• Title/Summary/Keyword: CT-reconstruction

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Noise Properties for Filtered Back Projection in CT Reconstruction (필터보정역투영 CT 영상재구성방법에서 잡음 특성)

  • Chon, Kwonsu
    • Journal of the Korean Society of Radiology
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    • v.8 no.6
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    • pp.357-364
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    • 2014
  • The filtered back projection in the image reconstruction algorithms for the clinic computed tomography system has been widely used. Noise of the reconstructed image was examined under the input noise for parallel and fan beam geometries. The reconstruction images of $512{\times}512$ size were carried out under 360 and 720 projection by the Visual C++ for parallel beam and fan beam, respectively, and those agreed with the original Shepp-Logan head phantom very much. Noise was generated because of intrinsic restriction (finite number of projections) for the image reconstruction algorithm, filtered back projection, when no input noise was applied. Because the result noise was rapidly increased under 0.5% input noise ratio, technologies for reducing noise in CT system and image processing is important.

CT Image Reconstruction of Wood Using Ultrasound Velocities II - Determination of the Initial Model Function of the SIRT Method -

  • Kim, Kwang-Mo;Lee, Jun-Jae
    • Journal of the Korean Wood Science and Technology
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    • v.33 no.5 s.133
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    • pp.29-37
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    • 2005
  • A previous study verified that the SIRT (simultaneous iterative reconstruction technique) method is more efficient than the back-projection method as a CT algorithm for wood. However, it was expected that the determination of the initial model function of the SIRT method would influence the quality of CT image. Therefore, in this study, we intended to develop a technique that could be used to determine an adequate initial model function. For this purpose, we proposed several techniques, and for each technique we examined the effects of the initial model function on the average errors and the CT image at each iteration. Through this study, it was shown that the average error was decreased and the image quality was improved using the proposed techniques. This tendency was most pronounced when the back-projection method was used to determine the initial model function. From the results of this study, we drew the following conclusions: 1) The initial model function of the SIRT method should be determined with careful attention, and 2) the back-projection method efficiently determines the initial model function of the SIRT method.

Evaluation of Standardized Uptake Value applying EQ PET across different PET/CT scanners and reconstruction (PET/CT 장비와 영상 재구성 차이에 따른 EQ PET을 이용한 표준섭취계수의 평가)

  • Yoon, Seok Hwan;Kim, Byung Jin;Moon, Il Sang;Lee, Hong Jae
    • The Korean Journal of Nuclear Medicine Technology
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    • v.22 no.1
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    • pp.35-42
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    • 2018
  • Purpose Standardized uptake value(SUV) has been widely used as a quantitative metric of uptake in PET/CT for diagnosis of malignant tumors and evaluation of tumor therapy response. However, the SUV depends on various factor including PET/CT scanner specifications and reconstruction parameter. The purpose of this study is to validate a EQ PET to evaluate SUV across different PET/CT systems. Materials and Methods First, NEMA IEC body phantom data were used to calculate the EQ filter for OSEM3D with PSF and TOF reconstruction from three different PET/CT systems in order to obtain EARL compliant recovery coefficients of each spheres. The Biograph true point 40 PET/CT images were reconstructed with a OSEM3D+PSF reconstruction, images of the Biograph mCT 40 and Biograph mCT 64 PET/CT scanners were reconstructed with a OSEM3D+PSF, OSEM3D+TOF, OSEM3D+PSF+TOF. Post reconstructions, the proprietary EQ filter was applied to the reconstruction data. Recovery coefficient can be estimated by ratio of measured to true activity concentration for spheres of different volume and coefficient variability(CV) value of RC for each sphere was compared. For clinical study, we compared SUVmax applying different reconstruction algorithms in FDG PET images of 61 patients with lung cancer using Biograph mCT 40 PET/CT scanner. Results For the phantom studied, the mean values of CV for OSEM3D, OSEM3D+PSF, OSEM3D+TOF and OSEM3D+PSF+TOF reconstructions were 0.05, 0.04, 0.04 and 0.03 respectively for RC. Application of the proprietary EQ filter, the mean values of CV for OSEM3D, OSEM3D+PSF, OSEM3D+TOF and OSEM3D+PSF+TOF reconstructions were 0.04, 0.03, 0.03 and 0.02 respectively for RC. Clinical study, there were no statistical significance of the difference applying EQ PET on SUVmax of 61 patients FDG PET image. (p=1.000) Conclusion This study indicates that CV values of RC in phantom were decreased after applying EQ PET for different PET/CT system and The EQ PET reduced reconstruction dependent variation in SUVs for 61 lung cancer patients, Therefore, EQ PET will be expected to provide accurate quantification when the patient is scanned on different PET/CT system.

Influence of CT Reconstruction on Spatial Resolution (CT 영상 재구성의 공간분해능에 대한 영향)

  • Chon, Kwon Su
    • Journal of the Korean Society of Radiology
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    • v.12 no.1
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    • pp.85-91
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    • 2018
  • Computed tomography, which obtains section images from reconstruction process using projection images, has been applied to various fields. The spatial resolution of the reconstructed image depends on the device used in CT system, the object, and the reconstruction process. In this paper, we investigates the effect of the number of projection images and the pixel size of the detector on the spatial resolution of the reconstructed image under the parallel beam geometry. The reconstruction program was written in Visual C++, and the matrix size of the reconstructed image was $512{\times}512$. The numerical bar phantom was constructed and the Min-Max method was introduced to evaluate the spatial resolution on the reconstructed image. When the number of projections used in reconstruction process was small, artifact like streak appeared and Min-Max was also low. The Min-Max showed upper saturation when the number of projections is increased. If the pixel size of the detector is reduced to 50% of the pixel size of the reconstructed image, the reconstructed image was perfectly recovered as the original phantom and the Min-Max decreased as increasing the detector pixel size. This study will be useful in determining the detector and the accuracy of rotation stage needed to achieve the spatial resolution required in the CT system.

Evaluation of Performance and No-reference-based Quality for CT Image with ADMIRE Iterative Reconstruction Parameters: A Pilot Study (ADMIRE 반복적 재구성 파라메터에 따른 CT 영상의 특성 및 무참조 기반 화질 평가: 선행연구)

  • Bo-Min Park;Yoo-Jin Seo;Seong-Hyeon Kang;Jina Shim;Hajin Kim;Sewon Lim;Youngjin Lee
    • Journal of radiological science and technology
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    • v.47 no.3
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    • pp.175-182
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    • 2024
  • Advanced modeled iterative reconstruction (ADMIRE) represents a repetitive reconstruction method that can adjust strength and kernel, each of which are known to affect computed tomography (CT) image quality. The aim of this study was to quantitatively analyze the noise and spatial resolution of CT images according to ADMIRE control factors. Patient images were obtained by applying ADMIRE strength 2 and 3, and kernel B40 and B59. For quantitative evaluations, the noise level, spatial resolution, and overall image quality were measured using coefficient of variation (COV), edge rise distance (ERD), and natural image quality evaluation (NIQE). The superior values for the average COV, ERD, and NIQE results were obtained for the ADMIRE reconstruction conditions of ADMIRE 2 + B40, ADMIRE 3 + B59, and ADMIRE3 + B59. NIQE, which represents the overall image quality based on no-reference, was about 6.04 when using ADMIRE 3 + B59, showing the best result among the reconstructed image acquisition conditions. The results of this study indicate that the ADMIRE strength and kernel chosen for use in ADMIRE reconstruction have a significant impact on CT image quality. This highlights the importance of adjusting to the control factors in consideration of the clinical environment.

An Iterative Image Reconstruction Method for the Region-of-Interest CT Assisted from Exterior Projection Data (Exterior 투영데이터를 이용한 Region-of-Interest CT의 반복적 영상재구성 방법)

  • Jin, Seung Oh;Kwon, Oh-Kyong
    • 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.

RECONSTRUCTION OF LIMITED-ANGLE CT IMAGES BY AN ADAPTIVE RESILIENT BACK-PROPAGATION ALGORITHM

  • Kazunori Matsuo;Zensho Nakao;Chen, Yen-Wei;Fath El Alem F. Ah
    • Proceedings of the IEEK Conference
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    • 2000.07b
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    • pp.839-842
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    • 2000
  • A new and modified neural network model Is proposed for CT image reconstruction from four projection directions only. The model uses the Resilient Back-Propagation (Rprop) algorithm, which is derived from the original Back-Propagation, for adaptation of its weights. In addition to the error in projection directions of the image being reconstructed, the proposed network makes use of errors in pixels between an image which passed the median filter and the reconstructed one. Improved reconstruction was obtained, and the proposed method was found to be very effective in CT image reconstruction when the given number of projection directions is very limited.

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Evaluation and Comparison of Contrast to Noise Ratio and Signal to Noise Ratio According to Change of Reconstruction on Breast PET/CT (Breast PET CT 영상 재구성 변화에 따른 대조도 대 잡음비와 신호 대 잡음비의 비교평가)

  • Lee, Jea-Young;Lee, Eul-Kyu;Kim, Ki-Won;Jeong, Hoi-Woun;Lyu, Kwang-Yeul;Park, Hoon-Hee;Son, Jin-Hyun;Min, Jung-Whan
    • Journal of radiological science and technology
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    • v.40 no.1
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    • pp.79-85
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    • 2017
  • The purpose of this study was to measure contrast to noise ratio (CNR) and signal to noise ratio (SNR) according to change of reconstruction from region of interest (ROI) in breast positron emission tomography-computed tomography (PET-CT), and to analyze the CNR and SNR statically. We examined images of breast PET-CT of 100 patients in a University-affiliated hospital, Seoul, Korea. Each patient's image of breast PET-CT were calculated by using ImageJ. Differences of CNR and SNR among four reconstruction algorithms were tested by SPSS Statistics21 ANOVA test for there was statistical significance (p<0.05). We have analysis socio-demographical variables, CNR and SNR according to reconstruction images, 95% confidence according to CNR and SNR of reconstruction and difference in a mean of CNR and SNR. SNR results, with the quality of distributions in the order of PSF_TOF, Iterative and Iterative-TOF, FBP-TOF. CNR, with the quality of distributions in the order of PSF_TOF, Iterative and Iterative-TOF, FBP-TOF. CNR and SNR of PET-CT reconstruction methods of the breast would be useful to evaluate breast diseases.

COMPARISON OF IMAGE REFORMATION USING PERSONAL COMPUTER WITH CT SCAN RECONSTRUCTION (CT 스캔 영상재구성과 개인용 컴퓨터를 이용한 영상 재형성과의 비교에 관한 연구)

  • Jung Gi-Hun;Kim Eun-Kyung;Kim Sang-Joon
    • Journal of Korean Academy of Oral and Maxillofacial Radiology
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    • v.24 no.2
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    • pp.361-368
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    • 1994
  • Radiographic planning is needed for implant placement in order to determine implant length, jaw bone volume, anatomical stucture and so on. Radiographic examination includes conventional radiography, conventional tomography and CT scan. The most accurate mesurement can be obtained from CT scan. For the cross-sectional view of mandible, CT scan reconstruction is generally needed. But the cross-sectional view of mandible can be reformed by personal computer. This study was performed to examine the clinical usefulness of reformed image using personal computer in comparison with CT scan reconstructed image. CT axial slices of 4 mandibles of 4 volunteers were used. Digital imaging system was composed of Macintosh Ⅱ ci computer, high resolution Sony XC-77 CCD camera, Quick Capture frame grabber board and 'NIH Image' program. Seven reconstructed cross-sectional images within CT machine(CT group) were obtained. And seven reformed cross-sectional images(PC group) after digitization of CT axial slices into the personal computer were obtained. PC group was compared with CT group in the objective and subjective aspects. The results were as follow: 1. Measurement of mandibular height & width in both group showed insignificant difference(P>0.05). 2. Subjective assessment of the mandibular canal in both group showed insignificant difference(P>0.05). 3. Image reformation using personal computer could provide panoramic view, which could not be obtained in CT scan reconstruction.

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Comparison of a Deep Learning-Based Reconstruction Algorithm with Filtered Back Projection and Iterative Reconstruction Algorithms for Pediatric Abdominopelvic CT

  • Wookon Son;MinWoo Kim;Jae-Yeon Hwang;Young-Woo Kim;Chankue Park;Ki Seok Choo;Tae Un Kim;Joo Yeon Jang
    • Korean Journal of Radiology
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    • v.23 no.7
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    • pp.752-762
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    • 2022
  • Objective: To compare a deep learning-based reconstruction (DLR) algorithm for pediatric abdominopelvic computed tomography (CT) with filtered back projection (FBP) and iterative reconstruction (IR) algorithms. Materials and Methods: Post-contrast abdominopelvic CT scans obtained from 120 pediatric patients (mean age ± standard deviation, 8.7 ± 5.2 years; 60 males) between May 2020 and October 2020 were evaluated in this retrospective study. Images were reconstructed using FBP, a hybrid IR algorithm (ASiR-V) with blending factors of 50% and 100% (AV50 and AV100, respectively), and a DLR algorithm (TrueFidelity) with three strength levels (low, medium, and high). Noise power spectrum (NPS) and edge rise distance (ERD) were used to evaluate noise characteristics and spatial resolution, respectively. Image noise, edge definition, overall image quality, lesion detectability and conspicuity, and artifacts were qualitatively scored by two pediatric radiologists, and the scores of the two reviewers were averaged. A repeated-measures analysis of variance followed by the Bonferroni post-hoc test was used to compare NPS and ERD among the six reconstruction methods. The Friedman rank sum test followed by the Nemenyi-Wilcoxon-Wilcox all-pairs test was used to compare the results of the qualitative visual analysis among the six reconstruction methods. Results: The NPS noise magnitude of AV100 was significantly lower than that of the DLR, whereas the NPS peak of AV100 was significantly higher than that of the high- and medium-strength DLR (p < 0.001). The NPS average spatial frequencies were higher for DLR than for ASiR-V (p < 0.001). ERD was shorter with DLR than with ASiR-V and FBP (p < 0.001). Qualitative visual analysis revealed better overall image quality with high-strength DLR than with ASiR-V (p < 0.001). Conclusion: For pediatric abdominopelvic CT, the DLR algorithm may provide improved noise characteristics and better spatial resolution than the hybrid IR algorithm.