanned projection data were reconstructed also in two ways, with the filtered back projection (FBP) and with the iterative reconstruction technique (AIDR-3D). The image quality was evaluated quantitatively with the noise standard deviation, modulation transfer function, and the contrast to noise ratio (CNR). More specifically, we analyzed the influences of selection of a tube voltage and a reconstruction algorithm on tube current modulation and consequently on radiation dose. Reduction of image noise by the iterative reconstruction algorithm compared with the FBP was revealed eminently, especially with the lower tube current protocols, i.e., it was decreased by 46% and 38%, when the AEC was established with the lower dose (the target SD=44) and the higher dose (the target SD=33), respectively. As a side effect of iterative reconstruction, the spatial resolution was decreased by a degree that could not mar the remarkable gains in terms of noise reduction. Consequently, if coronary CT angiogprahy is scanned and reconstructed using both the automatic exposure control and iterative reconstruction techniques, it is anticipated that, in comparison with a conventional acquisition method, image noise can be reduced significantly with slight decrease in spatial resolution, implying clinical advantages of radiation dose reduction, still being faithful to the ALARA principle.

• Journal of Biomedical Engineering Research
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• v.38 no.5
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• pp.219-226
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• 2017

Penalized-likelihood (PL) reconstruction methods for transmission tomography are known to provide improved image quality for reduced dose level by efficiently smoothing out noise while preserving edges. Unfortunately, however, most of the edge-preserving penalty functions used in conventional PL methods contain at least one free parameter which controls the shape of a non-quadratic penalty function to adjust the sensitivity of edge preservation. In this work, to avoid difficulties in finding a proper value of the free parameter involved in a non-quadratic penalty function, we propose a new adaptive method of space-variant smoothing with a simple quadratic penalty function. In this method, the smoothing parameter is adaptively selected for each pixel location at each iteration by using the image roughness measured by a pixel-wise standard deviation image calculated from the previous iteration. The experimental results demonstrate that our new method not only preserves edges, but also suppresses noise well in monotonic regions without requiring additional processes to select free parameters that may otherwise be included in a non-quadratic penalty function.

• Journal of Biomedical Engineering Research
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• v.35 no.5
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• pp.132-141
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• 2014

In an ordinary CT scan, a large number of projections with full field-of-view (FFOV) are necessary to reconstruct high resolution images. However, excessive x-ray dosage is a great concern in FFOV scan. Region-of-interest (ROI) CT or sparse-view CT is considered to be a solution to reduce x-ray dosage in CT scanning, but it suffers from bright-band artifacts or streak artifacts giving contrast anomaly in the reconstructed image. In this study, we propose an image reconstruction method to eliminate the bright-band artifacts and the streak artifacts simultaneously. In addition to the ROI scan for the interior projection data with relatively high sampling rate in the view direction, we get sparse-view exterior projection data with much lower sampling rate. Then, we reconstruct images by solving a constrained total variation (TV) minimization problem for the interior projection data, which is assisted by the exterior projection data in the compressed sensing (CS) framework. For the interior image reconstruction assisted by the exterior projection data, we implemented the proposed method which enforces dual data fidelity terms and a TV term. The proposed method has effectively suppressed the bright-band artifacts around the ROI boundary and the streak artifacts in the ROI image. We expect the proposed method can be used for low-dose CT scans based on limited x-ray exposure to a small ROI in the human body.

• Journal of the Korean Wood Science and Technology
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• v.33 no.5 s.133
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• pp.21-28
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• 2005

For the proper conservation of wooden cultural properties, non-destructive evaluation (NDE) method, which can be used to quantitatively evaluate the internal state of wood members, are needed. In this study, an ultrasonic CT system composed of portable devices was attempted, and the capacity of this system was verified by reconstructing the CT images for two phantoms and two artificially defected specimens. Results from this study showed that the sizes of detected defects were enlarged and the shapes were distorted on the CT images. Also, the positions were shifted somewhat toward the surface of specimen, which is regarded due to the anisotropic property of wood. Compared to the filtered back-projection method, SIRT (simultaneous iterative reconstruction technique) method was determined to be more efficient as the algorithm of image reconstruction for wood. A new ultrasonic CT system is thought to be used as a NDE method for wood. However wood characteristics and wave diffraction within wood made it difficult to accurately evaluate the size, shape and position of defects. To improve the quality of CT image of wood, more research including the relationship between wood and ultrasound is needed, and wood properties should be taken into consideration on the image reconstruction algorithm.

• Journal of radiological science and technology
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• v.40 no.3
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• pp.385-392
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• 2017

The purpose of this study is to investigate the image quality and exposure dose according to kVp and mAs in CT and to confirm improvement in image quality according to None IR and IR(Iterative Reconstruction) levels. Measurement results of image quality using Image J, HU(Hounsfield units) and BN(Background Noise) are decreased, while SNR(Signal to Noise Ratio) and $CTDI_{vol}$(CT dose index volume) are increased as the kVp increases and there was no change of BHU(Background Hounsfield units). BN was reduced due to increased kVp, while SNR and $CTDI_{vol}$ were increased. Also, the higher IR stage, the lower BN, SI(Signal Intensity) and HU while SNR was improved by about 10~60%. Based on this, when applying IR for clinical applications, it is necessary to finely adjust kVp and mA with a phased approach.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.12C
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• pp.1194-1200
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• 2006

In this paper, we propose an adaptive MAP (Maximum A Posteriori) high-resolution image reconstruction algorithm using local statistics. In order to preserve the edge information of an original high-resolution image, a visibility function defined by local statistics of the low-resolution image is incorporated into MAP estimation process, so that the local smoothness is adaptively controlled. The weighted non-quadratic convex functional is defined to obtain the optimal solution that is as close as possible to the original high-resolution image. An iterative algorithm is utilized for obtaining the solution, and the smoothing parameter is updated at each iteration step from the partially reconstructed high-resolution image is required. Experimental results demonstrate the capability of the proposed algorithm.

• Journal of the Korean Institute of Telematics and Electronics
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• v.17 no.3
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• pp.20-25
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• 1980

A singular value decomposition (SVD) pseudo-inversion method has been applied to the image reconstruction from projections. This approach is relatively unknown and differs from conventionally used reconstructioll methods such as the Foxier convolution and iterative techniques. In this paper, two SVD pseudo-inversion methods have been discussed for the search of optimum reconstruction and restoration, one using truncated inverse filtering, the other scalar Wiener filtering. These methods partly overcome the ill-conditioned nature of restoration problems by trading off between noise and signal quality. To test the SVD pseudo-inversion method, simulations were performed from projection data obtained from a phantom using truncated inversefiltering. The results are presented together with some limitations particular to the applications of the method to the general class of 3-D image reconstruction and restoration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4160-4176
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• 2015

Data compression like image and video compression has come a long way since the introduction of Compressive Sensing (CS) which compresses sparse signals such as images, videos etc. to very few samples i.e. M < N measurements. At the receiver end, a robust and efficient recovery algorithm estimates the original image or video. Many prominent algorithms solve least squares problem (LSP) iteratively in order to reconstruct the signal hence consuming more processing time. In this paper non-iterative threshold based recovery algorithm (NITRA) is proposed for the recovery of images and videos without solving LSP, claiming reduced complexity and better reconstruction quality. The elapsed time for images and videos using NITRA is in ㎲ range which is 100 times less than other existing algorithms. The peak signal to noise ratio (PSNR) is above 30 dB, structural similarity (SSIM) and structural content (SC) are of 99%.

• Proceedings of the Korean Society of Medical Physics Conference
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• 1999.11a
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• pp.121-123
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• 1999

• Nuclear Medicine and Molecular Imaging
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• v.42 no.2
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• pp.118-126
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• 2008

Statistical image reconstruction methods have played an important role in emission computed tomography (ECT) since they accurately model the statistical noise associated with gamma-ray projection data. Although the use of statistical methods in clinical practice in early days was of a difficult problem due to high per-iteration costs and large numbers of iterations, with the development of fast algorithms and dramatically improved speed of computers, it is now inevitably becoming more practical. Some statistical methods are indeed commonly available from nuclear medicine equipment suppliers. In this paper, we first describe a mathematical background for statistical reconstruction methods, which includes assumptions underlying the Poisson statistical model, maximum likelihood and maximum a posteriori approaches, and prior models in the context of a Bayesian framework. We then review a recent progress in developing fast iterative algorithms.