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

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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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Depth Camera Based Fluid reconstruction and its Solid-fluid Interaction 19

Depth Camera Based Fluid Reconstruction and its Solid-fluid ...

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32nd International conference on Computer Animation and Social Agents (CASA)

作者： Feng, Xiaobing Zhang, Juan Zhu, Dengming Shi, Min Wang, Zhaoqi Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Chinese Acad Sci Virtual Real Lab Inst Comp Technol Beijing Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China

ISBN: (纸本)9781450371599

Fluid animation has great value in study and application in many fields, such as video special effects, virtual reality and so on. However, due to the complexity and irregularity of fluid's motion, the existing simulation methods cannot make a good tradeoff between computational efficiency and realism. In this paper, a method of fluid animation synthesis based on depth camera is proposed. The RGB-D (red, green, blue-depth) data, captured by a RealSense camera, are used to reconstruct the fluid surface. By analysis the relation between color intensity and height field gradient of water surface, we use the RGB image as guidance to denoise the depth image, then possion reconstruction is employed to repair the incomplete data of depth image and fuse detail feature from the RGB to the reconstruction fluid surface. In addition, we propose a real-time method to simulate the interaction between solid and fluid, which is implemented on the wave particle system that fitted to the reconstruction fluid surface. Experiments show that the presented method can reconstruct fluid effectively and generate fluid animation of solid-fluid interaction plausibly.

关键词： data-driven depth camera fluid reconstruction fluid-solid interaction particles

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Patient evaluation of Breast Shape-corrected Tomosynthesis reconstruction

Patient evaluation of Breast Shape-corrected Tomosynthesis R...

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conference on Medical Imaging - Physics of Medical Imaging

作者： Michielsen, Koen Rangelova, Tsvetanka Sechopoulos, Ioannis Radboudumc Dept Radiol & Nucl Med Nijmegen Netherlands Dutch Reference Ctr Screening LRCB Nijmegen Netherlands

ISBN: (纸本)9781510625440

Iterative reconstruction is a good match with the sparsely sampled limited angle data generated by breast tomosynthesis systems. However, it suffers from a specific artifact near the breast edge where it overestimates the x-ray path length, resulting in a considerable underestimation of the reconstructed linear attenuation coefficients. In this work, we present the application of a method that uses the measured 3D breast shape to reduce these artifacts in patient data, by including this information as an additional constraint in the image reconstruction process. A series of 50 patients undergoing breast tomosynthesis were additionally imaged with a pair of structured light cameras placed left and right of the mammography unit. These 3D surfaces were then aligned with the help of the backprojected breast outline from the x-ray data to form a single contour following the true breast shape. This was then further processed to generate a binary 3D mask set to 1 inside and to 0 outside the breast, and used as constraint in the reconstruction. Due to incomplete coverage and image artifacts, this mask was created successfully for only 19 out of 50 cases. reconstructions were created with and without this constraint, and comparing attenuation profiles found that the artifact was almost completely corrected, bringing the reconstructed attenuation near the breast edge to the same level as the central region. Further visual inspection does show that higher quality optical 3D measurements and more precise alignment between optical and x-ray data are needed to avoid introducing new artifacts in the reconstruction.

关键词： Breast Signal attenuation Cameras X-rays data acquisition reconstruction algorithms 3D acquisition Digital breast tomosynthesis Structured light

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Geometry to the Rescue: 3D Instance reconstruction from a Cluttered Scene

Geometry to the Rescue: 3D Instance Reconstruction from a Cl...

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IEEE Computer Society conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Lin Li Salman Khan Nick Barnes Data61-CSIRO ANU IIAI ANU ANU

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

3D object instance reconstruction from a cluttered 2D scene image is an ill-posed problem. The main challenge is posed by the lack of geometric information in color images and heavy occlusions that lead to incomplete shape details. To deal with this problem, existing works on 3D instance reconstruction directly learn the mapping between the intensity image and the corresponding 3D volume model. Different from these works, we propose to explicitly incorporate 2.5D geometric cues, such as the surface normal, relative depth, and height, while generating full 3D shapes from 2D images. With an intermediate step focused on estimating these 2.5D geometric features, we propose a novel convolutional neural network design that progressively moves from 2D to full 3D estimation. Our model automatically generates instance-specific surface normal maps, relative depth, and height that are compactly encoded within our network design and consequently used to improve the 3D instance reconstruction. Our experimental results on the large-scale synthetic SUNCG dataset and the real-world NYU depth v2 dataset demonstrate the effectiveness of the proposed approach where it beats the state-of-the-art Factored3D network [15].

关键词： Three-dimensional displays Surface reconstruction Shape Estimation image reconstruction Two dimensional displays Training

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Online MR image reconstruction for compressed sensing acquisition in T2*imaging 18

Online MR image reconstruction for compressed sensing acquis...

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conference on Wavelets and Sparsity Xviii

作者： El Gueddari, Loubna Chouzenoux, Emilie Vignaud, Alexandre Pesquet, Jean-Christophe Ciuciu, Philippe CEA NeuroSpin Bat 145 F-91191 Gif Sur Yvette France Univ Paris Saclay Inria CEA Saclay Ile de France Parietal Team St Aubin France Univ Paris Saclay CVN Inria Saclay Cent Supelec St Aubin France

ISBN: (数字)9781510629707

ISBN: (纸本)9781510629707

Reducing acquisition time is a major challenge in high-resolution MRI that has been successfully addressed by Compressed Sensing (CS) theory. While the scan time has been massively accelerated by a factor up to 20 in 2D imaging, the complexity of image recovery algorithms has strongly increased, resulting in slower reconstruction processes. In this work we propose an online approach to shorten image reconstruction times in the CS setting. We leverage the segmented acquisition in multiple shots of k-space data to interleave the MR acquisition and image reconstruction steps. This approach is particularly appealing for 2D high-resolution T-2*-weighted anatomical imaging as the largest timing interval (i.e. Time of Repetition) between consecutive shots arises for this kind of imaging. During the scan, acquired shots are stacked together to form mini-batches and image reconstruction may start from incomplete data. For each newly available mini-batch, the previous partial solution is used as warm restart for the next sub-problem to be solved in a timing window compatible with the given TR and the number of shots stacked in a mini-batch. We demonstrate the interest and time savings of using online MR image reconstruction for Cartesian and non-Cartesian sampling strategies combined with a single receiver coil. Next, we extend the online formalism to address the more general multi-receiver phased array acquisition scenario. In this setting, calibrationless image reconstruction is adopted to remain compatible with the timing constraints of online delivery. Our retrospective and prospective results on ex-vivo 2D T-2*-weighted brain imaging show that high-quality MR images are recovered by the end of acquisition for single receiver acquisition and that additional iterations are required when parallel imaging is adopted. Overall, our approach implemented through the Gadgetron framework may be compatible with the data workflow on the scanner to provide the physician with reliable

关键词： C ompressed Sensing Magnetic Resonance Imaging image reconstruction Online processing Structured sparsity

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A Novel Computed Tomography Scanning Mode and Local image reconstruction of Impurities in Pipeline

A Novel Computed Tomography Scanning Mode and Local Image Re...

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3rd International conference on Sensing and Imaging (ICSI)

作者： Zhang, Lingli Zeng, Li Wu, Dong Chongqing Univ Coll Math & Stat Chongqing Peoples R China Chongqing Univ Educ Minist China Engn Res Ctr Ind Computed Tomog Nondestruct Testi Chongqing Peoples R China Jiangsu Univ Technol Sch Comp Engn Changzhou Jiangsu Peoples R China

ISBN: (纸本)9783319916590;9783319916583

Impurities (such as globular, blocky, or irregular solid) in pipeline easily lead to pipeline jamming, especially flowing fluid from main pipeline bifurcating into the thin pipeline. This situation may impact fluid speed and cause accidents. The novel computed tomography (CT) scanning mode is proposed to detect impurities in pipeline. Restricted by the scanning environment and impenetrability of the pipeline wall, this mode utilizes circular cone-beam scanning, and meanwhile it takes advantage of fluid flowing to obtain equivalent spiral cone-beam (ESCB) scanning mode, whose obtained projection data are affected by the pipeline wall. It treats the dynamic CT data as incomplete static CT data. Mechanical movement of the proposed scanning mode is simple and feasible, which can be easily adaptive to the live conditions of detected pipeline in service. According to the novel scanning mode, the corresponding approach which combines the general spiral cone-beam Feldkamp-Davis-Kress (FDK) image reconstruction algorithm and a local filter is presented to settle the truncated projection data. Numerical simulation tests show that the presented algorithm can facilitate relatively good local reconstruction images compared to general spiral cone-beam FDK. © Springer International Publishing AG, part of Springer Nature 2019.

关键词： A local filter Cone-beam computed tomography (CBCT) Local reconstruction Pipeline Spiral FDK

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Depth Completion Using a View-constrained Deep Prior

Depth Completion Using a View-constrained Deep Prior

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International conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Pallabi Ghosh Vibhav Vineet Larry S. Davis Abhinav Shrivastava Sudipta Sinha Neel Joshi University of Maryland Microsoft Research

ISBN: (数字)9781728181288

ISBN: (纸本)9781728181295

Recent work has shown that the structure of convolutional neural networks (CNNs) induces a strong prior that favors natural images. This prior, known as a deep image prior (DIP), is an effective regularizer in inverse problems such as image denoising and inpainting. We extend the concept of the DIP to depth images. Given color images and noisy and incomplete target depth maps, we optimize a randomly-initialized CNN model to reconstruct a depth map restored by virtue of using the CNN network structure as a prior combined with a view-constrained photo-consistency loss. This loss is computed using images from a geometrically calibrated camera from nearby viewpoints. We apply this deep depth prior for inpainting and refining incomplete and noisy depth maps within both binocular and multi-view stereo pipelines. Our quantitative and qualitative evaluation shows that our refined depth maps are more accurate and complete, and after fusion, produces dense 3D models of higher quality.

关键词： Electronics packaging Optimization Noise measurement Three-dimensional displays image reconstruction Training data Task analysis

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Limited-Angle Computed Tomography reconstruction using Combined FDK-Based Neural Network and U-Net

Limited-Angle Computed Tomography Reconstruction using Combi...

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Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Yiying Wang Tao Yang Weimin Huang Department of Computer Science University College London Institute for Infocomm Research A*STAR Singapore

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

The limited-angle cone-beam Computed Tomography (CT) is often used in C-arm for clinical diagnosis with the advantages of cheap cost and radiation dose reduction. However, due to incomplete projection data, the 3-dimensional CT images reconstructed by conventional methods, such as the Feldkamp, Davis and Kres (FDK) algorithm [1], suffer from heavy artifacts and missing features. In this paper, we propose a novel pipeline of neural networks jointly by a FDK-based neural network revisited from Würfl et al.'s work [2] and an image domain U-Net to enhance the 3-dimensional reconstruction quality for limited projection sinogram less than 180 degrees, i.e. 145 degrees in our work. Experimental results, on simulated projections of real-scan CTs, show that the proposed pipeline can reduce some of the major artifacts caused by the limited views while keep the key features, with a 16.60% improvement than Würfl et al.'s work on peak signal-to-noise ratio.

关键词： Neural networks Computed tomography image reconstruction Training Pipelines Machine learning Measurement

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SCFusion: Real-time Incremental Scene reconstruction with Semantic Completion

SCFusion: Real-time Incremental Scene Reconstruction with Se...

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International conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Shun-Cheng Wu Keisuke Tateno Nassir Navab Federico Tombari Technische Universitat München Germany Google Inc. Switzerland

ISBN: (数字)9781728181288

ISBN: (纸本)9781728181295

Real-time scene reconstruction from depth data inevitably suffers from occlusion, thus leading to incomplete 3D models. Partial reconstructions, in turn, limit the performance of algorithms that leverage them for applications in the context of, e.g., augmented reality, robotic navigation, and 3D mapping. Most methods address this issue by predicting the missing geometry as an offline optimization, thus being incompatible with real-time applications. We propose a framework that ameliorates this issue by performing scene reconstruction and semantic scene completion jointly in an incremental and real-time manner, based on an input sequence of depth maps. Our framework relies on a novel neural architecture designed to process occupancy maps and leverages voxel states to accurately and efficiently fuse semantic completion with the 3D global model. We evaluate the proposed approach quantitatively and qualitatively, demonstrating that our method can obtain accurate 3D semantic scene completion in real-time.

关键词： Three-dimensional displays Semantics image reconstruction Real-time systems Pipelines Shape Solid modeling

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Compression and Completion of Animated Point Clouds using Topological Properties of the Manifold

Compression and Completion of Animated Point Clouds using To...

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International conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Linfei Pan Ľubor Ladický Marc Pollefeys ETH Zurich ETH Zurich/Microsoft

ISBN: (数字)9781728181288

ISBN: (纸本)9781728181295

Recent progress in consumer hardware allowed for the collection of a large amount of animated point cloud data, which is on the one hand highly redundant and on the other hand incomplete. Our goal is to bridge this gap and find a low dimensional representation capable of approximation to a desired precision and completion of missing data. Model-less non-rigid 3D reconstruction algorithms, formulated as a linear factorization of observed point tracks into static shape component and dynamic pose, have been found insufficient to create suitable generative models, capable of generating new unobserved poses. This is due to the non-locality of the linear models, over-fitting to the non-causal correlations present in the data, which manifests in the reconstruction containing rigidly behaving not directly connected parts. In this paper, we propose a new method that can distinguish body parts and factorize the data into shape and pose purely using topological properties of the manifold-local deformations and neighborhoods. To obtain localized factorization, we formulate the deformation distance between two point tracks as the smallest deformation along the path between them. After embedding such distance in low dimensional space, a clustering of embedded data leads to close to rigid components, suitable as initialization for fitting a model-a skinned rigged mesh, used extensively in computer graphics. As both local deformations and neighborhoods of a point are local and can be estimated only from the part of the animation, the method can be used to recover unobserved data in each frame.

关键词： Strain Three-dimensional displays data models Computational modeling Shape Animation image reconstruction

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