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We investigate the development of a new analytic approach for 3D PET reconstruction by extension of the chord-based approach for X-ray cone-beam tomography (XCBT). The extension is made possible by recognizing a connection that allows one to reformat 3D PET data into effective cone-beam data so that the chord-based approach can be applied. An important advantage of the new approach is that it can work with general PET system geometries, not limited to the conventional circular geometry. In this paper, we consider a rectangular PET system consisting of four flat-panel detectors. We derive exact analytic reconstruction algorithms for this system and evaluate their performances in numerical studies. Our preliminary results indicate that the derived algorithms can yield accurate reconstructions when applied to noiseless PET data that are generated by assuming the ideal projection model. The algorithms are also observed to respond reasonably well to data noise. Currently, our algorithms do not utilize the entire 3D PET data. Potential modifications to the algorithms for enabling the inclusion of the entire 3D PET data for reconstruction, as well as the theoretical significances and potential practical advantages of this new analytic reconstruction approach, are discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for the exact reconstruction of gray-scale images from projections. The image projections construct an accumulator array, which is used afterwards to reconstruct the original grayscale image by applying the proposed decomposition algorithm. The proposed method determines the number of projections and the number of rays in each projection that are required in order to achieve the reconstruction. These two parameters also define the dimensions of the accumulator array. Using an accumulator array with proper dimensions ensures that there is always a unique characteristic sample for each pixel, which is used during the reconstruction process to extract the pixel's grayscale value. During the reconstruction phase, the sinusoidal contribution of each pixel is removed from the accumulator array. At the end of the decomposition process the accumulator array becomes empty and the original image is exactly reconstructed. The experimental results confirm the robustness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper we present a new algorithm to track an organ in a sequence of medical images in order to achieve a 3D reconstruction. The automatic method that we propose allows the tracking of the external contour of the anatomical organ in all the sequence from one contour initialized by the user on the first image. The required operations for our tracking method are the region-based active contours segmentation. The objects localization with dynamic prediction of displacements is based on the level-set functions and the definition of the region of interest for the robust local estimation of the image model. An application of this method is the 3D reconstruction of abdominal aorta. [ABSTRACT FROM AUTHOR]

In this paper we propose a technique that reconstructs high-resolution images with improved super-resolution algorithms, based on Irani and Peleg iterative method, and employs our suggested initial interpolation, robust image registration, automatic image selection and image enhancement post-processing. When the target of reconstruction is a moving object with respect to a stationary camera, high-resolution images can still be reconstructed, whereas previous systems only work well when we move the camera and the displacement of the whole scene is the same. [ABSTRACT FROM AUTHOR]

Image restoration problems can naturally be cast as constrained convex programming problems in which the constraints arise from a priori information and the observation of signals physically related to the image to be recovered. In this paper, the focus is placed on the construction of constraints based on wavelet representations. Using a mix of statistical and convex-analytical tools, we propose a general framework to construct wavelet-based constraints. The resulting optimization problem is then solved with a block-iterative parallel algorithm which offers great flexibility in terms of implementation. Numerical results illustrate an application of the proposed framework. [ABSTRACT FROM AUTHOR]