Purpose: Acquiring data for CT at low radiation doses has become a pressing goal. Unfortunately, the reduced data quality adversely affects the quality of the reconstructions, impeding their readability. In previous w...
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Purpose: Acquiring data for CT at low radiation doses has become a pressing goal. Unfortunately, the reduced data quality adversely affects the quality of the reconstructions, impeding their readability. In previous work, the authors showed how a prior regular-dose scan of the same patient can efficiently be used to mitigate low-dose artifacts. However, since a prior is not always available, the authors now extend the authors' method to use a database of images of other patients. Methods: The authors' framework first matches the low-dose (target) scan with the images in the database and then selects a set of images that contain anatomical content similar to the target. These "priors" are then registered to the target and form the set of regular-dose priors for restoration via an extended nonlocal means (NLM) filtering framework. To accommodate the larger spatial variability of the patient scans, the authors subdivide the image area into blocks and perform the filtering locally. The database itself is first preprocessed to map each image from its 2D image space to a corresponding high-D image feature space. From this encoding a visual vocabulary is learned that assists in the query of the database. Results: The authors demonstrate the authors' framework via a lung scan example, for both streak artifacts (resulting from smaller projection sets) as well as noise artifacts (resulting from lower mA settings). The authors find that in the authors' particular example case three priors were sufficient to restore all features faithfully. The authors also observe that the authors' method is quite robust in that it generates good results even when the noise conditions significantly worsen (here by 20%). Finally, the authors find that the restoration quality is significantly better than with conventional NLM filtering. Conclusions: The authors image restoration algorithm successfully restores images to high quality when the registration is well performed and also when the prior
We present our current state in developing and testing of Augmented Reality supported spaceflight procedures for intra-vehicular payload activities. Our vision is to support the ground team and the flight crew to auth...
We present our current state in developing and testing of Augmented Reality supported spaceflight procedures for intra-vehicular payload activities. Our vision is to support the ground team and the flight crew to author and operate easily AR guidelines without programming and AR knowledge. For visualization of the procedural instructions using an HMD, 3D registered visual aids are overlaid onto the payload model operated by additional voice control. Embedded informational resources (e.g., images and videos) are provided through a mobile tangible user interface. In a pilot study that was performed at the ESA European Astronaut Centre by application domain experts, we evaluated the performance, workload and acceptance by comparing our AR system with the conventional method of displaying PDF documents of the procedure.
In this paper, we explore various sparse regularization techniques for analyzing fMRI data, such as LASSO, elastic net and the recently introduced k-support norm. Employing sparsity regularization allow us to handle t...
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ISBN:
(纸本)9781467364560
In this paper, we explore various sparse regularization techniques for analyzing fMRI data, such as LASSO, elastic net and the recently introduced k-support norm. Employing sparsity regularization allow us to handle the curse of dimensionality, a problem commonly found in fMRI analysis. We test these methods on real data of both healthy subjects as well as cocaine addicted ones and we show that although LASSO has good prediction, it lacks interpretability since the resulting model is too sparse, and results are highly sensitive to the regularization parameter. We find that we can improve prediction performance over the LASSO using elastic net or the k-support norm, which is a convex relaxation to sparsity with an ℓ 2 penalty that is tighter than the elastic net. Elastic net and k-support norm overcome the problem of overly sparse solutions, resulting in both good prediction and interpretable solutions, while the k-support norm gave better prediction performance. Our experimental results support the general applicability of the k-support norm in fMRI analysis, both for prediction performance and interpretability.
We present an algorithm for interactive structure-preserving retargeting of irregular 3D architecture models, offering the modeler an easy-to-use tool to quickly generate a variety of 3D models that resemble an input ...
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We present an algorithm for interactive structure-preserving retargeting of irregular 3D architecture models, offering the modeler an easy-to-use tool to quickly generate a variety of 3D models that resemble an input piece in its structural style. Working on a more global and structural level of the input, our technique allows and even encourages replication of its structural elements, while taking into account their semantics and expected geometric interrelations such as alignments and adjacency. The algorithm performs automatic replication and scaling of these elements while preserving their structures. Instead of formulating and solving a complex constrained optimization, we decompose the input model into a set of sequences, each of which is a 1D structure that is relatively straightforward to retarget. As the sequences are retargeted in turn, they progressively constrain the retargeting of the remaining sequences. We demonstrate interactivity and variability of results from our retargeting algorithm using many examples modeled after real-world architectures exhibiting various forms of irregularity.
We present a method for structural summarization and abstraction of complex spatial arrangements found in architectural drawings. The method is based on the well-known Gestalt rules, which summarize how forms, pattern...
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We present a method for structural summarization and abstraction of complex spatial arrangements found in architectural drawings. The method is based on the well-known Gestalt rules, which summarize how forms, patterns, and semantics are perceived by humans from bits and pieces of geometric information. Although defining a computational model for each rule alone has been extensively studied, modeling a conjoint of Gestalt rules remains a challenge. In this work, we develop a computational framework which models Gestalt rules and more importantly, their complex interactions. We apply conjoining rules to line drawings, to detect groups of objects and repetitions that conform to Gestalt principles. We summarize and abstract such groups in ways that maintain structural semantics by displaying only a reduced number of repeated elements, or by replacing them with simpler shapes. We show an application of our method to line drawings of architectural models of various styles, and the potential of extending the technique to other computer-generated illustrations, and three-dimensional models.
Iterative CT algorithms are becoming increasingly popular in recent years, and have been found useful when the projections are limited in number, irregularly spaced, or noisy, which are imaging scenarios often encount...
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Iterative CT algorithms are becoming increasingly popular in recent years, and have been found useful when the projections are limited in number, irregularly spaced, or noisy, which are imaging scenarios often encountered in low-dose imaging and compressed sensing. One way to cope with the associated streak and noise artifacts in these settings is either to incorporate or to interleave a regularization objective into the iterative reconstruction framework. In this paper we explore possible techniques for the latter. We investigate a number of non-linear filters popular in the image processing literature for their suitability in iterative CT application, here OS-SIRT.
Graphic process units (GPUs) are well suited to computing-intensive tasks and are among the fastest solutions to perform Computed Tomography (CT) reconstruction. As previous research shows, the bottleneck of GPU-imple...
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Graphic process units (GPUs) are well suited to computing-intensive tasks and are among the fastest solutions to perform Computed Tomography (CT) reconstruction. As previous research shows, the bottleneck of GPU-implementation is not the computational power, but the memory bandwidth. We propose a cache-aware memory-scheduling scheme for the back-projection, which can ensure a better load-balancing between GPU processors and the GPU memory. The proposed reshuffling method can be directly applied on existing GPU-accelerated CT reconstruction pipelines. The experimental results show that our optimization can achieve speedup ranging from 1.18-1.48. Our cache-optimization method is particular effective for low-resolution volumes with high resolution projections.
The advent of GPGPU technologies has allowed for sensible speed-ups in many high-dimension, memory-intensive computational problems. In this paper we demonstrate the effectiveness of such techniques by describing two ...
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The DIRECT approach for 3-D Time-of-Flight (TOF) PET reconstruction performs all iterative predictor-corrector operations directly in image space. A computational bottleneck here is the convolution with the long TOF (...
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The DIRECT approach for 3-D Time-of-Flight (TOF) PET reconstruction performs all iterative predictor-corrector operations directly in image space. A computational bottleneck here is the convolution with the long TOF (resolution) kernels. Accelerating this convolution operation using GPUs is very important especially for spatially variant resolution kernels, which cannot be efficiently implemented in the Fourier domain. The main challenge here is the memory cache performance at non-axis aligned directions. We devised a scheme that first re-samples the image into an axis-aligned orientation offering good memory coherence for the convolution operations. In order to maintain good accuracy, we carefully design the resampling and new convolution kernels to combine into the original TOF kernel. This paper demonstrates the validity, accuracy, and high speed-performance of our scheme for a comprehensive set of orientation angles. Future work will apply these cascaded kernels within a GPU-accelerated version of DIRECT.
Volumetric datasets obtained from scientific simulation and partial differential equation solvers are typically given in the form of non-rectilinear grids. The splatting technique is a popular direct volume rendering ...
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