Method and systems for aliasing artifact reduction in computed tomography imaging

A method, comprising:

• acquiring, with an x-ray detector, a three-dimensional image volume of a subject while the subject moves in a direction relative to an imaging plane defined by the x-ray detector and an x-ray source;
• reconstructing a first plurality of two-dimensional images with a first slice thickness from the three-dimensional image volume along planes perpendicular to the imaging plane;
• reconstructing a second plurality of two-dimensional images with a second slice thickness from the three-dimensional image volume along the planes perpendicular to the imaging plane, the second slice thickness larger than the first slice thickness;
• and
• training a deep neural network to reduce aliasing artifacts in the first plurality of two-dimensional images based on ground truth images generated from the second plurality of two-dimensional images, wherein the trained deep neural network is trained by a super-resolution neural network configured to transform an input image with the second slice thickness to an output image with the first slice thickness, wherein the second slice thickness of the input image is selected to avoid artifacts caused by sub-sampling of acquired data for a given x-ray detector dimension and a scan configuration relative to the first slice thickness.

An imaging system, comprising:

• a gantry with a bore;
• an x-ray source mounted to the gantry and configured to generate x-rays;
• an x-ray detector mounted to the gantry and configured to detect the x-rays;
• and
• a processor configured with instructions in a non-transitory memory that when executed cause the processor to:
• acquire, with the x-ray detector, a three-dimensional image volume of a subject while the subject moves through the bore as the gantry rotates the x-ray detector and the x-ray source around the subject;
• input the three-dimensional image volume to a trained deep neural network to generate a corrected three-dimensional image volume with a reduction in aliasing artifacts, wherein the trained deep neural network is trained by a super-resolution neural network configured to transform an input image with a second slice thickness to an output image with a first slice thickness, wherein the second slice thickness of the input image is selected to avoid the aliasing artifacts caused by sub-sampling of acquired data for a given x-ray detector dimension and a scan configuration relative to the first slice thickness;
• and
• output the corrected three-dimensional image volume.