Improved image quality and computation reduction in 4-D reconstruction of cardiac-gated SPECT images

Spatiotemporal reconstruction of cardiac-gated SPECT images permits us to obtain valuable information related to cardiac function. However, the task of reconstructing this four-dimensional (4-D) data set is computation intensive. Typically, these studies are reconstructed frame-by-frame: a nonoptimal approach because temporal correlations in the signal are not accounted for. In this work, we show that the compression and signal decorrelation properties of the Karhunen-Loève (KL) transform may be used to greatly simplify the spatiotemporal reconstruction problem. The gated projections are first KL transformed in the temporal direction. This results in a sequence of KL-transformed projection images for which the signal components are uncorrelated along the time axis. As a result, the 4-D reconstruction task is simplified to a series of three-dimensional (3-D) reconstructions in the KL domain. The reconstructed KL components are subsequently inverse KL transformed to obtain the entire spatiotemporal reconstruction set. Our simulation and clinical results indicate that KL processing provides image sequences that are less noisy than are conventional frame-by-frame reconstructions. Additionally, by discarding high-order KL components that are dominated by noise, we can achieve savings in computation time because fewer reconstructions are needed in comparison to conventional frame-by-frame reconstructions.     

Optical Flow Estimation for a Periodic Image Sequence

We propose a temporal modeling approach for determining image motion from a sequence of images wherein the inherent motion is periodic over time. To exploit the periodic nature of the motion, we use a Fourier harmonic representation to model the temporal evolution of the motion field for the entire sequence. We then determine the motion field simultaneously for the different image frames by estimating the parameters of this representation model, where the model order in the Fourier representation serves as a regularization parameter on the temporal coherence of the motion field. This approach can take advantage of the statistics of all the available data in the image sequence. In our experiments, we tested the proposed approach on several motion types at different noise levels, including translational motion, convergent/divergent motion, and cardiac motion. Our results demonstrate that this approach could lead to more robust estimation of the motion field in the presence of strong imaging noise compared to a frame-by-frame estimation approach.     

Motion analysis and segmentation through spatio-temporal slices processing

This paper presents new approaches in characterizing and segmenting the content of video. These approaches are developed based upon the pattern analysis of spatio-temporal slices. While traditional approaches to motion sequence analysis tend to formulate computational methodologies on two or three adjacent frames, spatio-temporal slices provide rich visual patterns along a larger temporal scale. We first describe a motion computation method based on a structure tensor formulation. This method encodes visual patterns of spatio-temporal slices in a tensor histogram, on one hand, characterizing the temporal changes of motion over time, on the other hand, describing the motion trajectories of different moving objects. By analyzing the tensor histogram of an image sequence, we can temporally segment the sequence into several motion coherent subunits, in addition, spatially segment the sequence into various motion layers. The temporal segmentation of image sequences expeditiously facilitates the motion annotation and content representation of a video, while the spatial decomposition of image sequences leads to a prominent way of reconstructing background panoramic images and computing foreground objects.     

Dynamic X-ray computed tomography

Dynamic computed tomography (CT) imaging aims at reconstructing image sequences where the dynamic nature of the living human body is of primary interest. The main applications concerned are image-guided interventional procedures, functional studies and cardiac imaging. The introduction of ultra-fast rotating gantries along with multi-row detectors and in near future area detectors allows huge progress toward the imaging of moving organs with low-contrast resolution. This paper gives an overview of the different concepts used in dynamic CT. A new reconstruction algorithm based on a voxel-specific dynamic evolution compensation is also presented. It provides four-dimensional image sequences with accurate spatio-temporal information, where each frame is reconstructed using a long-scan acquisition mode on several half-turns. In the same time, this technique permits to reduce the dose delivered per rotation while keeping the same signal to noise ratio for every frame using an adaptive motion-compensated temporal averaging. Results are illustrated on simulated data.     

Group-based spatio-temporal video analysis and abstraction using wavelet parameters

In this paper, we present a spatio-temporal event-based approach to video signal analysis and abstraction employing wavelet transform features. The video signal is assumed to be a sequence of overlapping independent visual components called events, which typically are temporally overlapping compact functions that describe temporal evolution of a given set of the spatial parameters of the video signal. We utilize event-based temporal decomposition technique to resolve the overlapping arrangement of the video signal that is known to be one of the main concerns in video analysis via conventional frame-based schemes. In our method, a set of spatial parameters, extracted from the video, is expressed as a linear combination of a set of temporally overlapping compact functions, called events, through an optimization process. First, to reduce computational complexity, the video sequence is divided into overlapped groups. Next, Generalized Gaussian Density (GGD) parameters, extracted from 2D wavelet transform subbands, are used as the spatial parameters. Temporal decomposition is then applied to the GGD parameters, structured as a frame-based matrix of GGD vectors, to compute the event functions and associated orthogonal GGD parameters. Frames located at event centroids, which are much smaller in number than the number of frames in the original video, are taken as candidates for the keyframes that are selected based on a distance criterion in the feature space. Our contribution is that this still image video abstraction scheme does not need shot or cluster boundary detection, unlike current methods. Experimental results confirm the efficiency and accuracy of our approach.     

Super-resolution image reconstruction: a technical overview

A new approach toward increasing spatial resolution is required to overcome the limitations of the sensors and optics manufacturing technology. One promising approach is to use signal processing techniques to obtain an high-resolution (HR) image (or sequence) from observed multiple low-resolution (LR) images. Such a resolution enhancement approach has been one of the most active research areas, and it is called super resolution (SR) (or HR) image reconstruction or simply resolution enhancement. In this article, we use the term "SR image reconstruction" to refer to a signal processing approach toward resolution enhancement because the term "super" in "super resolution" represents very well the characteristics of the technique overcoming the inherent resolution limitation of LR imaging systems. The major advantage of the signal processing approach is that it may cost less and the existing LR imaging systems can be still utilized. The SR image reconstruction is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, including medical imaging, satellite imaging, and video applications. The goal of this article is to introduce the concept of SR algorithms to readers who are unfamiliar with this area and to provide a review for experts. To this purpose, we present the technical review of various existing SR methodologies which are often employed. Before presenting the review of existing SR algorithms, we first model the LR image acquisition process.     

Computational Reconstruction of Three-Dimensional Integral Imaging by Rearrangement of Elemental Image Pixels

In this paper, we present a method to implement computational three-dimensional (3D) integral imaging (II). This method is based on Pixels of the Elemental Image Rearrangement Technique (PERT). In our proposed method for computational reconstruction of II, the reconstructed 3D image is obtained by using the entire elemental images which are captured from the lenslet array. Instead of averaging the elemental images, our proposed method rearranges pixels of each elemental image. Therefore, the reconstructed 3D image has the same number of pixels as the entire elemental images' pixels. To verify this computational reconstruction method, we have implemented optical experiments.      

A super-resolution reconstruction algorithm for hyperspectral images

The spatial resolution of a hyperspectral image is often coarse because of the limitations of the imaging hardware. Super-resolution reconstruction (SRR) is a promising signal post-processing technique for hyperspectral image resolution enhancement. This paper proposes a maximum a posteriori (MAP) based multi-frame super-resolution algorithm for hyperspectral images. Principal component analysis (PCA) is utilized in both parts of the proposed algorithm: motion estimation and image reconstruction. A simultaneous motion estimation method with the first few principal components, which contain most of the information of a hyperspectral image, is proposed to reduce computational load and improve motion field accuracy. In the image reconstruction part, different image resolution enhancement techniques are applied to different groups of components, to reduce computational load and simultaneously remove noise. The proposed algorithm is tested on both synthetic images and real image sequences. The experimental results and comparative analyses verify the effectiveness of this algorithm.     

Fast algorithms for GS-model-based image reconstruction in data-sharing Fourier imaging

Many imaging experiments involve acquiring a time series of images. To improve imaging speed, several "data-sharing" methods have been proposed, which collect one (or a few) high-resolution reference(s) and a sequence of reduced data sets. In image reconstruction, two methods, known as "Keyhole" and reduced-encoding imaging by generalized-series reconstruction (RIGR), have been used. Keyhole fills in the unmeasured high-frequency data simply with those from the reference data set(s), whereas RIGR recovers the unmeasured data using a generalized series (GS) model, of which the basis functions are constructed based on the reference image(s). This correspondence presents a fast algorithm (and two extensions) for GS-based image reconstruction. The proposed algorithms have the same computational complexity as the Keyhole algorithm, but are more capable of capturing high-resolution dynamic signal changes.     

融合空间-时间双网络流和视觉注意的人体行为识别

该文受人脑视觉感知机理启发,在深度学习框架下提出融合时空双网络流和视觉注意的行为识别方法。首先,采用由粗到细Lucas-Kanade估计法逐帧提取视频中人体运动的光流特征。然后,利用预训练模型微调的GoogLeNet神经网络分别逐层卷积并聚合给定时间窗口视频中外观图像和相应光流特征。接着,利用长短时记忆多层递归网络交叉感知即得含高层显著结构的时空流语义特征序列;解码时间窗口内互相依赖的隐状态;输出空间流视觉特征描述和视频窗口中每帧标签概率分布。其次,利用相对熵计算时间维每帧注意力置信度,并融合空间网络流感知序列标签概率分布。最后,利用softmax分类视频中行为类别。实验结果表明,与其他现有方法相比,该文行为识别方法在分类准确度上具有显著优势。     

New approach to Doppler tomography for microwave imaging

A new approach to microwave imaging is presented. The requirements for large bandwidth and the computational burden in conventional range-Doppler imaging are reduced. In conventional microwave imaging, one of the steps in reconstructing a Doppler tomogram is to perform a two-dimensional Fourier transform which utilizes and produces data points in a Cartesian raster. The computational burden associated with this reconstruction is high. In contrast, the approach proposed in this paper is, in essence, a phase-based process rather than an amplitude-based process. Backscattered signals from the illuminated target are received and analysed using the wavelet transform to extract the Doppler variation information which contains the spatial distribution information of the scattering centres. Diagrams which are very similar to the 'sinogram’ used in X-ray tomography are obtained and are used to reconstruct the two-dimensional images. Simulated and experimental results are shown to confirm the practicality of the proposed approach. The quality of the images shown in this paper is satisfactory and the computational burden is greatly reduced.     

A new iterated two-band diffusion equation:theory and its application

We propose an iterated two-band filtering method to solve the selective image smoothing problem. We prove that a discrete computation step in an iterated nonlinear diffusion-based filtering algorithm is equivalent to a sequence of operations, including decomposition, regularization, and then reconstruction, in the proposed two-band filtering scheme. To correctly separate the high frequency components from the low frequency ones in the decomposition process, we adopt a dyadic wavelet-based approximation scheme. In the regularization process, we use a diffusivity function as a guide to retain useful data and suppress noises. Finally, the signal of the next stage, which is a "smoother" version of the signal at the previous stage, can be computed by reconstructing the decomposed low frequency component and the regularized high frequency component. Based on the proposed scheme, the smoothing operation can be applied to the correct targets. Experimental results show that our new approach is really efficient in noise removing.     

SNR enhancement in radial SSFP imaging using partial k-space averaging

The steady-state free precessing (SSFP) sequences, widely used in MRI today, acquire data only during a short fraction of the repetition time (TR). Thus, they exhibit a poor scan efficiency. In this paper, a novel approach to extending the acquisition window for a given TR without considerably modifying the basic sequence is explored for radial SSFP sequences. The additional data are primarily employed to increase the signal-to-noise ratio, rather than to improve the temporal resolution of the imaging. The approach is analyzed regarding its effect on the image SNR (signal to noise ratio) and the reconstruction algorithm. Results are presented for phantom experiments and cardiac functions studies. The gain in SNR is most notable in rapid imaging, since SNR enhancement for a constant repetition time may be used to compensate for the increase in noise resulting from angular undersampling.     

Temporal integration method for image-processing-basedsuper-high-resolution image acquisition

The authors propose an image processing-based approach towards the development of a super-high-resolution image acquisition system. Imaging methods based on this approach can be classified into two main categories: a spatial integration imaging method and a temporal integration imaging method. With regard to the spatial integration imaging method, the authors have previously presented a method for acquiring an improved-resolution image by integrating multiple images taken simultaneously with multiple different cameras. They develop their work, aiming at a particular class of application where a user indicates a region of interest (ROI) on an observed image in advance, and apply a prototypal temporal integration imaging method. The prototypal temporal integration imaging method does not involve global image segmentation, but uses a subpixel registration algorithm which describes an image warp within the ROI, with subpixel accuracy, as a deformation of quadrilateral patches. The method then performs a subpixel registration by warping an observed image with the warping function recovered from the deformed quadrilateral patches. Experimental simulations demonstrate that the temporal integration imaging is promising as a basic means of high resolution imaging     

Bayesian approach with the maximum entropy principle in image reconstruction from microwave scattered field data

Microwave imaging is of great interest in medical applications owing to its high sensitivity with respect to dielectric properties. It allows detection of very small inhomogeneities. The image reconstruction employing the microwave inverse scattering consists of reconstructing the image of an object from the scattered field measured behind the object. This reconstruction runs up against the nonuniqueness of the solution of the inverse scattering problem. The authors propose to solve the ill-posed inverse problem by a statistical regularization method based on the Bayesian maximum a posteriori estimation where the principle of maximum entropy is used for assigning the a priori laws. The results obtained demonstrate the power and potential of this method in image reconstruction.     

The gridding method for image reconstruction by Fourier transformation

The authors explore a computational method for reconstructing an n-dimensional signal f from a sampled version of its Fourier transform f;. The method involves a window function w; and proceeds in three steps. First, the convolution g;=w;*f; is computed numerically on a Cartesian grid, using the available samples of f;. Then, g=wf is computed via the inverse discrete Fourier transform, and finally f is obtained as g/w. Due to the smoothing effect of the convolution, evaluating w;*f; is much less error prone than merely interpolating f;. The method was originally devised for image reconstruction in radio astronomy, but is actually applicable to a broad range of reconstructive imaging methods, including magnetic resonance imaging and computed tomography. In particular, it provides a fast and accurate alternative to the filtered backprojection. The basic method has several variants with other applications, such as the equidistant resampling of arbitrarily sampled signals or the fast computation of the Radon (Hough) transform.     

瞬态成像模式下高帧频CMOS图像传感器性能研究

高帧频CMOS图像传感器具有集成度高、帧频高、功耗低、抗干扰抗辐照能力强等特性,在科学实验中应用广泛。为提高外同步触发瞬态成像模式下的成像性能,首先介绍了基于高帧频CIS(5T像素,超大快门)的瞬态成像系统构成及其工作模式;从像素结构出发,对该款CIS在不同工作模式下的成像性能进行了理论分析;搭建了基于EMVA1288的标准化测试平台,对瞬态工作模式下的多项关键性能指标进行了测试,并与稳态工作模式下的性能进行了对比。分析结果表明:与稳态工作模式相比,瞬态成像模式下图像传感器具有更大的暗本底和固定模式噪声,但传感器的时序噪声、光响应非均匀性优于稳态工作模式,具有更高的信噪比和动态范围,与理论分析基本吻合。测试分析结果可用于指导科学成像系统设计与性能优化。     

TRIO a technique for reconstruction using intensity order: application to undersampled MRI

Long acquisition times are still a limitation for many applications of magnetic resonance imaging (MRI), specially in 3-D and dynamic imaging. Several undersampling reconstruction techniques have been proposed to overcome this problem. These techniques are based on acquiring less samples than specified by the Nyquist criterion and estimating the nonacquired data by using some sort of prior information. Most of these reconstruction methods use prior information based on estimations of the pixel intensities of the images and therefore they are prone to introduce spatial or temporal blurring. Instead of using the pixel intensities, we propose to use information that allows us to sort the pixels of an image from darkest to brightest. The set of order relations which sort the pixels of an image has been called intensity order. The intensity order of an image can be estimated from low-resolution images, adjacent slices in volumetric acquisitions, temporal correlation in dynamic sequences or from prior reconstructions. Our technique for reconstruction using intensity order (TRIO) consists of looking for an image that satisfies the intensity order and minimizes the discrepancy between the acquired and reconstructed data. Results show that TRIO can effectively reconstruct 2-D-cine cardiac MR images (under-sampling factor of 4), estimating correctly the temporal evolution of the objects. Furthermore, TRIO is used as a second stage reconstruction after reconstructing with other techniques, keyhole, sliding window and k-t BLAST, to estimate the order information. In all cases the images are improved by TRIO.