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

Truncation artifact correction for micro-CT scanners

The work included in this project is framed on one of the lines of research carried out at the Laboratorio de Imagen Médica de la Unidad de Medicina y Cirugía Experimental (UMCE) of Hospital General Universitario Gregorio Marañón and the Bioengineering and Aerospace Department of Universidad Carlos III de Madrid. Its goal is to design, develop and evaluate new data acquisition systems, processing and reconstruction of multimodal images for application in preclinical research. Inside this research line, an x-ray computed tomography (micro-CT add on) system of high resolution has been designed for small animal. Nowadays, computed tomography (CT) is one of the techniques most widely used to obtain anatomical information from living subjects. Different artifacts from different nature usually degrade the qualitative and quantitative analysis of these images. This creates the urgent need of developing algorithms to compensate and/or reduce these artifacts. The general objective of the present thesis is to implement a method for compensating truncation artifact in the micro-CT add-on scanner for small animal developed at Hospital Universitario Gregorio Marañón. This artifact appears due to the acquisition of incomplete x-ray projections when part of the sample, especially obese rats, lies outside the field of view. As a result of these data inconsistencies, bright shading artifacts and quantification errors in the images may appear after the reconstruction process. First of all, truncation artifact in the high resolution micro-CT add-on scanner was studied. Then, after a review of the proposed methods in the literature, the optimal approach for the micro-CT add-on was selected, based on a sinogram extrapolation technique developed by Ohnesorge et al [1]. This method consists on a symmetric mirroring extrapolation of the truncated projections that guarantees continuity at the truncation point. It includes a sine shaping effect that ensures a smooth attenuation signal drop. Truncation artifact correction method has been validated in simulated and real studies. Results show an overall significant reduction of truncation artifact. This algorithm has been adapted and implemented in the reconstruction interface of the preclinical high-resolution micro-CT scanner, which is manufactured by SEDECAL S.L. and commercialized worldwide.El trabajo de este proyecto se encuadra dentro de una línea de investigación que se desarrolla en el Laboratorio de Imagen Médica de la Unidad de Medicina y Cirugía Experimental (UMCE) del Hospital General Universitario Gregorio Marañón y el Departamento de Bioingeniería e Ingeniería Aeroespacial de la Universidad Carlos III de Madrid. Su objetivo es diseñar, desarrollar y evaluar nuevos sistemas de adquisición de datos, procesamiento y reconstrucción de imágenes multi-modales para aplicaciones en investigación preclínica. Dentro de esta línea de investigación se ha desarrollado un tomógrafo de rayos X de alta resolución para pequeños animales (micro-TAC add-on). Actualmente, la tomografía axial computarizada es una de las técnicas más ampliamente utilizadas para la obtención de información anatómica in vivo. Existe una serie de artefactos de distinta naturaleza en este tipo de imágenes que generalmente degradan y dificultan el análisis cualitativo y cuantitativo de las imágenes, dando lugar a una necesidad imperante de desarrollar algoritmos de corrección y/o reducción de estos artefactos. El objetivo general del presente proyecto es la implementación de un algoritmo para la corrección del artefacto de truncamiento en el escáner micro-TAC add-on desarrollado en el Hospital Universitario Gregorio Marañón. Este artefacto aparece debido a la adquisición de proyecciones incompletas cuando parte de la muestra, especialmente ratas obesas, se extiende fuera del campo de visión. Estas inconsistencias en los datos obtenidos pueden dar lugar a la aparición de bandas brillantes y errores en la cuantificación de las imágenes después del proceso de reconstrucción. En primer lugar, se ha estudiado el artefacto de truncamiento en el escáner micro-TAC add-on de alta resolución. Seguidamente, se ha llevado a cabo una revisión de los métodos propuestos en la bibliografía, seleccionando una estrategia óptima para el micro-TAC add-on bajo estudio: una técnica de extrapolación del sinograma publicado por Ohnesorge et al [1]. Este método consiste en una extrapolación de espejo simétrico de las proyecciones truncadas que garantiza la continuidad en el punto de truncamiento. Incluye el modelado de una sinusoide que asegura una caída de señal en los valores de atenuación suave. Este método ha sido validado en estudios simulados y reales. Los resultados muestran una clara reducción del artefacto de truncamiento. El resultado de este proyecto ha sido incorporado en la interfaz de reconstrucción del escáner pre-clínico micro-TAC add-on de alta resolución fabricado por SEDECAL S.A. y comercializado por todo el mundo.Ingeniería Biomédic

Applications of advanced and dual energy computed tomography in proton therapy

This thesis focuses on advanced reconstruction methods and Dual Energy (DE) Computed Tomography (CT) applications for proton therapy, aiming at improving patient positioning and investigating approaches to deal with metal artifacts. To tackle the first goal, an algorithm for post-processing input DE images has been developed. The outputs are tumor- and bone-canceled images, which help in recognising structures in patient body. We proved that positioning error is substantially reduced using contrast enhanced images, thus suggesting the potential of such application. If positioning plays a key role in the delivery, even more important is the quality of planning CT. For that, modern CT scanners offer possibility to tackle challenging cases, like treatment of tumors close to metal implants. Possible approaches for dealing with artifacts introduced by such rods have been investigated experimentally at Paul Scherrer Institut (Switzerland), simulating several treatment plans on an anthropomorphic phantom. In particular, we examined the cases in which none, manual or Iterative Metal Artifact Reduction (iMAR) algorithm were used to correct the artifacts, using both Filtered Back Projection and Sinogram Affirmed Iterative Reconstruction as image reconstruction techniques. Moreover, direct stopping power calculation from DE images with iMAR has also been considered as alternative approach. Delivered dose measured with Gafchromic EBT3 films was compared with the one calculated in Treatment Planning System. Residual positioning errors, daily machine dependent uncertainties and film quenching have been taken into account in the analyses. Although plans with multiple fields seemed more robust than single field, results showed in general better agreement between prescribed and delivered dose when using iMAR, especially if combined with DE approach. Thus, we proved the potential of these advanced algorithms in improving dosimetry for plans in presence of metal implants

Advances in dual-energy computed tomography imaging of radiological properties

Dual-energy computed tomography (DECT) has shown great potential in the reduction of uncertainties of proton ranges and low energy photon cross section estimation used in radiation therapy planning. The work presented herein investigated three contributions for advancing DECT applications. 1) A linear and separable two-parameter DECT, the basis vector model (BVM) was used to estimate proton stopping power. Compared to other nonlinear two-parameter models in the literature, the BVM model shows a comparable accuracy achieved for typical human tissues. This model outperforms other nonlinear models in estimations of linear attenuation coefficients. This is the first study to clearly illustrate the advantages of linear model not only in accurately mapping radiological quantities for radiation therapy, but also in providing a unique model for accurate linear forward projection modelling, which is needed by the statistical iterative reconstruction (SIR) and other advanced DECT reconstruction algorithms. 2) Accurate DECT requires knowledge of x-ray beam properties. Using the Birch-Marshall1 model and beam hardening correction coefficients encoded in a CT scanner’s sinogram header files, an efficient and accurate way to estimate the x-ray spectrum is proposed. The merits of the proposed technique lie in requiring no physical transmission measurement after a one-time calibration against an independently measured spectrum. This technique can also be used in monitoring the aging of x-ray CT tubes. 3) An iterative filtered back projection with anatomical constraint (iFBP-AC) algorithm was also implemented on a digital phantom to evaluate its ability in mitigating beam hardening effects and supporting accurate material decomposition for in vivo imaging of photon cross section and proton stopping power. Compared to iFBP without constraints, both algorithms demonstrate high efficiency of convergence. For an idealized digital phantom, similar accuracy was observed under a noiseless situation. With clinically achievable noise level added to the sinograms, iFBP-AC greatly outperforms iFBP in prediction of photon linear attenuation at low energy, i.e., 28 keV. The estimated mean errors of iFBP and iFBP-AC for cortical bone are 1% and 0.7%, respectively; the standard deviations are 0.6% and 5%, respectively. The achieved accuracy of iFBP-AC shows robustness versus contrast level. Similar mean errors are maintained for muscle tissue. The standard deviation achieved by iFBP-AC is 1.2%. In contrast, the standard deviation yielded by iFBP is about 20.2%. The algorithm of iFBP-AC shows potential application of quantitative measurement of DECT. The contributions in this thesis aim to improve the clinical performance of DECT

Algorithms for enhanced artifact reduction and material recognition in computed tomography

Computed tomography (CT) imaging provides a non-destructive means to examine the interior of an object which is a valuable tool in medical and security applications. The variety of materials seen in the security applications is higher than in the medical applications. Factors such as clutter, presence of dense objects, and closely placed items in a bag or a parcel add to the difficulty of the material recognition in security applications. Metal and dense objects create image artifacts which degrade the image quality and deteriorate the recognition accuracy. Conventional CT machines scan the object using single source or dual source spectra and reconstruct the effective linear attenuation coefficient of voxels in the image which may not provide the sufficient information to identify the occupying materials. In this dissertation, we provide algorithmic solutions to enhance CT material recognition. We provide a set of algorithms to accommodate different classes of CT machines. First, we provide a metal artifact reduction algorithm for conventional CT machines which perform the measurements using single X-ray source spectrum. Compared to previous methods, our algorithm is robust to severe metal artifacts and accurately reconstructs the regions that are in proximity to metal. Second, we propose a novel joint segmentation and classification algorithm for dual-energy CT machines which extends prior work to capture spatial correlation in material X-ray attenuation properties. We show that the classification performance of our method surpasses the prior work's result. Third, we propose a new framework for reconstruction and classification using a new class of CT machines known as spectral CT which has been recently developed. Spectral CT uses multiple energy windows to scan the object, thus it captures data across higher energy dimensions per detector. Our reconstruction algorithm extracts essential features from the measured data by using spectral decomposition. We explore the effect of using different transforms in performing the measurement decomposition and we develop a new basis transform which encapsulates the sufficient information of the data and provides high classification accuracy. Furthermore, we extend our framework to perform the task of explosive detection. We show that our framework achieves high detection accuracy and it is robust to noise and variations. Lastly, we propose a combined algorithm for spectral CT, which jointly reconstructs images and labels each region in the image. We offer a tractable optimization method to solve the proposed discrete tomography problem. We show that our method outperforms the prior work in terms of both reconstruction quality and classification accuracy

Segmentation-Free Statistical Image Reconstruction for Polyenergetic X-Ray Computed Tomography with Experimental Validation

This paper describes a statistical image reconstruction method for x-ray CT that is based on a physical model that accounts for the polyenergetic x-ray source spectrum and the measurement nonlinearities caused by energy-dependent attenuation. Unlike our earlier work, the proposed algorithm does not require pre-segmentation of the object into the various tissue classes (e.g., bone and soft tissue) and allows mixed pixels. The attenuation coefficient of each voxel is modelled as the product of its unknown density and a weighted sum of energy-dependent mass attenuation coefficients. We formulate a penalized-likelihood function for this polyenergetic model and develop an iterative algorithm for estimating the unknown density of each voxel. Applying this method to simulated x-ray CT measurements of objects containing both bone and soft tissue yields images with significantly reduced beam hardening artefacts relative to conventional beam hardening correction methods. We also apply the method to real data acquired from a phantom containing various concentrations of potassium phosphate solution. The algorithm reconstructs an image with accurate density values for the different concentrations, demonstrating its potential for quantitative CT applications.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85911/1/Fessler66.pd