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    CT ์ƒ์˜ ๊ธˆ์† ํ—ˆ์ƒ๋ฌผ ์ œ๊ฑฐ๋ฅผ ์œ„ํ•œ ํšจ์œจ์ ์ธ ๋น” ๊ฒฝํ™” ๊ต์ • ์•Œ๊ณ ๋ฆฌ์ฆ˜

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2021. 2. ์‹ ์˜๊ธธ.๋น”๊ฒฝํ™”๋Š” ๋‹ค์ƒ‰ X์„ ์„ ์‚ฌ์šฉํ•˜๊ณ  ์—๋„ˆ์ง€ ์˜์กด์ ์ธ ๋ฌผ์งˆ ๊ฐ์‡  ๊ณ„์ˆ˜๋ฅผ ์ด์šฉํ•˜๋Š” CT ์‹œ์Šคํ…œ์˜ ํŠน์„ฑ์ƒ ๋ถˆ๊ฐ€ํ”ผํ•œ ํ˜„์ƒ์ด๋ฉฐ, ์ด๋Š” ํŠนํžˆ ๊ธˆ์† ์˜์—ญ์„ ํฌํ•จํ•˜๋Š” ํ”„๋กœ์ ์…˜ ์ƒ์˜ ๊ฐ’์„ ์˜ค์ธก์ •ํ•˜์—ฌ ๊ฒฐ๊ณผ์ ์œผ๋กœ CT ์˜์ƒ์— ํ—ˆ์ƒ๋ฌผ์„ ์œ ๋ฐœํ•œ๋‹ค. ๊ธˆ์† ํ—ˆ์ƒ๋ฌผ ์ €๊ฐํ™”๋Š” CT ์˜์ƒ์— ์กด์žฌํ•˜๋Š” ์ด๋Ÿฌํ•œ ํ—ˆ์ƒ๋ฌผ์„ ์ œ๊ฑฐํ•˜๊ณ  ๊ฐ€๋ ค์ง„ ์‹ค์ œ ์ •๋ณด๋ฅผ ๋ณต์›ํ•˜๋Š” ๊ณผ์ •์ด๋‹ค. ์˜์ƒ์„ ํ†ตํ•œ ์ง„๋‹จ๊ณผ ๋ฐฉ์‚ฌ์„ ์น˜๋ฃŒ๋ฅผ ์œ„ํ•œ ๊ณ„ํš ์ˆ˜๋ฆฝ์— ์žˆ์–ด์„œ ์ •ํ™•ํ•œ CT ์˜์ƒ์„ ํš๋“ํ•˜๊ธฐ ์œ„ํ•ด ๊ธˆ์† ํ—ˆ์ƒ๋ฌผ์˜ ์ œ๊ฑฐ๋Š” ํ•„์ˆ˜์ ์ด๋‹ค. ๋ฐ˜๋ณต์ ์ธ ์žฌ๊ตฌ์„ฑ์— ์˜ํ•œ ์ˆ˜์น˜์  ๋ฐฉ๋ฒ•์— ๊ธฐ๋ฐ˜์„ ๋‘” ํšจ๊ณผ์ ์ธ ๊ธˆ์† ํ—ˆ์ƒ๋ฌผ ์ œ๊ฑฐ์— ๊ด€ํ•œ ์ตœ์‹  ์—ฐ๊ตฌ๋“ค์ด ๋ฐœํ‘œ๋˜์—ˆ์œผ๋‚˜ ๋ฌด๊ฑฐ์šด ๊ณ„์‚ฐ๋Ÿ‰์œผ๋กœ ์ธํ•ด ์ž„์ƒ ์‹ค์Šต์— ์ ์šฉ์ด ์–ด๋ ค์šด ์ƒํ™ฉ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๊ณ„์‚ฐ์ ์ธ ์ด์Šˆ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ํšจ์œจ์ ์ธ ๋น” ๊ฒฝํ™” ์ถ”์ • ๋ชจ๋ธ๊ณผ ์ด๋ฅผ ์ด์šฉํ•œ ๊ธˆ์† ํ—ˆ์ƒ๋ฌผ ์ €๊ฐํ™” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•œ ๋ชจ๋ธ์€ ๊ธˆ์† ๋ฌผ์ฒด์˜ ๊ธฐํ•˜์ •๋ณด์™€ ๋‹ค์ƒ‰ X์„ ์ด ๋ฌผ์ฒด๋ฅผ ํ†ต๊ณผํ•˜๋ฉด์„œ ๋ฐœ์ƒํ•˜๋Š” ๋น”๊ฒฝํ™”์˜ ๋ฌผ๋ฆฌ์ ์ธ ํŠน์„ฑ์„ ๋ฐ˜์˜ํ•œ๋‹ค. ๋ชจ๋ธ์— ํ•„์š”ํ•œ ๋Œ€๋ถ€๋ถ„์˜ ๋งค๊ฐœ๋ณ€์ˆ˜๋“ค์€ ์ˆ˜์น˜ํ•™์ ์ธ ๋ฐฉ๋ฒ•์œผ๋กœ ๊ต์ • ์ „์˜ CT ์˜์ƒ๊ณผ CT ์‹œ์Šคํ…œ์œผ๋กœ๋ถ€ํ„ฐ ์ถ”๊ฐ€์ ์ธ ์ตœ์ ํ™” ๊ณผ์ • ์—†์ด ํš๋“ํ•œ๋‹ค. ๋น”๊ฒฝํ™” ํ—ˆ์ƒ๋ฌผ๊ณผ ๊ด€๋ จ๋œ ๋งค๊ฐœ ๋ณ€์ˆ˜ ์ค‘ ๋‹จ ํ•˜๋‚˜๋งŒ ์žฌ๊ตฌ์„ฑ ์ดํ›„์˜ ์˜์ƒ ๋‹จ๊ณ„์—์„œ ์„ ํ˜• ์ตœ์ ํ™”๋ฅผ ํ†ตํ•ด ํƒ์ƒ‰๋œ๋‹ค. ๋˜ํ•œ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ๊ต์ •๋œ ๊ฒฐ๊ณผ ์˜์ƒ์— ์ž”์กดํ•˜๋Š” ํ—ˆ์ƒ๋ฌผ๋“ค์„ ์ œ๊ฑฐํ•˜๊ธฐ ์œ„ํ•œ ์ถ”๊ฐ€์ ์ธ ๋‘๊ฐ€์ง€ ๊ฐœ์„  ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ๋‹ค์ˆ˜์˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐ์ดํ„ฐ์™€ ์‹ค์ œ ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ •์„ฑ์  ๋ฐ ์ •๋Ÿ‰์  ๋น„๊ต๋ฅผ ํ†ตํ•ด ์ œ์•ˆ ๊ธฐ๋ฒ•์˜ ์œ ํšจ์„ฑ์ด ์ฒด๊ณ„์ ์œผ๋กœ ํ‰๊ฐ€๋˜์—ˆ๋‹ค. ์ œ์•ˆ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์ •ํ™•์„ฑ ๋ฐ ๊ฒฌ๊ณ ์„ฑ ์ธก๋ฉด์—์„œ ์œ ์˜๋ฏธํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์—ฌ์ฃผ์—ˆ๊ณ , ๊ธฐ์กด์˜ ๊ธฐ๋ฒ•๋“ค์— ๋น„ํ•ด ํ–ฅ์ƒ๋œ ๊ฒฐ๊ณผ ์˜์ƒ์˜ ํ’ˆ์งˆ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ž„์ƒ์ ์œผ๋กœ ์ ์šฉํ• ๋งŒํ•œ ๋น ๋ฅธ ์ˆ˜ํ–‰ ์‹œ๊ฐ„์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” CT ์˜์ƒ์„ ํ†ตํ•œ ์ง„๋‹จ๊ณผ ๋ฐฉ์‚ฌ๋Šฅ ์น˜๋ฃŒ์˜ ๊ณ„ํš ์ˆ˜๋ฆฝ์„ ์œ„ํ•œ ์ •ํ™•์„ฑ ํ–ฅ์ƒ์— ์œ ์˜๋ฏธํ•œ ์˜๋ฏธ๋ฅผ ๊ฐ–๋Š”๋‹ค.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 ์ดˆ๋ก 115 Acknowledgements 117Docto

    Quad-Net: Quad-domain Network for CT Metal Artifact Reduction

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    Metal implants and other high-density objects in patients introduce severe streaking artifacts in CT images, compromising image quality and diagnostic performance. Although various methods were developed for CT metal artifact reduction over the past decades, including the latest dual-domain deep networks, remaining metal artifacts are still clinically challenging in many cases. Here we extend the state-of-the-art dual-domain deep network approach into a quad-domain counterpart so that all the features in the sinogram, image, and their corresponding Fourier domains are synergized to eliminate metal artifacts optimally without compromising structural subtleties. Our proposed quad-domain network for MAR, referred to as Quad-Net, takes little additional computational cost since the Fourier transform is highly efficient, and works across the four receptive fields to learn both global and local features as well as their relations. Specifically, we first design a Sinogram-Fourier Restoration Network (SFR-Net) in the sinogram domain and its Fourier space to faithfully inpaint metal-corrupted traces. Then, we couple SFR-Net with an Image-Fourier Refinement Network (IFR-Net) which takes both an image and its Fourier spectrum to improve a CT image reconstructed from the SFR-Net output using cross-domain contextual information. Quad-Net is trained on clinical datasets to minimize a composite loss function. Quad-Net does not require precise metal masks, which is of great importance in clinical practice. Our experimental results demonstrate the superiority of Quad-Net over the state-of-the-art MAR methods quantitatively, visually, and statistically. The Quad-Net code is publicly available at https://github.com/longzilicart/Quad-Net

    Orientation-Shared Convolution Representation for CT Metal Artifact Learning

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    During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts in the captured CT images and then impair the clinical treatment. Against this metal artifact reduction (MAR) task, the existing deep-learning-based methods have gained promising reconstruction performance. Nevertheless, there is still some room for further improvement of MAR performance and generalization ability, since some important prior knowledge underlying this specific task has not been fully exploited. Hereby, in this paper, we carefully analyze the characteristics of metal artifacts and propose an orientation-shared convolution representation strategy to adapt the physical prior structures of artifacts, i.e., rotationally symmetrical streaking patterns. The proposed method rationally adopts Fourier-series-expansion-based filter parametrization in artifact modeling, which can better separate artifacts from anatomical tissues and boost the model generalizability. Comprehensive experiments executed on synthesized and clinical datasets show the superiority of our method in detail preservation beyond the current representative MAR methods. Code will be available at \url{https://github.com/hongwang01/OSCNet

    Potentials and caveats of AI in Hybrid Imaging

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    State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research

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

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    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
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