Non-blind deconvolution with an alternating direction method of multipliers (ADMM) after noise reduction in nondestructive testing

Abstract: To ensure the quality of material, nondestructive testing is necessary, and radiography testing is the nondestructive technique most commonly used today. For inspection, the quality of a radiographic image is critical, and there are many image artifacts that can reduce inspection accuracies such as noise or blurring. The deterioration in spatial resolution caused by blur in both the X-ray imaging itself and the noise reduction process are particular problems. To tackle them, we implemented a non-blind deconvolution method that employs the alternating direction method of multipliers (ADMM) after noise reduction. Experimental results confirm that the proposed algorithm effectively restores edge sharpness. The 50% modulation transfer function of the restored image of a slit-camera was about 3.54 line-pairs per mm, which is about 2.5 times higher than that of the denoised image. Moreover, the edge preservation index values are about 0.82, 0.54, and 0.75 for the restored, denoised, and acquired images, respectively. Consequentially, the proposed method has the potential to increase inspection efficiency in industrial applications.ope

Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging

The global pandemic of coronavirus disease 2019 (COVID-19) has resulted in an increased demand for testing, diagnosis, and treatment. Reverse transcription polymerase chain reaction (RT-PCR) is the definitive test for the diagnosis of COVID-19; however, chest X-ray radiography (CXR) is a fast, effective, and affordable test that identifies the possible COVID-19-related pneumonia. This study investigates the feasibility of using a deep learning-based decision-tree classifier for detecting COVID-19 from CXR images. The proposed classifier comprises three binary decision trees, each trained by a deep learning model with convolution neural network based on the PyTorch frame. The first decision tree classifies the CXR images as normal or abnormal. The second tree identifies the abnormal images that contain signs of tuberculosis, whereas the third does the same for COVID-19. The accuracies of the first and second decision trees are 98 and 80%, respectively, whereas the average accuracy of the third decision tree is 95%. The proposed deep learning-based decision-tree classifier may be used in pre-screening patients to conduct triage and fast-track decision making before RT-PCR results are available.ope

Single-scan patient-specific scatter correction in computed tomography using peripheral detection of scatter and compressed sensing scatter retrieval

PURPOSE: X-ray scatter results in a significant degradation of image quality in computed tomography (CT), representing a major limitation in cone-beam CT (CBCT) and large field-of-view diagnostic scanners. In this work, a novel scatter estimation and correction technique is proposed that utilizes peripheral detection of scatter during the patient scan to simultaneously acquire image and patient-specific scatter information in a single scan, and in conjunction with a proposed compressed sensing scatter recovery technique to reconstruct and correct for the patient-specific scatter in the projection space. METHODS: The method consists of the detection of patient scatter at the edges of the field of view (FOV) followed by measurement based compressed sensing recovery of the scatter through-out the projection space. In the prototype implementation, the kV x-ray source of the Varian TrueBeam OBI system was blocked at the edges of the projection FOV, and the image detector in the corresponding blocked region was used for scatter detection. The design enables image data acquisition of the projection data on the unblocked central region of and scatter data at the blocked boundary regions. For the initial scatter estimation on the central FOV, a prior consisting of a hybrid scatter model that combines the scatter interpolation method and scatter convolution model is estimated using the acquired scatter distribution on boundary region. With the hybrid scatter estimation model, compressed sensing optimization is performed to generate the scatter map by penalizing the L1 norm of the discrete cosine transform of scatter signal. The estimated scatter is subtracted from the projection data by soft-tuning, and the scatter-corrected CBCT volume is obtained by the conventional Feldkamp-Davis-Kress algorithm. Experimental studies using image quality and anthropomorphic phantoms on a Varian TrueBeam system were carried out to evaluate the performance of the proposed scheme. RESULTS: The scatter shading artifacts were markedly suppressed in the reconstructed images using the proposed method. On the Catphan©504 phantom, the proposed method reduced the error of CT number to 13 Hounsfield units, 10% of that without scatter correction, and increased the image contrast by a factor of 2 in high-contrast regions. On the anthropomorphic phantom, the spatial nonuniformity decreased from 10.8% to 6.8% after correction. CONCLUSIONS: A novel scatter correction method, enabling unobstructed acquisition of the high frequency image data and concurrent detection of the patient-specific low frequency scatter data at the edges of the FOV, is proposed and validated in this work. Relative to blocker based techniques, rather than obstructing the central portion of the FOV which degrades and limits the image reconstruction, compressed sensing is used to solve for the scatter from detection of scatter at the periphery of the FOV, enabling for the highest quality reconstruction in the central region and robust patient-specific scatter correction.ope

Evaluation of Fabricated Semiconductor Sensor for Verification of γ-ray Distribution in Brachytherapy

In radiation therapy fields, a brachytherapy is a treatment that kills lesion of cells by inserting a radioisotope that keeps emitting radiation into the body. We currently verify the consistency of radiation treatment plan and dose distribution through film/screen system (F/S system), provide therapy after checking dose. When we check dose distribution, F/S systems have radiation signal distortion because there is low resolution by penumbra depending on the condition of film developed. In this study, We fabricated a HgI2 Semiconductor radiation sensor for base study in order that we verify the real dose distribution weather it's same as plans or not in brachytherapy. Also, we attempt to evaluate the feasibility of QA system by utilizing and evaluating the sensor to bracytherapy source. As shown in the result of detected signal with various source-to-detector distance (SDD), we quantitatively verified the real range of treatment which is also equivalent to treatment plans because only the low signal estimated as scatters was measured beyond the range of treatment. And the result of experiment that we access reproducibility on the same condition of γ-ray, we have made sure that the CV (coefficient of variation) is within 1.5 percent so we consider that the HgI2 sensor is available at QA of brachytherapy based on the result.ope

Hybrid model-based and deep learning-based metal artifact reduction method in dental cone-beam computed tomography

Objective: To present a hybrid approach that incorporates a constrained beam-hardening estimator (CBHE) and deep learning (DL)-based post-refinement for metal artifact reduction in dental cone-beam computed tomography (CBCT). Methods: Constrained beam-hardening estimator (CBHE) is derived from a polychromatic X-ray attenuation model with respect to X-ray transmission length, which calculates associated parameters numerically. Deep-learning-based post-refinement with an artifact disentanglement network (ADN) is performed to mitigate the remaining dark shading regions around a metal. Artifact disentanglement network (ADN) supports an unsupervised learning approach, in which no paired CBCT images are required. The network consists of an encoder that separates artifacts and content and a decoder for the content. Additionally, ADN with data normalization replaces metal regions with values from bone or soft tissue regions. Finally, the metal regions obtained from the CBHE are blended into reconstructed images. The proposed approach is systematically assessed using a dental phantom with two types of metal objects for qualitative and quantitative comparisons. Results: The proposed hybrid scheme provides improved image quality in areas surrounding the metal while preserving native structures. Conclusion: This study may significantly improve the detection of areas of interest in many dentomaxillofacial applications. © 2023 Korean Nuclear Societyope

Anisotropic Total Variation Denoising Technique for Low-Dose Cone-Beam Computed Tomography Imaging

This study aims to develop an improved Feldkamp-Davis-Kress (FDK) reconstruction algorithm using anisotropic total variation (ATV) minimization to enhance the image quality of low-dose conebeam computed tomography (CBCT). The algorithm first applies a filter that integrates the Shepp- Logan filter into a cosine window function on all projections for impulse noise removal. A total variation objective function with anisotropic penalty is then minimized to enhance the difference between the real structure and noise using the steepest gradient descent optimization with adaptive step sizes. The preserving parameter to adjust the separation between the noise-free and noisy areas is determined by calculating the cumulative distribution function of the gradient magnitude of the filtered image obtained by the application of the filtering operation on each projection. With these minimized ATV projections, voxel-driven backprojection is finally performed to generate the reconstructed images. The performance of the proposed algorithm was evaluated with the catphan503 phantom dataset acquired with the use of a low-dose protocol. Qualitative and quantitative analyses showed that the proposed ATV minimization provides enhanced CBCT reconstruction images compared with those generated by the conventional FDK algorithm, with a higher contrast-to-noise ratio (CNR), lower root-mean-square-error, and higher correlation. The proposed algorithm not only leads to a potential imaging dose reduction in repeated CBCT scans via lower mA levels, but also elicits high CNR values by removing noisy corrupted areas and by avoiding the heavy penalization of striking features.ope

Low-dose CBCT reconstruction via joint non-local total variation denoising and cubic B-spline interpolation

This study develops an improved Feldkamp-Davis-Kress (FDK) reconstruction algorithm using non-local total variation (NLTV) denoising and a cubic B-spline interpolation-based backprojector to enhance the image quality of low-dose cone-beam computed tomography (CBCT). The NLTV objective function is minimized on all log-transformed projections using steepest gradient descent optimization with an adaptive control of the step size to augment the difference between a real structure and noise. The proposed algorithm was evaluated using a phantom data set acquired from a low-dose protocol with lower milliampere-seconds (mAs).The combination of NLTV minimization and cubic B-spline interpolation rendered the enhanced reconstruction images with significantly reduced noise compared to conventional FDK and local total variation with anisotropic penalty. The artifacts were remarkably suppressed in the reconstructed images. Quantitative analysis of reconstruction images using low-dose projections acquired from low mAs showed a contrast-to-noise ratio with spatial resolution comparable to images reconstructed using projections acquired from high mAs. The proposed approach produced the lowest RMSE and the highest correlation. These results indicate that the proposed algorithm enables application of the conventional FDK algorithm for low mAs image reconstruction in low-dose CBCT imaging, thereby eliminating the need for more computationally demanding algorithms. The substantial reductions in radiation exposure associated with the low mAs projection acquisition may facilitate wider practical applications of daily online CBCT imaging.ope

A Study on Acceptance of Voice-based Virtual Secretary in Smart Home

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 협동과정 기술경영·경제·정책전공, 2018. 8. 황준석.2010년 이후부터 연결 가능한 IoT기기들이 인구 수를 넘어서며 그 수는 기하급수적으로 증가하는 상황에서 IoT기기들의 통합 및 플랫폼화가 진행되고 있다. 개인 가정 측면에서도 Smart Home Appliance들의 통합이 요구되는 가운데 스마트홈 산업이 빠르게 성장하고 있으며 스마트홈의 허브로서 음성가상비서가 독보적인 기술로 자리매김하고 있다. 본 연구는 스마트홈의 음성가상비서의 캐즘발생에 대한 가능성에서 출발하여 기술수용모델(TAM)을 이용하여 스마트홈의 음성가상비서 수용의 결정요인을 식별하고 캐즘 전, 후의 혁신집단의 차이를 보기 위해 혁신고집단과 혁신저집단으로 나누어 다중집단분석을 통해 영향도차이를 보는 것을 논문의 목적과 연구방법으로 설정하였다. 연구는 기술수용모델(TAM)에 Perceived Risk를 포함한 확장된 형태의 모델을 차용하여 독립변수로서 커뮤니케이션과 비서적요소인 감정적 대화 유도성, 비서적 기능성, 통합적 미디어 제공 기능성과 스마트홈 제어와 기능적 요소인 스마트홈 가전의 제어의 정도 및 호환성, 경비/감시 기능성과 리스크적 요소인 사생활 침해 위험, 재무적 위험, 기능적 위험을 설정하였다. 또한 혁신성을 DSI기준으로 측정하여 전체집단을 혁신성의 평균으로 나눠 혁신고집단과 혁신저집단으로 구분하였다. 전체 집단 분석결과 비서적 기능성, 스마트홈 기기 제어의 정도 및 호환성이 유용성과 용이성에 모두 영향을 미쳐 중요한 요인으로 파악이 되었고, 통합적 미디어 제공의 기능성, 경비/감시 기능성, 기능적 위협이 사용유용성에 영향을 미치는 요인으로 분석되었다. 또한 유용성과 용이성 중 사용의도에 유용성만 유의미한 영향을 미쳐 수용자들이 편리성보다 목적성을 더 중시한다는 것을 알 수 있었고, 재무적 위험이 수용의도에 직접적인 악영향을 주는 것을 알 수 있었다. 혁신고집단과 혁신저집단의 영향도 차이 분석의 결과로, 스마트홈 기기 제어의 정도 및 호환성이 혁신저집단에서만 중요한 요소로 작용함을 알 수 있었고, 경비/감시 기능성, 통합적 미디어 제공의 기능성이 혁신고집단에서 긍정적 요소로 작용함을 알 수 있었다. 전체집단 분석과 마찬가지로 혁식저집단에서는 재무적 위험이 사용의도에 부정적 영향을 주었지만 혁신고집단에서는 오히려 사용유용성에 긍정적인 영향을 주었다. 본 연구는 스마트홈 음성가상비서의 수용에 미치는 요인들을 분석한 효시적 연구이며 제품자체가 고유하게 가지고 있는 속성에 초점을 맞춰 스마트홈 음성가상비서의 수용요인 분석에 특화된 논문이라는 학술적 의의가 있으며, 기본적인 제품 수용에 영향을 미치는 요인들을 분석하는 것을 물론, 혁신고집단과 혁신저집단의 다중집단 분석으로 캐즘의 경계의 전기 집단과 후기집단의 차이를 분석하여 구체적인 전략에 대해 통찰을 제공한다는 점에서 산업적인 의의 또한 있다.초 록 iii 목 차 v 표 목차 viii 그림 목차 ix 1. 서론 1 1.1 연구배경 1 1.2 연구목적 6 2. 이론적 배경 7 2.1 스마트홈의 음성가상 비서 7 2.1.1 가상비서의 정의 7 2.1.2 스마트홈과 음성가상비서의 관계 및 스마트홈의 음성가상 비서의 정의 8 2.2 스마트홈의 음성가상비서와 캐즘 9 2.2.1 스마트홈 음성가상비서와 캐즘의 관계 13 2.2.2 캐즘과 개인의 혁신성과의 관계 및 연구 14 2.3 신기술 수용의 요인을 찾기 위한 연구모형 17 2.3.1 기술수용모델(TAM, Technology Acceptance Model) 17 2.3.2 Featherman 기술수용모델의 개념 및 유용성 20 2.4 외부변수에 대한 고찰 21 2.4.1 커뮤니케이션과 비서적 요소 21 2.4.2 스마트홈 제어와 기능적 요소 23 2.4.3 리스크적 요소 25 2.4.4 변수의 조작적 정의 26 3. 연구설계 30 3.1 연구모형 30 3.2 연구가설 설정 31 3.2.1 커뮤니케이션과 비서적 요소의 가설 31 3.2.2 스마트홈 제어와 기능적 요소의 가설 33 3.2.3 리스크 요소의 가설 34 3.2.4 지각된 사용유용성, 사용용이성과 사용의도의 가설 35 4. 분석결과 36 4.1 인구통계학적 특성 36 4.2 타당성 및 신뢰도 검증 38 4.2.1 변수들의 탐색적 요인분석 39 4.2.2 변수들의 확인적 요인분석 46 4.3 판별타당성 51 4.4 연구모형의 분석결과 53 4.5 간접효과 검증 60 4.6 혁신집단에 따른 다중집단분석 61 5. 결론 65 5.1 연구 요약 65 5.2 연구 시사점 67 5.3 연구의 한계 및 향후 연구 70 참 고 문 헌(국내) 72 참 고 문 헌(해외) 78 부록 1 : 설문조사 85 Abstract 99Maste

Feasibility of hybrid TomoHelical- and TomoDirect-based volumetric gradient matching technique for total body irradiation

Background: Tomotherapy-based total body irradiation (TBI) is performed using the head-first position (HFP) and feet-first position (FFP) due to treatment length exceeding the 135 cm limit. To reduce the dosimetric variation at the match lines, we propose and verify a volumetric gradient matching technique (VGMT) by combining TomoHelical (TH) and TomoDirect (TD) modes. Methods: Two planning CT image sets were acquired with HFP and FFP using 15 × 55 × 18 cm3 of solid water phantom. Planning target volume (PTV) was divided into upper, lower, and gradient volumes. The junction comprised 2-cm thick five and seven gradient volumes (5-GVs and 7-GVs) to create a dose distribution with a gentle slope. TH-IMRT and TD-IMRT plans were generated with 5-GVs and 7-GVs. The setup error in the calculated dose was assessed by shifting dose distribution of the FFP plan by 5, 10, 15, and 20 mm in the longitudinal direction and comparing it with the original. Doses for 95% (D95) and 5% of the PTV (D5) were calculated for all simulated setup error plans. Absolute dose measurements were performed using an ionization chamber in the junction. Results: The TH&TD plan produced a linear gradient in junction volume, comparable to that of the TH&TH plan. D5 of the PTV was 110% of the prescribed dose when the FFP plan was shifted 0.7 cm and 1.2 cm in the superior direction for 5-GVs and 7-GVs. D95 of the PTV decreased to < 90% of the prescribed dose when the FF plan was shifted 1.1 cm and 1.3 cm in the inferior direction for 5-GVs and 7-GVs. The absolute measured dose showed a good correlation with the calculated dose in the gradient junction volume. The average percent difference (±SD) in all measured points was − 0.7 ± 1.6%, and the average dose variations between depths was − 0.18 ± 1.07%. Conclusion: VGMT can create a linear dose gradient across the junction area in both TH&TH and TH&TD and can minimize the dose sensitivity to longitudinal setup errors in tomotherapy-based TBI.ope

Compact bunker shielding assessment for 1.5 T MR-Linac

This study evaluated the effect of the 1.5 T magnetic field of the magnetic resonance-guided linear accelerator (MR-Linac) on the radiation leakage doses penetrating the bunker radiation shielding wall. The evaluated 1.5 T MR-Linac Unity system has a bunker of the minimum recommended size. Unlike a conventional Linac, both primary beam transmission and secondary beam leakage were considered independently in the design and defined at the machine boundary away from the isocenter. Moreover, additional shielding was designed considering the numerous ducts between the treatment room and other rooms. The Linac shielding was evaluated by measuring the leakage doses at several locations. The intrinsic vibration and magnetic field were inspected at the proposed isocenter of the system. For verification, leakage doses were measured before and after applying the magnetic field. The intrinsic vibration and magnetic field readings were below the permitted limit. The leakage dose (0.05-12.2 µSv/week) also complied with internationally stipulated limits. The special shielding achieved a five-fold reduction in leakage dose. Applying the magnetic field increased the leakage dose by 0.12 to 4.56 µSv/week in several measurement points, although these values fall within experimental uncertainty. Thus, the effect of the magnetic field on the leakage dose could not be ascertained.ope