• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.203-209
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• 2022

One of the typical methods for lowering radiation dose while maintaining image quality of computed tomography (CT) is the use of model-based iterative reconstruction (MBIR). This study is to evaluate the image quality by adjusting the strength of the advanced modeled iterative reconstruction (ADMIRE), which is well known as a representative model of MBIR. The study was conducted using phantom, and CT images were obtained while adjusting the strength of ADMIRE in units of 1 to 5. Quantitative evaluation includes noise levels using coefficient of variation (COV) and contrast to noise ratio (CNR), as well as natural image quality evaluation (NIQE) and blind/referenceless image spatial quality evaluator (BRISQUE). As a result, in both noise level and blind quality evaluation results, the higher the strength of ADMIRE, the better the results were derived. In particular, it was confirmed that COV and CNR were improved 1.89 and 1.75 times at ADMIRE 5 compared to ADMIRE 1, respectively, and NIQE and BRISQUE were proved to be improved 1.35 and 1.22 times at ADMIRE 5 compared to ADMIRE 1, respectively. In conclusion, this study was proved that the reconstruction strength of ADMIRE had a great influence on the noise level and overall image quality evaluation of CT images.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3153-3155
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• 1999

최근 수년간 의료영상분야는 국내외적으로 급격한 발전을 거듭하고 있다. 특히 자기공명영상장치 (Magnetic Resonance Imaging), X-ray CT(Computed Tomography)와 단층촬영장치는 인체내부를 비침습적(non-invasive)으로 영상화함으로써 해부학적인 질병진단에 많은 장점을 가지고 있다. 이와같은 단층영상 재구성에는 역매트릭스법(matrix inversion). 반복재구성법(interative method), 역투영 법(back-projection), 2차원 Fourier 변환법(2D FFT), 중첩재구성법(Filtered back-projection) 등의 다양한 알고리즘을 사용하고 있다. 본 연구에서는 X-ray CT에서의 단층영상재구성 기법 중 널리 사용되고 있는 Filtered Back Projection 기법의 연산순서도와 연산량을 분석하고 이를 시뮬레이션을 통하여 확인하고 실시간 영상재구성을 위하여 범용 Digital Signal Processor의 병렬처리시스템 구성에 기반된 최적 Architecture를 선정하고자 한다.

• Journal of radiological science and technology
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• v.37 no.4
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• pp.323-330
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• 2014

Image reconstruction is one of the most important processes in CT (Computed tomography) technology. For fast scanning and low dose to the objects, iterative reconstruction is becoming more and more important. In the implementation of iterative reconstruction, projection and backprojection processes are considered to be indispensable parts. However, many approaches for projection and backprojection may result severe image artifacts due to their discrete characteristics and affects the reconstructed image quality. Thus, new approaches for projection and backprojection are highly demanded these days. In this paper, distance-driven approach was evaluated and compared with other conventional methods. The numerical simulator was developed to make the phantoms, and projection and backprojection images were compared using these approaches. As a result, it turned out that there are less artifacts during projection and backprojection in parallel and fan beam geometry.

• Journal of Biomedical Engineering Research
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• v.24 no.5
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• pp.473-480
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• 2003

Iterative reconstruction methods have played a prominent role in emission computed tomography due to their remarkable advantages over the conventional filtered backprojection method. However, since iterative reconstructions typically are comprised of repeatedly projecting and backprojecting the data, the computational load required for reconstructing an image depends highly on the performance of the projector-backprojector pair used in the algorithm. In this work we compare quantitative performance of representative methods for implementing projector-backprojector pairs. To reduce the overall cost for the projection-backprojection operations for each method, we investigate how previously computed results can be reused so that the number of redundant calculations can be minimized. Our experimental results demonstrate that the ray tracing method not only outperforms other methods in computation time, but also provides improved reconstructions with good accuracy.

• 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 the Korean Society of Radiology
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• v.13 no.2
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• pp.159-168
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• 2019

We examined the effect of Siemens ADMIRE (Advanced Modeled Iterative Reconstruction) on image quality by measuring changes in HU, noise, and SNR of background air, fat, muscle, and background signals on a chest CT scan. Experimental results show that as the ADMIRE Strength increases, the noise decreases and the signal increases, consequently the signal-to-noise ratio increases. ADMIRE can reduce noise by 28 ~ 61% compared to FBP, which is a conventional image reconstruction algorithm, and improves SNR by 16 ~ 100%.

• Journal of Korea Multimedia Society
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• v.12 no.3
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• pp.372-382
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• 2009

When the range of projection angles is limited, tomographic reconstruction suffers from artifacts caused by incomplete data. One can consider a data completion technique, which estimates projection data at unobserved angles using a prior knowledge or mathematical exploration, but the result is often not improved; the improvement by the data completion often undermined by the artifacts by inaccurate estimation, In this paper, we propose an iterative method, which computes projection data at unobserved angles by using the current estimate on the image, links the computed projection data to the observed ones by using the consistence condition of Radon transform, and reconstruct the next estimate on the image by filtered backprojection. The proposed method does not require a prior knowledge on the image, and has much faster approximation rate than the expectation maximization method. The performance of the proposed method was tested through several simulation studies.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.6
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• pp.511-520
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• 2009

In this paper, we present a new iso-density surface reconstruction scheme based on a hierarchy on the input volume data and the output mesh data. From the input volume data, we construct a hierarchy of volumes, called a volume pyramid, based on a 3D dilation filter. After constructing the volume pyramid, we extract a coarse base mesh from the coarsest resolution of the pyramid with the Cell-boundary representation scheme. We iteratively fit this mesh to the iso-points extracted from the volume data under O(3)-adjacency constraint. For the surface fitting, the shrinking process and the smoothing process are adopted as in the SWIS (Shrink-wrapped isosurface) algorithm[6], and we subdivide the mesh to be able to reconstruct fine detail of the isosurface. The advantage of our method is that it generates a mesh which can be utilized by several multiresolution algorithms such as compression and progressive transmission.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.11 s.188
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• pp.93-102
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• 2006

In this study an advanced domain decomposition method is suggested in order to construct surface models from very large amount of points. In this method the spatial domain of interest that is occupied by the input set of points is divided in repetitive manner. First, the space is divided into smaller domains where the problem can be solved independently. Then each subdomain is again divided into much smaller domains where the problem can be solved locally. These local solutions of subdivided domains are blended together to obtain a solution of each subdomain using partition of unity function. Then the solutions of subdomains are merged together in order to construct whole surface model. The suggested methods are conceptually very simple and easy to implement. Since RDDM(Repetitive Domain Decomposition Method) is effective in the computation time and memory consumption, the present study is capable of providing a fast and accurate reconstructions of complex shapes from large amount of point data containing millions of points. The effectiveness and validity of the suggested methods are demonstrated by performing numerical experiments for the various types of point data.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.297-303
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• 2023

Pediatric Computed Tomography (CT) examinations can often result in exam failures or the need for frequent retests due to the difficulty of cooperation from young patients. Deep Learning Image Reconstruction (DLIR) methods offer the potential to obtain diagnostically valuable images while reducing the retest rate in CT examinations of pediatric patients with high radiation sensitivity. In this study, we investigated the possibility of applying DLIR to reduce artifacts caused by respiration or motion and obtain clinically useful images in pediatric chest CT examinations. Retrospective analysis was conducted on chest CT examination data of 43 children under the age of 7 from P Hospital in Gyeongsangnam-do. The images reconstructed using Filtered Back Projection (FBP), Adaptive Statistical Iterative Reconstruction (ASIR-50), and the deep learning algorithm TrueFidelity-Middle (TF-M) were compared. Regions of interest (ROI) were drawn on the right ascending aorta (AA) and back muscle (BM) in contrast-enhanced chest images, and noise (standard deviation, SD) was measured using Hounsfield units (HU) in each image. Statistical analysis was performed using SPSS (ver. 22.0), analyzing the mean values of the three measurements with one-way analysis of variance (ANOVA). The results showed that the SD values for AA were FBP=25.65±3.75, ASIR-50=19.08±3.93, and TF-M=17.05±4.45 (F=66.72, p=0.00), while the SD values for BM were FBP=26.64±3.81, ASIR-50=19.19±3.37, and TF-M=19.87±4.25 (F=49.54, p=0.00). Post-hoc tests revealed significant differences among the three groups. DLIR using TF-M demonstrated significantly lower noise values compared to conventional reconstruction methods. Therefore, the application of the deep learning algorithm TrueFidelity-Middle (TF-M) is expected to be clinically valuable in pediatric chest CT examinations by reducing the degradation of image quality caused by respiration or motion.