Siemens Healthcare Gmbh

Johannes Flake

Mariappan S Nadar

Boris Mailhe

Qiu Wang

Laszlo Lazar

Yuanhsi Chen

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.

Fast sparse computed tomography image reconstruction from few views

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. Each iteration of the iterative compressed-sensing reconstruction process comprises: individually transferring the plurality of coil data sets from the host unit to the graphical processing device using a plurality of asynchronous data streams, overlapping with transfer of the plurality of coil data sets, optimizing a plurality of data fidelity values on the graphical processing device, each respective data fidelity value corresponding to a distinct coil data set, and computing an estimated image on the graphical processing device based on the plurality of data fidelity values.

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Princeton, NJ, United States of America

Johannes Flake