CT 상의 금속 허상물 제거를 위한 효율적인 빔 경화 교정 알고리즘

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2021. 2. 신영길.빔경화는 다색 X선을 사용하고 에너지 의존적인 물질 감쇠 계수를 이용하는 CT 시스템의 특성상 불가피한 현상이며, 이는 특히 금속 영역을 포함하는 프로젝션 상의 값을 오측정하여 결과적으로 CT 영상에 허상물을 유발한다. 금속 허상물 저감화는 CT 영상에 존재하는 이러한 허상물을 제거하고 가려진 실제 정보를 복원하는 과정이다. 영상을 통한 진단과 방사선치료를 위한 계획 수립에 있어서 정확한 CT 영상을 획득하기 위해 금속 허상물의 제거는 필수적이다. 반복적인 재구성에 의한 수치적 방법에 기반을 둔 효과적인 금속 허상물 제거에 관한 최신 연구들이 발표되었으나 무거운 계산량으로 인해 임상 실습에 적용이 어려운 상황이다. 본 논문에서는 이러한 계산적인 이슈를 해결하기 위한 효율적인 빔 경화 추정 모델과 이를 이용한 금속 허상물 저감화 방법을 제안한다. 제안한 모델은 금속 물체의 기하정보와 다색 X선이 물체를 통과하면서 발생하는 빔경화의 물리적인 특성을 반영한다. 모델에 필요한 대부분의 매개변수들은 수치학적인 방법으로 교정 전의 CT 영상과 CT 시스템으로부터 추가적인 최적화 과정 없이 획득한다. 빔경화 허상물과 관련된 매개 변수 중 단 하나만 재구성 이후의 영상 단계에서 선형 최적화를 통해 탐색된다. 또한 제안한 방법으로 교정된 결과 영상에 잔존하는 허상물들을 제거하기 위한 추가적인 두가지 개선 방법을 제시한다. 다수의 시뮬레이션 데이터와 실제 데이터를 사용하여 정성적 및 정량적 비교를 통해 제안 기법의 유효성이 체계적으로 평가되었다. 제안 알고리즘은 정확성 및 견고성 측면에서 유의미한 결과를 보여주었고, 기존의 기법들에 비해 향상된 결과 영상의 품질 뿐만 아니라 임상적으로 적용할만한 빠른 수행 시간을 보여주었다. 이 연구는 CT 영상을 통한 진단과 방사능 치료의 계획 수립을 위한 정확성 향상에 유의미한 의미를 갖는다.Beam hardening in X-ray computed tomography (CT) is an inevitable problem due to the characteristics of CT system that uses polychromatic X-rays and energy-dependent attenuation coefficients of materials. It causes artifacts in CT images as the result of underestimation on the projection data, especially on metal regions. Metal artifact reduction is the process of reducing the artifacts in CT and restoring the actual information hidden by the artifacts. In order to obtain exact CT images for more accurate diagnosis and treatment planning on radiotherapy in clinical fields, it is essential to reduce metal artifacts. State-of-the-art approaches on effectively reducing metal artifact based on numerical methods by iterative reconstruction have been presented. However, it is difficult to be applied in clinical practice due to a heavy computational burden. In this dissertation, we proposes an efficient beam-hardening estimation model and a metal artifact reduction method using this model to address this computational issue. The proposed model reflects the geometric information of metal objects and physical characteristics of beam hardening during the transmission of polychromatic X-ray through a material. Most of the associated parameters are numerically obtained from an initial uncorrected CT image and CT system without additional optimization. Only the unknown parameter related to beam-hardening artifact is fine-tuned by linear optimization, which is performed only in the reconstruction image domain. Two additional refinement methods are presented to reduce residual artifacts in the result image corrected by the proposed metal artifact reduction method. The effectiveness of the proposed method was systematically assessed through qualitative and quantitative comparisons using numerical simulations and real data. The proposed algorithm showed significant results in the aspects of accuracy and robustness. Compared to existing methods, it showed improved image quality as well as fast execution time that is clinically applicable. This work may have significant implications in improving the accuracy of diagnosis and treatment planning for radiotheraphy through CT imaging.Chapter 1 Introduction 1 1.1 Background and motivation 1 1.2 Scope and aim 5 1.3 Main contribution 6 1.4 Contents organization 8 Chapter 2 Related Works 9 2.1 CT physics 9 2.1.1 Fundamentals of X-ray 10 2.1.2 CT reconstruction algorithms 13 2.2 CT artifacts 18 2.2.1 Physics-based artifacts 19 2.2.2 Patient-based artifacts 21 2.3 Metal artifact reduction 22 2.3.1 Sinogram-completion based MAR 24 2.3.2 Sinogram-correction based MAR 27 2.3.3 Deep-learning based MAR 29 2.4 Summary 31 Chapter 3 Constrained Beam-hardening Estimator for Polychromatic X-ray 33 3.1 Characteristics of polychromatic X-ray 34 3.2 Constrained beam-hardening estimator 35 3.3 Summary 41 Chapter 4 Metal Artifact Reduction with Constrained Beam-hardening Estimator 43 4.1 Metal segmentation 44 4.2 X-ray transmission length 46 4.3 Artifact reduction with CBHE 48 4.3.1 Artifact estimation for a single type of metal 48 4.3.2 Artifact estimation for multiple types of metal 51 4.4 Refinement methods 54 4.4.1 Collaboration with ADN 54 4.4.2 Application of CBHE to bone 57 4.5 Summary 59 Chapter 5 Experimental Results 61 5.1 Data preparation and quantitative measures 62 5.2 Verification on constrained beam-hardening estimator 67 5.2.1 Accuracy 67 5.2.2 Robustness 72 5.3 Performance evaluations 81 5.3.1 Evaluation with simulated phantoms 81 5.3.2 Evaluation with hardware phantoms 86 5.3.3 Evaluation on refinement methods 91 Chapter 6 Conclusion 95 Bibliography 101 초록 115 Acknowledgements 117Docto

Metal Artifact Reduction in Sinograms of Dental Computed Tomography

Use of metal objects such as dental implants, fillings, crowns, screws, nails, prosthesis and plates have increased in dentistry over the past 20 years, which raised a need for new methods for reducing the metal artifacts in medical images. Although there are several algorithms for metal artifact reduction, none of these algorithms are efficient enough to recover the original image free of all artifacts. This thesis presents two approaches for reducing metal artifacts through accurate segmentation of metal objects on dental computed tomography images. First approach was based on construction and tilting of a 3D jaw phantom, aiming to obtain fewer metals on each slice. 3D jaw phantom included the main anatomical structures of a jaw, and multiple metal fillings inserted on the teeth. Each jaw slice on the 3D phantom was tilted in order to mimic the (1) nodding movement, and (2) mouth opening/closing. Second approach was to segment the metals on an experimental dataset, consisting of a Cone-Beam Computed Tomography image, by using different segmentation algorithms. K-means clustering, Otsu’s thresholding method and logarithmic enhancement were used for extracting the metals from a real dental CT slice. Once the metal fillings on the jaw phantom were segmented out from the image, they were compensated by gap filling methods; Discrete Cosine Domain Gap Filling and inpainting. Qualitative and quantitative analyses were carried out for evaluating the performance of implemented segmentation methods. Efficiency of tilting alternatives on the segmentation of metal fillings was compared. In conclusion, jaw opening/closing movement between 24º-30º suggested a significant enhancement in segmentation, thus, metal artifact reduction on the jaw phantom. Inpainting method showed a better performance for both simulated and experimental dataset over the DCT domain gap filling method. Moreover, merging the logarithmic enhancement and inpainting showed superior results over other metal artifact reduction alternatives

Adaptive Target Recognition: A Case Study Involving Airport Baggage Screening

This work addresses the question whether it is possible to design a computer-vision based automatic threat recognition (ATR) system so that it can adapt to changing specifications of a threat without having to create a new ATR each time. The changes in threat specifications, which may be warranted by intelligence reports and world events, are typically regarding the physical characteristics of what constitutes a threat: its material composition, its shape, its method of concealment, etc. Here we present our design of an AATR system (Adaptive ATR) that can adapt to changing specifications in materials characterization (meaning density, as measured by its x-ray attenuation coefficient), its mass, and its thickness. Our design uses a two-stage cascaded approach, in which the first stage is characterized by a high recall rate over the entire range of possibilities for the threat parameters that are allowed to change. The purpose of the second stage is to then fine-tune the performance of the overall system for the current threat specifications. The computational effort for this fine-tuning for achieving a desired PD/PFA rate is far less than what it would take to create a new classifier with the same overall performance for the new set of threat specifications