Limited-angle tomography has gained much interest in late years Nevertheless, imagereconstructionfromincomplete projections is a classic ill-posed issue in the field of computational imaging. In this paper, we prop...
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ISBN:
(数字)9781510628298
ISBN:
(纸本)9781510628298
Limited-angle tomography has gained much interest in late years Nevertheless, imagereconstructionfromincomplete 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.
Diffuse optical tomography (DOT) images the distribution of the optical properties, such as the absorption and scattering coefficients, via the imagereconstructionfrom the light intensities measured at the surface o...
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ISBN:
(数字)9781510628427
ISBN:
(纸本)9781510628410;9781510628427
Diffuse optical tomography (DOT) images the distribution of the optical properties, such as the absorption and scattering coefficients, via the imagereconstructionfrom the light intensities measured at the surface of the biological medium. The changes in the optical properties reflect the conditions of the tissues. Therefore, DOT image can provide the information which is not obtained from the other modalities and is useful for medical diagnoses. In this study, the application of the DOT to thyroid cancer diagnosis was investigated. The ultrasound technique is usually carried out for the thyroid cancer diagnosis. It is, however, difficult to distinguish follicular carcinoma from adenoma of thyroid. The optical properties may be helpful for the diagnosis. The imagereconstruction algorithm employing the regularization minimizing l(p)-norm (0 < p < 2) of the reconstructed image was developed. The image was reconstructed from the time-resolved measurement data. The numerical simulations of the imagereconstruction were tried. The numerical simulation demonstrated that the developed algorithm was able to image the changes in the optical properties in the medium. Additionally, the imagereconstruction of the numerical neck phantom was simulated. The thyroid cancer region was reconstructed successfully. It was demonstrated that the developed algorithm had the possibility to image thyroid cancer.
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 impo...
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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.
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...
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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 incompletedata 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.
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 o...
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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 imagereconstruction for limited-angle artifacts reduction for dual-panel TOF PET imaging. The deep imagereconstruction 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.
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 si...
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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 incompletedata 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.
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...
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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 imagereconstruction 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.
3D object instance reconstructionfrom 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 ...
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ISBN:
(数字)9781728193601
ISBN:
(纸本)9781728193618
3D object instance reconstructionfrom 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].
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 ...
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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 imagereconstruction times in the CS setting. We leverage the segmented acquisition in multiple shots of k-space data to interleave the MR acquisition and imagereconstruction 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 imagereconstruction may start fromincompletedata. 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 imagereconstruction 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 imagereconstruction 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
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 sp...
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