4,677 research outputs found
CT ์์ ๊ธ์ ํ์๋ฌผ ์ ๊ฑฐ๋ฅผ ์ํ ํจ์จ์ ์ธ ๋น ๊ฒฝํ ๊ต์ ์๊ณ ๋ฆฌ์ฆ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ปดํจํฐ๊ณตํ๋ถ, 2021. 2. ์ ์๊ธธ.๋น๊ฒฝํ๋ ๋ค์ X์ ์ ์ฌ์ฉํ๊ณ ์๋์ง ์์กด์ ์ธ ๋ฌผ์ง ๊ฐ์ ๊ณ์๋ฅผ ์ด์ฉํ๋ CT ์์คํ
์ ํน์ฑ์ ๋ถ๊ฐํผํ ํ์์ด๋ฉฐ, ์ด๋ ํนํ ๊ธ์ ์์ญ์ ํฌํจํ๋ ํ๋ก์ ์
์์ ๊ฐ์ ์ค์ธก์ ํ์ฌ ๊ฒฐ๊ณผ์ ์ผ๋ก CT ์์์ ํ์๋ฌผ์ ์ ๋ฐํ๋ค. ๊ธ์ ํ์๋ฌผ ์ ๊ฐํ๋ CT ์์์ ์กด์ฌํ๋ ์ด๋ฌํ ํ์๋ฌผ์ ์ ๊ฑฐํ๊ณ ๊ฐ๋ ค์ง ์ค์ ์ ๋ณด๋ฅผ ๋ณต์ํ๋ ๊ณผ์ ์ด๋ค. ์์์ ํตํ ์ง๋จ๊ณผ ๋ฐฉ์ฌ์ ์น๋ฃ๋ฅผ ์ํ ๊ณํ ์๋ฆฝ์ ์์ด์ ์ ํํ CT ์์์ ํ๋ํ๊ธฐ ์ํด ๊ธ์ ํ์๋ฌผ์ ์ ๊ฑฐ๋ ํ์์ ์ด๋ค. ๋ฐ๋ณต์ ์ธ ์ฌ๊ตฌ์ฑ์ ์ํ ์์น์ ๋ฐฉ๋ฒ์ ๊ธฐ๋ฐ์ ๋ ํจ๊ณผ์ ์ธ ๊ธ์ ํ์๋ฌผ ์ ๊ฑฐ์ ๊ดํ ์ต์ ์ฐ๊ตฌ๋ค์ด ๋ฐํ๋์์ผ๋ ๋ฌด๊ฑฐ์ด ๊ณ์ฐ๋์ผ๋ก ์ธํด ์์ ์ค์ต์ ์ ์ฉ์ด ์ด๋ ค์ด ์ํฉ์ด๋ค.
๋ณธ ๋
ผ๋ฌธ์์๋ ์ด๋ฌํ ๊ณ์ฐ์ ์ธ ์ด์๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํ ํจ์จ์ ์ธ ๋น ๊ฒฝํ ์ถ์ ๋ชจ๋ธ๊ณผ ์ด๋ฅผ ์ด์ฉํ ๊ธ์ ํ์๋ฌผ ์ ๊ฐํ ๋ฐฉ๋ฒ์ ์ ์ํ๋ค. ์ ์ํ ๋ชจ๋ธ์ ๊ธ์ ๋ฌผ์ฒด์ ๊ธฐํ์ ๋ณด์ ๋ค์ X์ ์ด ๋ฌผ์ฒด๋ฅผ ํต๊ณผํ๋ฉด์ ๋ฐ์ํ๋ ๋น๊ฒฝํ์ ๋ฌผ๋ฆฌ์ ์ธ ํน์ฑ์ ๋ฐ์ํ๋ค. ๋ชจ๋ธ์ ํ์ํ ๋๋ถ๋ถ์ ๋งค๊ฐ๋ณ์๋ค์ ์์นํ์ ์ธ ๋ฐฉ๋ฒ์ผ๋ก ๊ต์ ์ ์ CT ์์๊ณผ CT ์์คํ
์ผ๋ก๋ถํฐ ์ถ๊ฐ์ ์ธ ์ต์ ํ ๊ณผ์ ์์ด ํ๋ํ๋ค. ๋น๊ฒฝํ ํ์๋ฌผ๊ณผ ๊ด๋ จ๋ ๋งค๊ฐ ๋ณ์ ์ค ๋จ ํ๋๋ง ์ฌ๊ตฌ์ฑ ์ดํ์ ์์ ๋จ๊ณ์์ ์ ํ ์ต์ ํ๋ฅผ ํตํด ํ์๋๋ค. ๋ํ ์ ์ํ ๋ฐฉ๋ฒ์ผ๋ก ๊ต์ ๋ ๊ฒฐ๊ณผ ์์์ ์์กดํ๋ ํ์๋ฌผ๋ค์ ์ ๊ฑฐํ๊ธฐ ์ํ ์ถ๊ฐ์ ์ธ ๋๊ฐ์ง ๊ฐ์ ๋ฐฉ๋ฒ์ ์ ์ํ๋ค.
๋ค์์ ์๋ฎฌ๋ ์ด์
๋ฐ์ดํฐ์ ์ค์ ๋ฐ์ดํฐ๋ฅผ ์ฌ์ฉํ์ฌ ์ ์ฑ์ ๋ฐ ์ ๋์ ๋น๊ต๋ฅผ ํตํด ์ ์ ๊ธฐ๋ฒ์ ์ ํจ์ฑ์ด ์ฒด๊ณ์ ์ผ๋ก ํ๊ฐ๋์๋ค. ์ ์ ์๊ณ ๋ฆฌ์ฆ์ ์ ํ์ฑ ๋ฐ ๊ฒฌ๊ณ ์ฑ ์ธก๋ฉด์์ ์ ์๋ฏธํ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ฌ์ฃผ์๊ณ , ๊ธฐ์กด์ ๊ธฐ๋ฒ๋ค์ ๋นํด ํฅ์๋ ๊ฒฐ๊ณผ ์์์ ํ์ง ๋ฟ๋ง ์๋๋ผ ์์์ ์ผ๋ก ์ ์ฉํ ๋งํ ๋น ๋ฅธ ์ํ ์๊ฐ์ ๋ณด์ฌ์ฃผ์๋ค. ์ด ์ฐ๊ตฌ๋ CT ์์์ ํตํ ์ง๋จ๊ณผ ๋ฐฉ์ฌ๋ฅ ์น๋ฃ์ ๊ณํ ์๋ฆฝ์ ์ํ ์ ํ์ฑ ํฅ์์ ์ ์๋ฏธํ ์๋ฏธ๋ฅผ ๊ฐ๋๋ค.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
์ด๋ก 115
Acknowledgements 117Docto
Quad-Net: Quad-domain Network for CT Metal Artifact Reduction
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
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
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
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
- โฆ