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

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

PND-Net: Physics based Non-local Dual-domain Network for Metal Artifact Reduction

Metal artifacts caused by the presence of metallic implants tremendously degrade the reconstructed computed tomography (CT) image quality, affecting clinical diagnosis or reducing the accuracy of organ delineation and dose calculation in radiotherapy. Recently, deep learning methods in sinogram and image domains have been rapidly applied on metal artifact reduction (MAR) task. The supervised dual-domain methods perform well on synthesized data, while unsupervised methods with unpaired data are more generalized on clinical data. However, most existing methods intend to restore the corrupted sinogram within metal trace, which essentially remove beam hardening artifacts but ignore other components of metal artifacts, such as scatter, non-linear partial volume effect and noise. In this paper, we mathematically derive a physical property of metal artifacts which is verified via Monte Carlo (MC) simulation and propose a novel physics based non-local dual-domain network (PND-Net) for MAR in CT imaging. Specifically, we design a novel non-local sinogram decomposition network (NSD-Net) to acquire the weighted artifact component, and an image restoration network (IR-Net) is proposed to reduce the residual and secondary artifacts in the image domain. To facilitate the generalization and robustness of our method on clinical CT images, we employ a trainable fusion network (F-Net) in the artifact synthesis path to achieve unpaired learning. Furthermore, we design an internal consistency loss to ensure the integrity of anatomical structures in the image domain, and introduce the linear interpolation sinogram as prior knowledge to guide sinogram decomposition. Extensive experiments on simulation and clinical data demonstrate that our method outperforms the state-of-the-art MAR methods.Comment: 19 pages, 8 figure

Simplified statistical image reconstruction for X-ray CT with beam-hardening artifact compensation

CT images are often affected by beam-hardening artifacts due to the polychromatic nature of the X-ray spectra. These artifacts appear in the image as cupping in homogeneous areas and as dark bands between dense regions, such as bones. This paper proposes a simplified statistical reconstruction method for X-ray CT based on Poisson statistics that accounts for the non-linearities caused by beam hardening. The main advantages of the proposed method over previous algorithms is that it avoids the preliminary segmentation step, which can be tricky, especially for low-dose scans, and it does not require knowledge of the whole source spectrum, which is often unknown. Each voxel attenuation is modeled as a mixture of bone and soft tissue by defining density-dependent tissue fractions, maintaining one unknown per voxel. We approximate the energy-dependent attenuation corresponding to different combinations of bone and soft tissue, so called beam-hardening function, with the 1D function corresponding to water plus two parameters that can be tuned empirically. Results on both simulated data with Poisson sinogram noise and two rodent studies acquired with the ARGUSCT system showed a beam hardening reduction (both cupping and dark bands) similar to analytical reconstruction followed by post-processing techniques, but with reduced noise and streaks in cases with low number of projections, as expected for statistical image reconstruction.This work was partially funded by NIH grants R01-HL-098686 and U01 EB018753, by Spanish Ministerio de Economia y Competitividad (projects TEC2013-47270-R and RTC-2014-3028-1) and the Spanish Ministerio de Economia, Industria y Competitividad (projects DPI2016-79075-R AEI/FEDER, UE - Agencia Estatal de Investigación and DTS17/00122 Instituto de Salud Carlos III - FIS), and co-financed by ERDF (FEDER) Funds from the European Commission, “A way of making Europe”. The CNIC is supported by the Spanish Ministerio de Economia, Industria y Competitividad and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505).En prens