Abstract: A method for performing Computed Tomography (CT) reconstruction includes acquiring a sparse measurement matrix using a CT scanner and applying a reconstruction process over a number of iterations to reconstruct image data from the sparse measurement matrix. The reconstruction process performed during each respective iteration includes generating a random view subset and determining a portion of the sparse measurement matrix corresponding to the random view subset. The reconstruction process further includes performing a stochastic gradient descent on the portion of the sparse measurement matrix to yield an image, applying a proximal total variation regularization to the image, and adjusting a step size associated with the stochastic gradient descent.

Abstract: A computer-implemented method for reconstructing magnetic resonance images using a parallel computing platform comprising a host unit and a graphical processing device includes receiving a plurality of coil data sets from a magnetic resonance imaging system, each respective coil data set comprising scanner data and a coil sensitivity map associated with a distinct coil included in the magnetic resonance imaging system. An iterative compressed-sensing reconstruction process is applied to reconstruct an image based on the plurality of coil data sets.

Abstract: A method for performing Computed Tomography (CT) reconstruction includes acquiring a sparse measurement matrix using a CT scanner and applying a reconstruction process over a number of iterations to reconstruct image data from the sparse measurement matrix. The reconstruction process performed during each respective iteration includes generating a random view subset and determining a portion of the sparse measurement matrix corresponding to the random view subset. The reconstruction process further includes performing a stochastic gradient descent on the portion of the sparse measurement matrix to yield an image, applying a proximal total variation regularization to the image, and adjusting a step size associated with the Acquire CT sparse measurement stochastic gradient descent.

Abstract: An image transformation method, performed by a computer, for determining a transformation between two medical images of an anatomical structure, respectively taken at a first and second point in time, said method comprising the steps of: acquiring first and second image datasets which represent the first and second image, respectively; determining an image-based transformation between the first image and the second image; acquiring an elastic model of the anatomical structure at the first point in time, wherein the elastic model comprises a plurality of nodes and the relationships between the nodes; calculating the displacements of a subset of the nodes in accordance with the image-based transformation; calculating the displacements of the other nodes in accordance with the relationships between the nodes as represented by the elastic model, while keeping the displacements of the nodes in the subset of nodes constant; and calculating the transformation from the displacement vectors which represent the displac

Abstract: An image transformation method, performed by a computer, for determining a transformation between two medical images of an anatomical structure, respectively taken at a first and second point in time, said method comprising the steps of: acquiring first and second image datasets which represent the first and second image, respectively; determining an image-based transformation between the first image and the second image; acquiring an elastic model of the anatomical structure at the first point in time, wherein the elastic model comprises a plurality of nodes and the relationships between the nodes; calculating the displacements of a subset of the nodes in accordance with the image-based transformation; calculating the displacements of the other nodes in accordance with the relationships between the nodes as represented by the elastic model, while keeping the displacements of the nodes in the subset of nodes constant; and calculating the transformation from the displacement vectors which represent the displac

Abstract: A computer-implemented method for reconstructing magnetic resonance images using a parallel computing platform comprising a host unit and a graphical processing device includes receiving a plurality of coil data sets from a magnetic resonance imaging system, each respective coil data set comprising scanner data and a coil sensitivity map associated with a distinct coil included in the magnetic resonance imaging system. An iterative compressed-sensing reconstruction process is applied to reconstruct an image based on the plurality of coil data sets.