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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data III"

共 622 条记录，以下是101-110 订阅

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JSR-NET: A DEEP NETWORK FOR JOINT SPATIAL-RADON DOMAIN CT reconstruction from incomplete data 44

JSR-NET: A DEEP NETWORK FOR JOINT SPATIAL-RADON DOMAIN CT RE...

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44th IEEE International conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Zhang, Haimiao Dong, Bin Liu, Baodong Peking Univ Beijing Peoples R China Chinese Acad Sci Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781479981311

CT image reconstruction from incomplete data, such as sparse views and limited angle reconstruction, is an important and challenging problem in medical imaging. This work proposes a new deep convolutional neural network (CNN), called JSR-Net, that jointly reconstructs CT images and their associated Radon domain projections. JSR-Net combines the traditional model based approach with deep architecture design of deep learning. A hybrid loss function is adopted to improve the performance of the JSR-Net making it more effective in protecting important image structures. Numerical experiments demonstrate that JSR-Net outperforms some latest model based reconstruction methods, as well as a recently proposed deep model.

关键词： Joint spatial-Radon domain reconstruction Sparse-view CT Limited angle CT Convolutional neural networks Deep learning

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Self-Guided and MR-Guided Deep-Learned Post-reconstruction PET Processing

Self-Guided and MR-Guided Deep-Learned Post-Reconstruction P...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Guillaume Corda-D’Incan Julia A. Schnabel Andrew J. Reader School of Biomedical Engineering and Imaging Sciences King’s College London UK The Technical University of Munich Germany The Helmholtz Center Munich Germany

ISBN: (数字)9781665421133

ISBN: (纸本)9781665421140

Reconstructed PET images exhibit high noise levels and low spatial resolution when shorter scan times and reduced injected doses are used. Regularisation methods such as post-reconstruction smoothing can help to improve image quality. Recently, neural networks have proved to be highly effective for this task by learning an ensemble of kernels. For post-processing of PET images, a high-resolution MR image can also be used for guidance to further improve the final image quality. In this work, we investigate the impact of the input choice and the number of training samples used on a neural network’s performance for PET post-reconstruction processing. To do so, six combinations of low-count PET and MR independent reconstruction outputs are fed into a state-of-the-art residual convolutional neural network (CNN). Six different networks were trained using as input i) the last iteration of a conventional PET reconstruction, ii) all the iterates from the PET reconstruction, iii) only the final PET and MR estimates, iv) all the PET estimates and the final MR, v) the final PET and all the MR estimates and vi) all the iteration outputs of independent PET and MR reconstruction. The networks have been trained using a different number of training samples as well. The results obtained suggest that using all the intermediate reconstructions lead the network to perform better when the training set size is limited. Furthermore, the gain in performance observed when the dataset size increases are higher for methods using all the intermediate reconstruction outputs. Future work will focus on training networks with a higher number of training samples to confirm the trend observed and assess the proposed method on 3D real data.

关键词： Training image quality Three-dimensional displays Smoothing methods Neural networks Convolutional neural networks Task analysis

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Iterative Imputation of Missing data Using Auto-Encoder Dynamics 27th

Iterative Imputation of Missing Data Using Auto-Encoder Dyna...

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27th International conference on Neural Information Processing

作者： Smieja, Marek Kolomycki, Maciej Struski, Lukasz Juda, Mateusz Figueiredo, Mario A. T. Jagiellonian Univ Fac Math & Comp Sci Krakow Poland Cracow Univ Technol Fac Mech Engn Inst Appl Informat Krakow Poland Univ Lisbon Inst Telecomunicacoes Inst Super Tecn Lisbon Portugal

ISBN: (纸本)9783030638351;9783030638368

This paper introduces an approach to missing data imputation based on deep auto-encoder models, adequate to high-dimensional data exhibiting complex dependencies, such as images. The method exploits the properties of the vector field associated to an auto-encoder, which allows to approximate the gradient of the log-density from its reconstruction error, based on which we propose a projected gradient ascent algorithm to obtain the conditionally most probable estimate of the missing values. Our approach does not require any specialized training procedure and can be used together with any auto-encoder model trained on complete data in a classical way. Experiments performed on benchmark datasets show that imputations produced by our model are sharp and realistic.

关键词： Missing data imputation image inpainting Auto-encoder Dynamical system Auto-encoder's vector field

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A Self-Supervised Bootstrap Method for Single-image 3D Face reconstruction 19

A Self-Supervised Bootstrap Method for Single-Image 3D Face ...

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19th IEEE Winter conference on Applications of Computer Vision (WACV)

作者： Xing, Yifan Tewari, Rahul Mendonc, Paulo R. S. Amazon Web Serv Seattle WA 98109 USA

ISBN: (纸本)9781728119755

State-of-the-art methods for 3D reconstruction of faces from a single image require 2D-3D pairs of ground-truth data for supervision. Such data is costly to acquire, and most datasets available in the literature are restricted to pairs for which the input 2D images depict faces in a near fronto-parallel pose. Therefore, many data-driven methods for single-image 3D facial reconstruction perform poorly on profile and near-profile faces. We propose a method to improve the performance of single-image 3D facial reconstruction networks by utilizing the network to synthesize its own training data for fine-tuning, comprising: (i) single-image 3D reconstruction of faces in near-frontal images without ground-truth 3D shape;(ii) application of a rigid-body transformation to the reconstructed face model;(iii) rendering of the face model from new viewpoints;and (iv) use of the rendered image and corresponding 3D reconstruction as additional data for supervised fine-tuning. The new 2D-3D pairs thus produced have the same high-quality observed for near fronto-parallel reconstructions, thereby nudging the network towards more uniform performance as a function of the viewing angle of input faces. Application of the proposed technique to the fine-tuning of a state-of-the-art single-image 3D-reconstruction network for faces demonstrates the usefulness of the method, with particularly significant gains for profile or near-profile views.

关键词： Three-dimensional displays Face image reconstruction Solid modeling data models Training Two dimensional displays

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image reconstruction in CT from Limited-Angle Projections 10

Image Reconstruction in CT from Limited-Angle Projections

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10th International conference on Graphics and image Processing (ICGIP)

作者： Li, Dou Wang, Shanshan Cai, Zemin Liang, Dong Luo, Jianhua Shantou Univ Coll Engn Dept Elect Engn Shantou Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Paul C Lauterbur Res Ctr Biomed Imaging Shenzhen 518055 Peoples R China Shanghai Jiao Tong Univ Sch Aeronaut & Atronaut Shanghai 200240 Peoples R China

ISBN: (数字)9781510628298

ISBN: (纸本)9781510628298

Limited-angle tomography has gained much interest in late years Nevertheless, image reconstruction from incomplete projections is a classic ill-posed issue in the field of computational imaging. In this paper, we propose a scheme based on the sparsifying operators and approximation of l(0)-minimization. Our framework includes two main components, one for a sparsifying operator, and one for learning the scheme parameters using l(0)-minimization from insufficient computed tomography data. Thus, the proposed scheme is capable of recovering high quality reconstructions at a range of angles and noise. Compared to the total-variation (TV) regularized reconstruction scheme, delta-u scheme and ATV (Anisotropic Total Variation) scheme, validations using Shepp-Logan phantom computed tomography data demonstrate the significant improvements in SNR and suppressed noise and artifacts.

关键词： incomplete projection Sparsifying operator l(0)-minimization

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data-driven Adversarial Learning for Sinogram-based Iterative Low-Dose CT image reconstruction 23

Data-driven Adversarial Learning for Sinogram-based Iterativ...

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23rd International conference on System Theory, Control and Computing (ICSTCC)

作者： Vizitiu, Anamaria Puiu, Andrei Reaungamornrat, Sureerat Itu, Lucian Mihai Siemens SRL Corp Technol Brasov Romania Transilvania Univ Brasov Dept Automat & Informat Technol Brasov Romania Siemens Healthineers Med Imaging Technol Princeton NJ USA

ISBN: (纸本)9781728106991

One of the most active research areas in computed tomography (CT) is to devise a strategy to reduce radiation exposure, while maintaining high image quality, required for accurate diagnosis. The recent advancements offered by deep learning based data-driven approaches for solving inverse problems in biomedical imaging have led to the development of an alternative method for producing high-quality reconstructed images from low-dose CT data. While most of the reconstruction approaches tackle the problem from a post-processing perspective, in this paper, inspired by the idea of unfolding a proximal gradient descent optimization algorithm to finite iterations, and replacing the proximal terms with trainable deep artificial neural networks, we propose an end-to-end solution for reconstructing full-dose tomographic images directly from low-dose measurements. The framework is designed to encapsulate the knowledge of the physical model of CT image formation, and to produce high-quality images that account for human perception through a Generative Adversarial Network with Wasserstein distance and a contextual loss. The proposed method was validated on a clinical dataset, and promising results have been obtained compared to the state-of-the-art mean-squared-error (MSE) based learned iterative reconstruction approach, while also maintaining a runtime suitable for a routine clinical setting.

关键词： CT imaging deep learning reconstruction

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RECOVERY OF SUBSPACE STRUCTURE from HIGH-RANK data WITH MISSING ENTRIES 26

RECOVERY OF SUBSPACE STRUCTURE FROM HIGH-RANK DATA WITH MISS...

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26th IEEE International conference on image Processing (ICIP)

作者： Carvalho, Joao Marques, Manuel Costeira, Joao P. Univ Lisbon Inst Super Tecn LARSyS Inst Syst & Robot Lisbon Portugal

ISBN: (纸本)9781538662496

We propose a method to reconstruct and cluster incomplete high-dimensional data lying in a union of low-dimensional subspaces. Exploring the sparse representation model, we jointly estimate the missing data while imposing the intrinsic subspace structure. Although we have a non-convex problem, we propose an algorithm robust to initialization. Extensive experiments with synthetic and real data show that our approach leads to significant improvements in the reconstruction and segmentation, outperforming current state of the art for both low and high-rank data.

关键词： Motion Segmentation Subspace Clustering Missing data Matrix Completion Sparse Representation

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Deep Multimodal reconstruction of Retinal images Using Paired or Unpaired data

Deep Multimodal Reconstruction of Retinal Images Using Paire...

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International Joint conference on Neural Networks (IJCNN)

作者： Hervella, Alvaro S. Rouco, Jose Novo, Jorge Ortega, Marcos Univ A Coruna CITIC Res Ctr Informat & Commun Technol La Coruna Spain Univ A Coruna Dept Comp Sci La Coruna Spain

ISBN: (纸本)9781728119854

This paper explores the application of deep learning-based methods for the multimodal reconstruction of fluorescein angiography from retinography. The objective of this multimodal reconstruction is not only to estimate an invasive modality from a non-invasive one, but also to apply the learned models for transfer learning or domain adaption. Deep neural networks have demonstrated to be successful at learning the mapping between complementary image domains, using both paired or unpaired data. The paired data allows taking advantage of the rich information that is available from the pixelwise correspondence of paired images. However, this requires the pre-registration of the multimodal image pairs. In the case of the retinal images, the multimodal registration is a challenging task that may fail in complex scenarios, such as severe pathological cases or low quality samples. In contrast, the use of generative adversarial networks allows learning the mapping between image domains using unpaired data. This avoids the pre-registration of the images and allows including all the available data for training. In this work, we analyze both paired and unpaired deep learning-based approaches for the multimodal reconstruction of retinal images. The objective is to understand the implications of each alternative and the considerations for their future usage. For that purpose, we perform several experiments with the focus on producing a fair comparison between paired and unpaired approaches.

关键词： Angiography Training image reconstruction Generators Neural networks Retina Training data

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Integrating data and image Domain Deep Learning for Limited Angle Tomography using Consensus Equilibrium 17

Integrating Data and Image Domain Deep Learning for Limited ...

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IEEE/CVF International conference on Computer Vision (ICCV)

作者： Ghani, Muhammad Usman Karl, W. Clem Boston Univ Boston MA 02215 USA

ISBN: (纸本)9781728150239

Computed Tomography (CT) is a non-invasive imaging modality with applications ranging from healthcare to security. It reconstructs cross-sectional images of an object using a collection of projection data collected at different angles. Conventional methods, such as FBP, require that the projection data be uniformly acquired over the complete angular range. In some applications, it is not possible to acquire such data. Security is one such domain where non-rotational scanning configurations are being developed which violate the complete data assumption. Conventional methods produce images from such data that are filled with artifacts. The recent success of deep learning (DL) methods has inspired researchers to post-process these artifact laden images using deep neural networks (DNNs). This approach has seen limited success on real CT problems. Another approach has been to pre-process the incomplete data using DNNs aiming to avoid the creation of artifacts altogether. Due to imperfections in the learning process, this approach can still leave perceptible residual artifacts. In this work, we aim to combine the power of deep learning in both the data and image domains through a two-step process based on the consensus equilibrium (CE) framework. Specifically, we use conditional generative adversarial networks (cGANs) in both the data and the image domain for enhanced performance and efficient computation and combine them through a consensus process. We demonstrate the effectiveness of our approach on a real security CT dataset for a challenging 900 limited-angle problem. The same framework can be applied to other limited data problems arising in applications such as electron microscopy, nondestructive evaluation, and medical imaging.

关键词： Computational imaging Computed tomography Consensus equilibrium data domain learning Deep imaging Deep learning image domain learning incomplete data Learned imaging Limited data Model based image reconstruction

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Deep image reconstruction for Reducing Limited-Angle Artifacts in a Dual-Panel TOF PET

Deep Image Reconstruction for Reducing Limited-Angle Artifac...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Yusheng Li Samuel Matej University of Pennsylvania Philadelphia Pennsylvania

Dual-panel PET scanners have many advantages in dedicated breast imaging and on-board imaging applications since the compact scanners can be combined with other imaging and treatment modalities. The major challenges of dual-panel PET imaging are the incomplete sampling and data truncation, which can cause severe limited-angle artifacts. In this incomplete sampling case, time-of-flight (TOF) provides new information and thus reduces the artifacts, however, the problem is still quite challenging even with 200 to 300 ps timing resolution. In this work, we explore deep learning based image reconstruction for limited-angle artifacts reduction for dual-panel TOF PET imaging. The deep image reconstruction consists of two components, namely, TOF ordered subsets expectation maximization (OSEM) reconstruction, and a deep neural network for limited-angle artifacts reduction. We adopt and optimize a U-net based architecture for limited-angle artifacts reduction (LaU-net) to predict expected images from limited-angle TOF reconstructions. We perform numerical simulations with a generic 2D dual-panel TOF PET system with timing resolution of 300 ps and angular coverage of 90°. We generate 640 2D training datasets by performing TOF ordered subsets expectation maximization (OSEM) reconstructions from randomly generated phantom images. Then 3 additional folds of datasets were obtained using data augmentation by flipping horizontal and vertical dimensions for each dataset. We used Kullback-Leibler divergence as loss function for nonnegative images, and the Adam optimizer for training. We show from both random phantoms and a high resolution hot-rod phantom that the deep reconstruction can substantially reduce limited-angle artifacts and improve quantitative accuracy of reconstructed images.

关键词： Deep learning Training Performance evaluation image resolution Three-dimensional displays Breast Imaging phantoms

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