Imaging of moving targets with multi-static SAR using an overcomplete dictionary

This paper presents a method for imaging of moving targets using multi-static SAR by treating the problem as one of spatial reflectivity signal inversion over an overcomplete dictionary of target velocities. Since SAR sensor returns can be related to the spatial frequency domain projections of the scattering field, we exploit insights from compressed sensing theory to show that moving targets can be effectively imaged with transmitters and receivers randomly dispersed in a multi-static geometry within a narrow forward cone around the scene of interest. Existing approaches to dealing with moving targets in SAR solve a coupled non-linear problem of target scattering and motion estimation typically through matched filtering. In contrast, by using an overcomplete dictionary approach we effectively linearize the forward model and solve the moving target problem as a larger, unified regularized inversion problem subject to sparsity constraints.Comment: This work has been submitted to IEEE Journal on Selected Topics in Signal Processing (Special Issue on MIMO Radar and Its Applications) for possible publicatio

High Fidelity System Modeling for High Quality Image Reconstruction in Clinical CT

Today, while many researchers focus on the improvement of the regularization term in IR algorithms, they pay less concern to the improvement of the fidelity term. In this paper, we hypothesize that improving the fidelity term will further improve IR image quality in low-dose scanning, which typically causes more noise. The purpose of this paper is to systematically test and examine the role of high-fidelity system models using raw data in the performance of iterative image reconstruction approach minimizing energy functional. We first isolated the fidelity term and analyzed the importance of using focal spot area modeling, flying focal spot location modeling, and active detector area modeling as opposed to just flying focal spot motion. We then compared images using different permutations of all three factors. Next, we tested the ability of the fidelity terms to retain signals upon application of the regularization term with all three factors. We then compared the differences between images generated by the proposed method and Filtered-Back-Projection. Lastly, we compared images of low-dose in vivo data using Filtered-Back-Projection, Iterative Reconstruction in Image Space, and the proposed method using raw data. The initial comparison of difference maps of images constructed showed that the focal spot area model and the active detector area model also have significant impacts on the quality of images produced. Upon application of the regularization term, images generated using all three factors were able to substantially decrease model mismatch error, artifacts, and noise. When the images generated by the proposed method were tested, conspicuity greatly increased, noise standard deviation decreased by 90% in homogeneous regions, and resolution also greatly improved. In conclusion, the improvement of the fidelity term to model clinical scanners is essential to generating higher quality images in low-dose imaging

Using shape distributions as priors in a curve evolution framework

In this paper we propose a framework of constructing and using a shape prior in estimation problems. The key novelty of our technique is a new way to use high level, global shape knowledge to derive a local driving force in a curve evolution context. We capture information about shape in the form of a family of shape distributions (cumulative distribution functions) of features related to the shape. We design a prior objective function that penalizes the differences between model shape distributions and those of an estimate. We incorporate this prior in a curve evolution formulation for function minimization. Shape distribution-based representations are shown to satisfy several desired properties, such as robustness and invariance. They also have good discriminative and generalizing properties. To our knowledge, shape distribution-based representations have only been used for shape classification. Our work represents the development of a tractable framework for their incorporation in estimation problems. We apply our framework to three applications: shape morphing, average shape calculation, and image segmentation

An Efficient Region of Interest Acquisition Method for Dynamic Magnetic Resonance Imaging

Motivated by recent work in the area of dynamic magnetic resonance imaging (MRI), we develop a new approach toward the problem of reduced order MRI acquisition. Recent efforts in this field have concentrated on the use of Fourier and Singular Value Decomposition (SVD) methods to obtain low order representations of an entire image plane. We augment this work to the case of imaging an arbitrarily shaped region of interest (ROI) embedded within the full image. After developing a natural error metric for this problem, we show that determining the minimal order required to meet a prescribed error level is in general intractable, but can be solved under certain assumptions. We then develop an optimization approach to the related problem of minimizing the error for a given order. Finally we demonstrate the utility of this approach and its advantages over existing Fourier and SVD methods on a number of MRI images

A modeling effect comparison on LS and TV images: (a) Coronal view of soft contrast section of phantom, (b), (c), and (d) show axial views of line A, B, and C respectively.

<p>The FFS only model (so called Siddon Model, ) shows circular line artifacts in LS and TV as well. In contrast, the proposed model () shows high quality image even in LS without regularization term and significant noise suppression effect on TV.</p

SNR comparison of the three reconstruction methods for the half-dose dataset in Figure 10.

<p>SNR comparison of the three reconstruction methods for the half-dose dataset in Figure 10.</p