2,449 research outputs found
Task adapted reconstruction for inverse problems
The paper considers the problem of performing a task defined on a model
parameter that is only observed indirectly through noisy data in an ill-posed
inverse problem. A key aspect is to formalize the steps of reconstruction and
task as appropriate estimators (non-randomized decision rules) in statistical
estimation problems. The implementation makes use of (deep) neural networks to
provide a differentiable parametrization of the family of estimators for both
steps. These networks are combined and jointly trained against suitable
supervised training data in order to minimize a joint differentiable loss
function, resulting in an end-to-end task adapted reconstruction method. The
suggested framework is generic, yet adaptable, with a plug-and-play structure
for adjusting both the inverse problem and the task at hand. More precisely,
the data model (forward operator and statistical model of the noise) associated
with the inverse problem is exchangeable, e.g., by using neural network
architecture given by a learned iterative method. Furthermore, any task that is
encodable as a trainable neural network can be used. The approach is
demonstrated on joint tomographic image reconstruction, classification and
joint tomographic image reconstruction segmentation
Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)
Positron emission tomography (PET) image synthesis plays an important role,
which can be used to boost the training data for computer aided diagnosis
systems. However, existing image synthesis methods have problems in
synthesizing the low resolution PET images. To address these limitations, we
propose multi-channel generative adversarial networks (M-GAN) based PET image
synthesis method. Different to the existing methods which rely on using
low-level features, the proposed M-GAN is capable to represent the features in
a high-level of semantic based on the adversarial learning concept. In
addition, M-GAN enables to take the input from the annotation (label) to
synthesize the high uptake regions e.g., tumors and from the computed
tomography (CT) images to constrain the appearance consistency and output the
synthetic PET images directly. Our results on 50 lung cancer PET-CT studies
indicate that our method was much closer to the real PET images when compared
with the existing methods.Comment: 9 pages, 2 figure
ํด๋ถํ์ ์ ๋ PET ์ฌ๊ตฌ์ฑ: ๋งค๋๋ฝ์ง ์์ ์ฌ์ ํจ์๋ถํฐ ๋ฅ๋ฌ๋ ์ ๊ทผ๊น์ง
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ์๊ณผ๋ํ ์๊ณผํ๊ณผ, 2021. 2. ์ด์ฌ์ฑ.Advances in simultaneous positron emission tomography/magnetic resonance imaging (PET/MRI) technology have led to an active investigation of the anatomy-guided regularized PET image reconstruction algorithm based on MR images. Among the various priors proposed for anatomy-guided regularized PET image reconstruction, Bowsherโs method based on second-order smoothing priors sometimes suffers from over-smoothing of detailed structures. Therefore, in this study, we propose a Bowsher prior based on the l1 norm and an iteratively reweighting scheme to overcome the limitation of the original Bowsher method. In addition, we have derived a closed solution for iterative image reconstruction based on this non-smooth prior. A comparison study between the original l2 and proposed l1 Bowsher priors were conducted using computer simulation and real human data. In the simulation and real data application, small lesions with abnormal PET uptake were better detected by the proposed l1 Bowsher prior methods than the original Bowsher prior. The original l2 Bowsher leads to a decreased PET intensity in small lesions when there is no clear separation between the lesions and surrounding tissue in the anatomical prior. However, the proposed l1 Bowsher prior methods showed better contrast between the tumors and surrounding tissues owing to the intrinsic edge-preserving property of the prior which is attributed to the sparseness induced by l1 norm, especially in the iterative reweighting scheme. Besides, the proposed methods demonstrated lower bias and less hyper-parameter dependency on PET intensity estimation in the regions with matched anatomical boundaries in PET and MRI.
Moreover, based on the formulation of l1 Bowsher prior, the unrolled network containing the conventional maximum-likelihood expectation-maximization (ML-EM) module was also proposed. The convolutional layers successfully learned the distribution of anatomically-guided PET images and the EM module corrected the intermediate outputs by comparing them with sinograms. The proposed unrolled network showed better performance than ordinary U-Net, where the regional uptake is less biased and deviated. Therefore, these methods will help improve the PET image quality based on the anatomical side information.์์ ์๋ฐฉ์ถ๋จ์ธต์ดฌ์ / ์๊ธฐ๊ณต๋ช
์์ (PET/MRI) ๋์ ํ๋ ๊ธฐ์ ์ ๋ฐ์ ์ผ๋ก MR ์์์ ๊ธฐ๋ฐ์ผ๋ก ํ ํด๋ถํ์ ์ฌ์ ํจ์๋ก ์ ๊ทํ ๋ PET ์์ ์ฌ๊ตฌ์ฑ ์๊ณ ๋ฆฌ์ฆ์ ๋ํ ์ฌ๋์๋ ํ๊ฐ๊ฐ ์ด๋ฃจ์ด์ก๋ค. ํด๋ถํ ๊ธฐ๋ฐ์ผ๋ก ์ ๊ทํ ๋ PET ์ด๋ฏธ์ง ์ฌ๊ตฌ์ฑ์ ์ํด ์ ์ ๋ ๋ค์ํ ์ฌ์ ์ค 2์ฐจ ํํํ ์ฌ์ ํจ์์ ๊ธฐ๋ฐํ Bowsher์ ๋ฐฉ๋ฒ์ ๋๋๋ก ์ธ๋ถ ๊ตฌ์กฐ์ ๊ณผ๋ํ ํํํ๋ก ์ด๋ ค์์ ๊ฒช๋๋ค. ๋ฐ๋ผ์ ๋ณธ ์ฐ๊ตฌ์์๋ ์๋ Bowsher ๋ฐฉ๋ฒ์ ํ๊ณ๋ฅผ ๊ทน๋ณตํ๊ธฐ ์ํด l1 norm์ ๊ธฐ๋ฐํ Bowsher ์ฌ์ ํจ์์ ๋ฐ๋ณต์ ์ธ ์ฌ๊ฐ์ค์น ๊ธฐ๋ฒ์ ์ ์ํ๋ค. ๋ํ, ์ฐ๋ฆฌ๋ ์ด ๋งค๋๋ฝ์ง ์์ ์ฌ์ ํจ์๋ฅผ ์ด์ฉํ ๋ฐ๋ณต์ ์ด๋ฏธ์ง ์ฌ๊ตฌ์ฑ์ ๋ํด ๋ซํ ํด๋ฅผ ๋์ถํ๋ค. ์๋ l2์ ์ ์ ๋ l1 Bowsher ์ฌ์ ํจ์ ๊ฐ์ ๋น๊ต ์ฐ๊ตฌ๋ ์ปดํจํฐ ์๋ฎฌ๋ ์ด์
๊ณผ ์ค์ ๋ฐ์ดํฐ๋ฅผ ์ฌ์ฉํ์ฌ ์ํ๋์๋ค. ์๋ฎฌ๋ ์ด์
๋ฐ ์ค์ ๋ฐ์ดํฐ์์ ๋น์ ์์ ์ธ PET ํก์๋ฅผ ๊ฐ์ง ์์ ๋ณ๋ณ์ ์๋ Bowsher ์ด์ ๋ณด๋ค ์ ์ ๋ l1 Bowsher ์ฌ์ ๋ฐฉ๋ฒ์ผ๋ก ๋ ์ ๊ฐ์ง๋์๋ค. ์๋์ l2 Bowsher๋ ํด๋ถํ์ ์์์์ ๋ณ๋ณ๊ณผ ์ฃผ๋ณ ์กฐ์ง ์ฌ์ด์ ๋ช
ํํ ๋ถ๋ฆฌ๊ฐ ์์ ๋ ์์ ๋ณ๋ณ์์์ PET ๊ฐ๋๋ฅผ ๊ฐ์์ํจ๋ค. ๊ทธ๋ฌ๋ ์ ์ ๋ l1 Bowsher ์ฌ์ ๋ฐฉ๋ฒ์ ํนํ ๋ฐ๋ณต์ ์ฌ๊ฐ์ค์น ๊ธฐ๋ฒ์์ l1 ๋
ธ๋ฆ์ ์ํด ์ ๋๋ ํฌ์์ฑ์ ๊ธฐ์ธํ ํน์ฑ์ผ๋ก ์ธํด ์ข
์๊ณผ ์ฃผ๋ณ ์กฐ์ง ์ฌ์ด์ ๋ ๋์ ๋๋น๋ฅผ ๋ณด์ฌ์ฃผ์๋ค. ๋ํ ์ ์๋ ๋ฐฉ๋ฒ์ PET๊ณผ MRI์ ํด๋ถํ์ ๊ฒฝ๊ณ๊ฐ ์ผ์นํ๋ ์์ญ์์ PET ๊ฐ๋ ์ถ์ ์ ๋ํ ํธํฅ์ด ๋ ๋ฎ๊ณ ํ์ดํผ ํ๋ผ๋ฏธํฐ ์ข
์์ฑ์ด ์ ์์ ๋ณด์ฌ์ฃผ์๋ค.
๋ํ, l1Bowsher ์ฌ์ ํจ์์ ๋ซํ ํด๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ๊ธฐ์กด์ ML-EM (maximum-likelihood expectation-maximization) ๋ชจ๋์ ํฌํจํ๋ ํผ์ณ์ง ๋คํธ์ํฌ๋ ์ ์๋์๋ค. ์ปจ๋ณผ๋ฃจ์
๋ ์ด์ด๋ ํด๋ถํ์ ์ผ๋ก ์ ๋ ์ฌ๊ตฌ์ฑ๋ PET ์ด๋ฏธ์ง์ ๋ถํฌ๋ฅผ ์ฑ๊ณต์ ์ผ๋ก ํ์ตํ์ผ๋ฉฐ, EM ๋ชจ๋์ ์ค๊ฐ ์ถ๋ ฅ๋ค์ ์ฌ์ด๋
ธ๊ทธ๋จ๊ณผ ๋น๊ตํ์ฌ ๊ฒฐ๊ณผ ์ด๋ฏธ์ง๊ฐ ์ ๋ค์ด๋ง๊ฒ ์์ ํ๋ค. ์ ์๋ ํผ์ณ์ง ๋คํธ์ํฌ๋ ์ง์ญ์ ํก์์ ๋์ด ๋ ํธํฅ๋๊ณ ํธ์ฐจ๊ฐ ์ ์ด, ์ผ๋ฐ U-Net๋ณด๋ค ๋ ๋์ ์ฑ๋ฅ์ ๋ณด์ฌ์ฃผ์๋ค. ๋ฐ๋ผ์ ์ด๋ฌํ ๋ฐฉ๋ฒ๋ค์ ํด๋ถํ์ ์ ๋ณด๋ฅผ ๊ธฐ๋ฐ์ผ๋ก PET ์ด๋ฏธ์ง ํ์ง์ ํฅ์์ํค๋ ๋ฐ ์ ์ฉํ ๊ฒ์ด๋ค.Chapter 1. Introduction 1
1.1. Backgrounds 1
1.1.1. Positron Emission Tomography 1
1.1.2. Maximum a Posterior Reconstruction 1
1.1.3. Anatomical Prior 2
1.1.4. Proposed l_1 Bowsher Prior 3
1.1.5. Deep Learning for MR-less Application 4
1.2. Purpose of the Research 4
Chapter 2. Anatomically-guided PET Reconstruction Using Bowsher Prior 6
2.1. Backgrounds 6
2.1.1. PET Data Model 6
2.1.2. Original Bowsher Prior 7
2.2. Methods and Materials 8
2.2.1. Proposed l_1 Bowsher Prior 8
2.2.2. Iterative Reweighting 13
2.2.3. Computer Simulations 15
2.2.4. Human Data 16
2.2.5. Image Analysis 17
2.3. Results 19
2.3.1. Simulation with Brain Phantom 19
2.3.2.Human Data 20
2.4. Discussions 25
Chapter 3. Deep Learning Approach for Anatomically-guided PET Reconstruction 31
3.1. Backgrounds 31
3.2. Methods and Materials 33
3.2.1. Douglas-Rachford Splitting 33
3.2.2. Network Architecture 34
3.2.3. Dataset and Training Details 35
3.2.4. Image Analysis 36
3.3. Results 37
3.4. Discussions 38
Chapter 4. Conclusions 40
Bibliography 41
Abstract in Korean (๊ตญ๋ฌธ ์ด๋ก) 52Docto
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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
Artificial Intelligence Techniques in Medical Imaging: A Systematic Review
This scientific review presents a comprehensive overview of medical imaging modalities and their diverse applications in artificial intelligence (AI)-based disease classification and segmentation. The paper begins by explaining the fundamental concepts of AI, machine learning (ML), and deep learning (DL). It provides a summary of their different types to establish a solid foundation for the subsequent analysis. The prmary focus of this study is to conduct a systematic review of research articles that examine disease classification and segmentation in different anatomical regions using AI methodologies. The analysis includes a thorough examination of the results reported in each article, extracting important insights and identifying emerging trends. Moreover, the paper critically discusses the challenges encountered during these studies, including issues related to data availability and quality, model generalization, and interpretability. The aim is to provide guidance for optimizing technique selection. The analysis highlights the prominence of hybrid approaches, which seamlessly integrate ML and DL techniques, in achieving effective and relevant results across various disease types. The promising potential of these hybrid models opens up new opportunities for future research in the field of medical diagnosis. Additionally, addressing the challenges posed by the limited availability of annotated medical images through the incorporation of medical image synthesis and transfer learning techniques is identified as a crucial focus for future research efforts
A novel framework for efficient identification of brain cancer region from brain MRI
Diagnosis of brain cancer using existing imaging techniques, e.g., Magnetic Resonance Imaging (MRI) is shrouded with various degrees of challenges. At present, there are very few significant research models focusing on introducing some novel and unique solutions towards such problems of detection. Moreover, existing techniques are found to have lesser accuracy as compared to other detection schemes. Therefore, the proposed paper presents a framework that introduces a series of simple and computationally cost-effective techniques that have assisted in leveraging the accuracy level to a very higher degree. The proposed framework takes the input image and subjects it to non-conventional segmentation mechanism followed by optimizing the performance using directed acyclic graph, Bayesian Network, and neural network. The study outcome of the proposed system shows the significantly higher degree of accuracy in detection performance as compared to frequently existing approaches
Mathematics of biomedical imaging todayโa perspective
Biomedical imaging is a fascinating, rich and dynamic research area, which has huge importance in biomedical research and clinical practice alike. The key technology behind the processing, and automated analysis and quantification of imaging data is mathematics. Starting with the optimisation of the image acquisition and the reconstruction of an image from indirect tomographic measurement data, all the way to the automated segmentation of tumours in medical images and the design of optimal treatment plans based on image biomarkers, mathematics appears in all of these in different flavours. Non-smooth optimisation in the context of sparsity-promoting image priors, partial differential equations for image registration and motion estimation, and deep neural networks for image segmentation, to name just a few. In this article, we present and review mathematical topics that arise within the whole biomedical imaging pipeline, from tomographic measurements to clinical support tools, and highlight some modern topics and open problems. The article is addressed to both biomedical researchers who want to get a taste of where mathematics arises in biomedical imaging as well as mathematicians who are interested in what mathematical challenges biomedical imaging research entails
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