• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.180-187
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• 2021

In this paper, we present a computational volumetric reconstruction method for three-dimensional (3D) photon counting imaging with enhanced visual quality when low-resolution elemental images are used under photon-starved conditions. In conventional photon counting imaging with low-resolution elemental images, it may be difficult to estimate the 3D scene correctly because of a lack of scene information. In addition, the reconstructed 3D images may be blurred because volumetric computational reconstruction has an averaging effect. In contrast, with our method, the pixels of the elemental image rearrangement technique and a Bayesian approach are used as the reconstruction and estimation methods, respectively. Therefore, our method can enhance the visual quality and estimation accuracy of the reconstructed 3D images because it does not have an averaging effect and uses prior information about the 3D scene. To validate our technique, we performed optical experiments and demonstrated the reconstruction results.

• The Journal of Engineering Research
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• v.2 no.1
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• pp.49-56
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• 1997

In emission computed tomography, statistical reconstruction methods in the context of a Bayesian framework have been a topic of interest over the last decade. This was mainly due to the fact that Bayesian approaches can incorporate a priori information into the reconstruction algorithm. Although these approaches can exhibit good performance, their applications to the clinic is hindered mainly by their high computational cost. On the other hand, the speed and simplicity of the filtered backprojection (FBP) algorithm have led to its widespread use in most clinical applications. In this work, we use spline models, which have been quite useful in Bayesian reconstruction, as regularizers for high-frequency apodization in FBP algorithm and show that the effects of using spline models as priors in Bayesian reconstruction can also be achieved in FBP reconstruction.

• Communications for Statistical Applications and Methods
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• v.22 no.2
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• pp.159-172
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• 2015

This paper considers the problem of reconstructing an underlying signal from noisy data. This paper presents a reconstruction method based on synchrosqueezed wavelet transform recently developed for multiscale representation. Synchrosqueezed wavelet transform based on continuous wavelet transform is efficient to estimate the instantaneous frequency of each component that consist of a signal and to reconstruct components. However, an objective selection method for the optimal number of intrinsic mode type functions is required. The proposed method is obtained by coupling the synchrosqueezed wavelet transform with cross-validation scheme. Simulation studies and musical instrument sounds are used to compare the empirical performance of the proposed method with existing methods.

• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.37-46
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• 2023

This study aims to evaluate the usefulness of Deep Learning Image Reconstruction (TrueFidelity, TF), the image quality of existing Filtered Back Projection (FBP) and Adaptive Statistical Iterative Reconstruction-Veo (ASIR-V) were compared. Noise, CNR, and SSIM were measured by obtaining images with doses fixed at 17.29 mGy and altered to 10.37 mGy, 12.10 mGy, 13.83 mGy, and 15.56 mGy in reconstruction techniques of FBP, ASIR-V 50%, and TF-H. TF-H has superior image quality compared to FBP and ASIR-V when the reconstruction technique change is given at 17.29 mGy. When dose changes were made, Noise, CNR, and SSIM were significantly different when comparing 10.37 mGy TF-H and FBP (p<0.05), and no significant difference when comparing 10.37 mGy TF-H and ASIR-V 50% (p>0.05). TF-H has a dose-reduction effect of 30%, as the highest dose of 15.56 mGy ASIR-V has the same image quality as the lowest dose of 10.37 mGy TF-H. Thus, Deep Learning Reconstruction techniques (TF) were able to reduce dose compared to Iterative Reconstruction techniques (ASIR-V) and Filtered Back Projection (FBP). Therefore, it is considered to reduce the exposure dose of patients.

• Archives of Plastic Surgery
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• v.45 no.5
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• pp.441-448
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• 2018

Background Previously, surveys have been used to investigate breast reconstruction statistics. Since 2015, breast reconstruction surgery after mastectomy has been covered by the National Health Insurance Service in Korea, and data from breast reconstruction patients are now available from the Health Insurance Review and Assessment Service (HIRA). We investigated statistics in breast reconstruction in Korea through statistics provided by the HIRA Big Data Hub. Methods We investigated the number of cases in mastectomy and breast reconstruction methods from April 1, 2015 to December 31, 2016. Data were furnished by the HIRA Big Data Hub and accessed remotely online. Results were tabulated using SAS Enterprise version 6.1. Results The 31,155 mastectomy cases included 7,088 breast reconstruction cases. Implant-based methods were used in 4,702 cases, and autologous methods in 2,386. The implant-based reconstructions included 1,896 direct-to-implant and 2,806 tissue-expander (2-stage) breast reconstructions. The 2-stage tissue-expander reconstructions included 1,624 expander insertions (first stage) and 1,182 expander-to-permanent-implant exchanges (second stage). Of the autologous breast reconstructions, 705 involved latissimus dorsi muscle flaps, 498 involved pedicled transverse rectus abdominis myocutaneous (TRAM) flaps, and 1,183 involved free-tissue transfer TRAM flaps, including deep inferior epigastric perforator free-tissue transfer flaps. There were 1,707 nipple-areolar complex reconstructions, including 1,565 nipple reconstructions and 142 areola reconstructions. The 1-year mean number of breast reconstructions was 4,050. Conclusions This was the first attempt to evaluate the total number of breast reconstruction procedures using accurate, comprehensive data, and our findings may prove valuable as a foundation for future statistical studies of breast reconstruction procedures in Korea.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.1111-1116
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• 2009

In this paper, we discuss distortion-tolerant pattern recognition using computational integral imaging reconstruction. Three-dimensional object information is captured by the integral imaging pick-up process. The captured information is numerically reconstructed at arbitrary depth-levels by averaging the corresponding pixels. We apply Fisher linear discriminant analysis combined with principal component analysis to computationally reconstructed images for the distortion-tolerant recognition. Fisher linear discriminant analysis maximizes the discrimination capability between classes and principal component analysis reduces the dimensionality with the minimum mean squared errors between the original and the restored images. The presented methods provide the promising results for the classification of out-of-plane rotated objects.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.46.1-46.1
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• 2016

The formation process of a current sheet is important for solar flare from a viewpoint of a space weather prediction. We therefore derive the temporal development of the spatial and statistical distribution of electric current density distributed in a flare-producing active region to describe the formation of a current sheet. We derive time sequence distribution of electric current density by applying a nonlinear force-free approximation reconstruction to Active Region 12158 that produces an X1.6-class flare. The time sequence maps of photospheric vector magnetic field used for reconstruction are captured by a Helioseismic and Magnetic Imager (HMI) onboard Solar Dynamic Observatory (SDO) on 10th September, 2014. The spatial distribution of electric current density in NLFFF model well reproduce observed sigmoidal structure at the preflare phase, although a layer of high current density shrinks at the postflare phase. A double power-law profile of electric current density is found in statistical analysis. This may be expected to use an indicator of the occurrence of a solar flare.

• The Journal of Korean Physical Therapy
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• v.14 no.1
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• pp.27-37
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• 2002

This is the study of the knee joint injured patients at the orthopaedic surgery clinic where is located in Daejon, who has MCL combine injured ACL reconstruction caused by sport activity and accident during the period from Jan. 2001 to Oct. 2001. By comparing with groups between 7th case of I-group for MCL combined stitch and II-group for ACL reconstruction since 6weeks cast. We have been concluded with that following results. 1. Range of motion for the knee was not limited at 5th case(37%) of I-group, 6th case(42%) of II-group and the cases of Flexion deficit less then 10 -degree were 2nd case(13%) of I-group and II-group 1st case(8%) with no extension deficit more then 5 -degree. 2. The level of activity that tells you whether you are capable of exercise for six month after operation. It han been divided by 3 levels. The case of capable of doing low risk exercise(swimming, cycling, etc.) was 5th case of I-group, the case of capable of doing medium risk exercise(jogging, etc.) was 3rd case of I-group and 4th case of II-group and the case of capable of doing high risk exercise(football, etc.) were 3rd case of I-group and 3rd case of II-group. 3. The timing of the return to their job were average 6.4 weeks for I-group and average 22.9 weeks for II-group(P<.05, statistical difference). 4. There was no statistical difference between I-group and II-group for the timing of the return to their job(P>.05). 5. By using VAS to compare them there was no statistical difference between I-group and II-group of clinical results according to Lysholm scale.

• 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.

• Journal of International Society for Simulation Surgery
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• v.1 no.2
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• pp.57-61
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• 2014

We present a robust 3D facial reconstruction method using a single image generated by face-specific super resolution technique. Based on the several consecutive frames with low resolution, we generate a single high resolution image and a three dimensional facial model based on it. To do this, we apply PME method to compute patch similarities for SR after two-phase warping according to facial attributes. Based on the SRI, we extract facial features automatically and reconstruct 3D facial model with basis which selected adaptively according to facial statistical data less than a few seconds. Thereby, we can provide the facial image of various points of view which cannot be given by a single point of view of a camera.