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Brain Segmentation from k-Space with End-to-End Recurrent Attention Network 22nd

Brain Segmentation from k-Space with End-to-End Recurrent At...

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10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： Huang, Qiaoying Chen, Xiao Metaxas, Dimitris Nadar, Mariappan S. Rutgers State Univ Dept Comp Sci Piscataway NJ 08854 USA Siemens Healthineers Digital Technol & Innovat Princeton NJ USA Siemens Healthineers Princeton NJ USA

ISBN: (纸本)9783030322489;9783030322472

The task of medical image segmentation commonly involves an image reconstruction step to convert acquired raw data to images before any analysis. However, noises, artifacts and loss of information due to the reconstruction process are almost inevitable, which compromises the final performance of segmentation. We present a novel learning framework that performs magnetic resonance brain image segmentation directly from k-space data. The end-to-end framework consists of a unique task-driven attention module that recurrently utilizes intermediate segmentation estimation to facilitate image-domain feature extraction from the raw data, thus closely bridging the reconstruction and the segmentation tasks. In addition, to address the challenge of manual labeling, we introduce a novel workflow to generate labeled training data for segmentation by exploiting imaging modality simulators and digital phantoms. Extensive experimental results show that the proposed method outperforms several state-of-the-art methods.

关键词： image segmentation

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Deep Learning Based Framework for Direct reconstruction of PET images 1

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10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： Liu, Zhiyuan Chen, Huai Liu, Huafeng Zhejiang Univ Coll Opt Sci & Engn State Key Lab Modern Opt Instrumentat Hangzhou Peoples R China

ISBN: (数字)9783030322489

ISBN: (纸本)9783030322489;9783030322472

In Positron Emission Tomography (PET), high radioactivity maps are essential to better understand the physiological processes associated with the disease. In this paper, we propose a deep learning based framework for PET image reconstruction from sinogram domain directly. In the framework, conditional Generative Adversarial Networks (cGANs) is constructed to learn a mapping from sinogram data to reconstructed image and generate a well-trained model. To verify the accuracy and robustness of the model, both Monte Carlo simulation data and real data are adopted in the test. The experimental results show that the proposed framework is of great robustness and the reconstructed image is much more accurate in comparison with the traditional methods.

关键词： PET image Direct reconstruction cGANs

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Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR image reconstruction 1

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10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： Schlemper, Jo Salehi, Seyed Sadegh Mohseni Kundu, Prantik Lazarus, Carole Dyvorne, Hadrien Rueckert, Daniel Sofka, Michal Hyperfine Res Guilford CT 06437 USA Imperial Coll London Biomed Image Anal Grp London England

ISBN: (数字)9783030322489

ISBN: (纸本)9783030322489;9783030322472

Deep learning for accelerated magnetic resonance (MR) image reconstruction is a fast growing field, which has so far shown promising results. However, most works are limited in the sense that they assume equidistant rectilinear (Cartesian) data acquisition in 2D or 3D. In practice, a reconstruction from nonuniform samplings such as radial and spiral is an attractive choice for more efficient acquisitions. Nevertheless, it has less been explored as the reconstruction process is complicated by the necessity to handle non-Cartesian samples. In this work, we present a novel approach for reconstructing from nonuniform undersampled MR data. The proposed approach, termed nonuniform variational network (NVN), is a convolutional neural network architecture based on the unrolling of a traditional iterative nonlinear reconstruction, where the knowledge of the nonuniform forward and adjoint sampling operators are efficiently incorporated. Our extensive evaluation shows that the proposed method outperforms existing state-of-the-art deep learning methods, hence offering a method that is widely applicable to different imaging protocols for both research and clinical deployments.

关键词： Magnetic resonance

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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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Reconstructing High-Quality Diffusion MRI data from Orthogonal Slice-Undersampled data Using Graph Convolutional Neural Networks 22nd

Reconstructing High-Quality Diffusion MRI Data from Orthogon...

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10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： Hong, Yoonmi Chen, Geng Yap, Pew-Thian Shen, Dinggang Univ N Carolina Dept Radiol Chapel Hill NC 27515 USA Univ N Carolina BRIC Chapel Hill NC 27515 USA

ISBN: (纸本)9783030322489;9783030322472

Diffusion MRI (dMRI), while powerful for the characterization of tissue microstructure, suffers from long acquisition times. In this paper, we propose a super-resolution (SR) reconstruction method based on orthogonal slice-undersampling for accelerated dMRI acquisition. Instead of scanning full diffusion-weighted (DW) image volumes, only a subsample of equally-spaced slices need to be acquired. We show that complementary information from DWvolumes corresponding to different diffusion wave-vectors can be harnessed using graph convolutional neural networks for reconstruction of the full DW volumes. We demonstrate that our SR reconstruction method outperforms typical interpolation methods and mitigates partial volume effects. Experimental results indicate that acceleration up to a factor of 5 can be achieved with minimal information loss.

关键词： Diffusion MRI Accelerated acquisition Super resolution Graph CNN Adversarial learning

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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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