Earticle

본 연구는 환자 피폭선량의 감소를 위한 목적으로 일개 병원에서 시행되는 경부 ct 검사의 촬영조건에서 관 전압과 영상처리 알고리즘의 적절한 적용으로 기존 영상 화질과 유사한 촬영조건과 방사선량을 평가하였다. 2021년 6월부터 11월까지 내원한 여성 환자 150명을 대상으로 경부 CT 검사를 시행하였다. 촬영조건은 80kV, 100kV, 120kV 자동 노출 조절 방법으로 영상을 획득하였다. 쵤영 후 영상 재구성 알고리즘인 SAFIRE를 단계적 0, 1, 3, 5 로 적용하였고, 경부 주변 조직에 관심 영역을 설정하여 신호대잡음비(Signal to noise, SNR)와 대조도비(Contrast to noise ratio, CNR)로 영상을 정량적 분석하였다. 그룹 간 비교에서 SAFIRE의 강도가 증가할수록 노이즈가 감소 하였으며 신호대잡음비, 대조도비가 증가하는 양상을 보였다. 또한 120kV의 자동 노출 조절 방법과 SAFIRE 강도 3과 비교적 유사한 영상의 화질을 가지는 조건은 100kV의 자동 노출 조절 방법과 SAFIRE 강도 3으로 나타났다. 120kV 자동 노출 조절 방법에서 100kV 자동 노출 조절 방법으로 낮추었더니 방사선량이 13.73%로 감소하였다.

This study evaluated the imaging conditions and radiation dose similar to the existing image quality by appropriate application of tube voltage and image processing algorithm in the imaging conditions of a cervical ct test performed at a hospital for the purpose of reducing the patient's exposure dose. Cervical CT scans were performed on 150 female patients who visited the hospital from June to November 2021. Images were acquired by automatic exposure control method of 80kV, 100kV, and 120kV under the shooting conditions. SAFIRE, a post-image reconstruction algorithm, was applied in steps of 0, 1, 3, and 5, and the region of interest was set in the surrounding tissues of the neck to obtain images with Signal to Noise (SNR) and Contrast to Noise Ratio (CNR). was quantitatively analyzed. In comparison between groups, as the intensity of SAFIRE increased, the noise decreased, and the signal-to-noise ratio and contrast ratio increased. In addition, the conditions of the automatic exposure control method of 120kV and the image quality relatively similar to SAFIRE intensity 3 were the automatic exposure control method of 100kV and SAFIRE intensity 3. When it was lowered from the 120kV automatic exposure control method to the 100kV automatic exposure control method, the radiation dose was reduced to 13.73%.

In this study, the correlation among the changes of Modulation Transfer Function(MTF) in the noise and high-contrast resolution and the change of Contrast to noise ratio(CNR) in the low-contrast resolution will be examined to investigate the estimation of image quality according to the type of algorithms. The image data obtained by scanning American Association of Physicists in Medicine(AAPM) phantom was applied to each algorithm and the exposure condition of 120 kVp, 250 mAs, and then the CT number and noise were measured. The MTF curved line of the high-contrast resolution was calculated with Point Spread Function(PSF) by using the analysis program by Philips, resulting in 0.5 MTF, 0.1 MTF and 0.02 MTF respectively. The low-contrast resolution was calculated with CNR and the uniformity was measured to each algorithm. Since the measurement value for the uniformity of the equipment was below ± 5 HU, which is the criterion figure, it was found to belong to the normal range. As the algorithm got closer from soft to edge, the standard deviation of CT number increased, which indicates that the noise increased as well. As for MTF, 0.5 MTF, 0.1 MTF and 0.02 MTF were all sharp algorithms, and as the algorithm got closer from soft to edge, it was possible to distinguish more clearly with the naked eye. On the other hand, CNR gradually decreased, because the difference between the contrast hole CT number and the acrylic CT number was the same while the noise of hole increased.

To solve the "soft field" effect and the ill-posed problem in electrical capacitance tomography technology, a novel image reconstruction algorithm based on population entropy and adaptive differential evolution for Electrical Capacitance Tomography is proposed in this study. The algorithm uses all the gray pixels as the initial population’s individual. After finite iterations, the algorithm mutates and makes crossover of the population in order to obtain the optimal species populations. That is the optimal value for the ECT imaging pixels. The population entropy and the variation factor make the range of each searching generation decreasing. In the simulation, the improved adaptive differential evolution algorithm will be compared with the LBP algorithm. The result shows that the new algorithm has better image quality and more stable boundary than the LBP Algorithm, which provides a new way to reconstruct images for ECT.

According to the image reconstruction accuracy influenced by the “soft field” nature and the limited projection data in electrical capacitance tomography, based on the working principle of the electrical capacitance tomography system, a Novel image reconstruction algorithm based on compressed sensing is proposed in the paper. The method based on ART (algebra reconstruction technique) organically combines the gradient sparse of image and ART, and reduces the norm of image gradient with full-variational method, and improves the accuracy and speed of image reconstruction. Experimental results and simulation data indicate that the imaging accuracy is markedly improved, and the image is closed to the prototype. This new algorithm presents a feasible and effective way to research on image reconstruction algorithm for Electrical Capacitance Tomography System.

In order to solve the problem of image reconstruction in electrical capacitance tomography (ECT) technology, the feasibility of applying convolutional neural network (CNN) to ECT image reconstruction is studied. The convolution layer and the training of the structure of the sub sampling method is improved based on deep research for convolution neural network for the more time-consuming process of deep structure and training issues, and a fast convergence convolution neural network (FCCNN) image reconstruction method is proposed. Matlab was used to build a ECT simulation system. For each algorithm, the simulation results were compared and analyzed. The experimental results show that our algorithm improved the image reconstruction efficiency and quality of the common flow pattern.

Due to the problem of low imaging accuracy and slow imaging speed when applying SVM image reconstruction algorithm in ECT system to dealing with a large amount of sample data set, the method of combining feature dimension reduction with SVM algorithm is proposed. This method classifies the sample data by using the way of clustering and extracts the feature parameter, finds out the connection between each sample and the feature, and deals the sample data with dimension reduction, thus finally getting the high-quality training sample. Then it trains the simplified sample data by applying SVM algorithm and obtains decision function, then the decision function is used to predict and image. The experimental results of image reconstruction show that this method greatly reduces the running time and improves the accuracy of imaging compared to using the SVM algorithm alone.

The paper introduces the Keren registration method and points out its disadvantage which means it will become inaccuracy on the large scale parameters. To reduce the error on large scale parameters of Keren registration, a two step method is proposed, which the phase correlation algorithm is used to estimate the large translation and rotation angle roughly and the improved Keren algorithm is used to estimate accurately the small translation and rotation angle. The experimental results show that the two step method makes less absolute error of angle than Keren method in the situation of large translation and rotation angle. A new method of estimating the standard deviation of noise is introduced to the robust certainty function, which reduces the impact of noise in the process of interpolation using normalized convolution algorithm. By the edge detection of fusion image in the first stage of the interpolation process of normalized convolution algorithm, a calculation method of long axis and short axis of the structure self-adaptive function is improved. The experimental results show that the proposed interpolation method can improve the performance of the original algorithm and enhance the effect of image super-resolution reconstruction.

Superresolution image reconstruction technique uses single or a series of low-resolution images to reconstruct a high resolution image without changing the hardware devices, while improving image quality and the spatial resolution of the image. High resolution means the image with a higher pixel density, can provide more details. In this paper, a novel image superresolution algorithm based on sparse representation is studied. During over-complete dictionary of the training phase, the proposed method improves two aspects including feature extraction and dimension reduction. In the feature extraction process, combining the second derivative with the gradient direction, we construct a new descent direction to improve gradient method. The convergence speed of the new algorithm is faster than the gradient method and can get better results. Then improved two-dimensional Principal Component Analysis (2DPCA) algorithm is used to reduce the dimension, it could eliminate the correlation of the image lines and column. Experiment results show that this method of image reconstruction is better and faster for high resolution image reconstruction.

Compressed sensing is a new signal sampling theory that fully makes use of signal’s sparsity or compressibility. The theory shows that, the acquisition of a small amount of the sparse or compressible signal value can be used for exact signal reconstruction. Based on the study and summarization of the existing reconstruction algorithms, this paper proposes a novel blocking variable step size forward-backward pursuit (BVSSFBP). This paper proposed variable step size forward-backward pursuit algorithm by introducing the concept of sparse phase and variable step size to deal with different situations. The algorithm also divides two-dimensional image into blocks, in order to reduce the scale of observation matrix during single processing, reduce the single processing speed and the overall running time. Experimental results show BVSSFBP algorithm can obtain better reconstructed image quality.

Biological tissues have electrical conductivity and permittivity properties which depend on tissue composition, structure and health status. Bioelectrical properties can be utilized for non invasive disease diagnosis. Electrical Impedance Tomography (EIT) is a method for reconstructing the image of distribution of electrical conductivity and permittivity inside a volume from measurements made at the surface of the volume. EIT image reconstruction is an ill-posed problem that requires a priori information called regularization. The Total Variation (TV) regularization is often used in solving EIT inverse problem. In this paper, simulation has been carried out in noise free and noisy cases and TV regularized iterative Primal Dual Interior Point Method (PD-IPM) has been used to reconstruct the difference conductivity image.

Image super resolution reconstruction has important significance in remote sensing image feature extraction and classification etc.. Because the remote sensing image size is larger, it is difficult to super resolution reconstruction using multiple images, the compressed sensing (CS) theory was introduced into the super-resolution reconstruction. Algorithm designed the low pass filter to reduce the sample correlation matrix and wavelet, at the same time, the algorithm selects the partial Hadamard-matrix as the measurement matrix, it has faster reconstruction speed and low storage requirements, which ensure that the image reconstruction keep with the RIP criterion of compressed sensing theory . Finally, this paper realizes the remote sensing image super resolution reconstruction through the improved iterative algorithm. Experiments show that the reconstructed images of the PSNR value has increased, the reconstructed image has a better visual effect.

최근에 광간섭 단층촬영은 생물학적 조직을 비 침습적으로 이미지를 얻는데 많이 사용되고 있다. 그러나, 광간섭 단층촬영은 노이즈 때문에 해석하는데 아직까지는 어려움을 갖고 있다. 본 논문에서는 인체와 토끼의 연골 이미지들의 이미지에서 잡음을 제거하는 다양한 영상처리 기술을 적용해 보았다. 또한 광간섭 단층촬영으로 얻은 이미지들을 영상 분할 방법을 통해 얻고자 하는 부위를 구별 하였으며 대부분의 이미지들이 영상분할 알고리즘에 적합함을 볼 수 있었다. 그리고, 광간섭 단층영상에 적합한 영상분할 방법을 선택한 후 영상을 재구성 하였다. 광간섭 단층촬영은 작은 깊이와 거리에 제한을 가지고 있기 때문에 영상처리장치에 단점을 가지고 있다. 광간섭 이미지가 매우 작은 공간에서 이루어 짐으로 같은 지역의 영상을 재구성 하기는 어려운 점이 있다. 그래서, 광간섭 단층영상 재구성을 할 때 좋은 매칭 알고리즘 방법이 필요하다. 본 논문에서는 챔퍼 매칭 알고리즘을 사용하여 재구성 하였다. 본 연구에서는 OCT 연골 이미지를 얻어 노이즈 제거, 영상 분할, 3D 광간섭 단층 영상을 재구성 할 수 있었다.

Recently, optical coherence tomography (OCT) has demonstrated considerable promise for the noninvasive assessment of biological tissues. However, OCT images difficult to analyze due to speckle noise. In this paper, we tested various image processing techniques for speckle removal of human and rabbit cartilage OCT images. Also, we distinguished the images which get with methods of image segmentation for OCT images, and found the most suitable method for segmenting an image. And, we selected image segmentation suitable for OCT before image reconstruction. OCT was a weak point to system design and image processing. It was a limit owing to measure small a distance and depth size. So, good edge matching algorithms are important for image reconstruction. This paper presents such an algorithm, the chamfer matching algorithm. It is made of background for 3D image reconstruction. The purpose of this paper is to describe good image processing techniques for speckle removal, image segmentation, and the 3D reconstruction of cartilage OCT images.

In electrical impedance tomography (EIT), various image reconstruction algorithms have been used in order to compute the internal resistivity distribution of the unknown object with its electric potential data at the boundary. Mathematically, the EIT image reconstruction algorithm is a nonlinear ill-posed inverse problem. This paper presents a simulated annealing technique as a statistical reconstruction algorithm for the solution of the static EIT inverse problem. Computer simulations with 32 channels synthetic data show that the spatial resolution of reconstructed images by the proposed scheme is improved as compared to that of the mNR algorithm at the expense of increased computational burden.

Electrical impedance tomograpy (EIT) determines the resistivity distribution inside an inhomogeneous target by means of voltage and current measurements conducted at the target boundary. In this paper, a genetic algorithm (GA) approach is proposed for the solution of the EIT image reconstruction. Results of numerical experiments of EIT solved by the GA approach are presented and compared to that obtained by the modified Newton-Raphson method. The GA approach is relatively expensive in terms of computing time and resources, and at present this limits the applicability of GA to the field of static imaging. However, the continuous and rapid growth of computing resources makes the development of real-time dynamic imaging applications based on GA´s conceivable in the near future.

In electrical impedance tomography (EIT), the internal resistivity distribution of the unknown object is computed using the boundary voltage data induced by different current patterns using various reconstruction algorithms. This paper presents a new image reconstruction algorithm based on the genetic algorithm (GA) via a two-step approach for the solution of the EIT inverse problem, in particular for the reconstruction of "static" images. The computer simulation for the 32 channels synthetic data shows that the spatial resolution of reconstructed images in the proposed scheme is improved compared to that of the modified Newton-Raphson algorithm at the expense of an increased computational burden.rden.