An ica algorithm for analyzing multiple data sets

In this paper we derive an independent-component analysis (ICA) method for analyzing two or more data sets simultaneously. Our model permits there to be components individual to the various data sets, and others that are common to all the sets. We explore the assumed time autocorrelation of independent signal components and base our algorithm on prediction analysis. We illustrate the algorithm using a simple image separation example. Our aim is to apply this method to functional brain mapping using functional magnetic resonance imaging (fMRI). 1

An Introduction to the Inverse Quantum Bound State Problem in One Dimension

A technique to reconstruct one-dimensional, reflectionless potentials and the associated quantum wave functions starting from a finite number of known energy spectra is discussed. The method is demonstrated using spectra that scale like the lowest energy states of standard problems encountered in the undergraduate curriculum such as: the infinite square well, the simple harmonic oscillator, and the one-dimensional hydrogen atom.Comment: 10 pages, 10 figures, Submitted to Am. J. Phys. August 201

A Spatially Robust ICA Algorithm for Multiple fMRI Data Sets

In this paper we derive an independent-component analysis (ICA) method for analyzing two or more data sets simultaneously. Our model extracts independent components common to all data sets and independent data-set-specific components. We use time-delayed autocorrelations to obtain independent signal components and base our algorithm on prediction analysis. We applied this method to functional brain mapping using functional magnetic resonance imaging (fMRI). The results of our 3-subject analysis demonstrate the robustness of the algorithm to the spatial misalignment intrinsic in multiple-subject fMRI data sets. 1

Method for Detecting a Mass Density Image of an Object

A method for detecting a mass density image of an object. An x-ray beam is transmitted through the object and a transmitted beam is emitted from the object. The transmitted beam is directed at an angle of incidence upon a crystal analyzer. A diffracted beam is emitted from the crystal analyzer onto a detector and digitized. A first image of the object is detected from the diffracted beam emitted from the crystal analyzer when positioned at a first angular position. A second image of the object is detected from the diffracted beam emitted from the crystal analyzer when positioned at a second angular position. A refraction image is obtained and a regularized mathematical inversion algorithm is applied to the refraction image to obtain a mass density image.Sponsorship: Illinois Institute of TechnologyUnited States Paten

CONTENT-ADAPTIVE 3D MESH MODELING FOR REPRESENTATION OF VOLUMETRIC IMAGES

ABSTRACT In this work we present a fast, content-adaptive approach for three-dimensional (3D

ICIP-2001 paper proposal: Tomographic Image Reconstruction Using Content-Adaptive Mesh Modeling

In this work we propose the use of a content-adaptive mesh model (CAMM) for tomographic image reconstruction. In the proposed framework, the image to be reconstructed is first modeled by an efficient mesh representation. The image is then obtained through estimation of the nodal values from the measured data. The use of a CAMM can greatly alleviate the ill-posed nature of the reconstruction problem, thereby leading to improved quality in the reconstructed images. In addition, it can also lead to development of efficient numerical reconstruction algorithms. The proposed methods are tested using gated cardiac-perfusion images. Results demonstrate that the proposed approach achieves the best performance when compared to several commonly used methods for image reconstruction, and produces results very rapidly. 1