Tomographic reconstruction based on deep learning

A method comprising:

• obtaining measured data from a tomography scanner;
• providing inputs to a trained neural network comprising one or more of the measured data or one or more tomographic transforms of the measured data, wherein the neural network comprises at least one layer based on wavelets, wavelet frames, curvelets, or other sparsifying transforms, wherein the inputs further comprise a reconstructed image in addition to the one or more tomographic transforms or the one or more of the measure data, wherein the reconstructed image comprises at least one filtered backprojection image with a special filter kernel, wherein the filter coefficients are selected so as to preserve the original sinogram after a reprojection;
• and
• obtaining one or more outputs from the trained neural network based on the inputs.

An image processing system comprising:

• a processing component configured to execute one or more stored processor-executable routines;
• and
• a memory storing the one or more executable-routines, wherein the one or more executable routines, when executed by the processing component, cause acts to be performed comprising:
• acquiring or accessing a set of scan data, wherein the set of scan data is initially represented by a set of original measurements;
• calculating one or more tomographic transforms of the set of scan data;
• providing the one or more tomographic transforms and an input reconstructed image as inputs to a trained neural network, wherein the input reconstructed image comprises a filtered backprojection image with a special filter kernel, wherein the filter coefficients are selected that preserve the original sinogram after a reprojection, and wherein the trained neural network comprises at least one layer based on a wavelet filter bank, wavelets, wavelet frames, curvelets, or other sparsifying transforms;
• and
• obtaining one or more outputs from the trained neural network based on the inputs.