Spatial-spectral analysis by augmented modeling of 3d image appearance characteristics with application to radio frequency tagged cardiovascular magnetic resonance

A method for processing a tagged magnetic resonance image, the method comprising:

• receiving a plurality of magnetic resonance images associated with different time slices of a cardiac cycle, wherein each magnetic resonance image includes a plurality of tags, and wherein each magnetic resonance image includes a plurality of pixels, each pixel including an associated gray level value;
• generating a spatiotemporal three dimensional (3D) model based on the magnetic resonance images including a plurality of voxels based on the pixels of the magnetic resonance images, wherein each voxel includes an associated gray level based on the gray levels of the pixels, and wherein each voxel corresponds with a tag or background;
• analyzing the voxels of the 3D model using a first order model and a second order model to classify each voxel as corresponding to a tag or background;
• adjusting the gray level associated with each voxel based on the classification of the voxel;
• and
• deconstructing the 3D model into processed tagged magnetic resonance images, wherein the gray level values of each pixel is adjusted based on the adjusted gray level of the voxels.

A method for enhancing a plurality of images for use in spectral tracking, the method comprising:

• receiving image data for a plurality of images respectively associated with a plurality of times, wherein each image includes a plurality of pixels, each pixel including an associated gray level value;
• generating a spatiotemporal three dimensional (3D) model based on the image data including a plurality of voxels based on the pixels of the images, wherein each voxel includes an associated gray level based on the gray levels of the pixels, wherein each voxel corresponds to one of a plurality of values of a parameter, and wherein a first dimension of the 3D model is a temporal dimension;
• and
• classifying each voxel as corresponding to one of the plurality of values of the parameter based at least in part on a Markov-Gibbs random field model.

An apparatus, comprising:

• at least one processor;
• and
• program code configured upon execution by the at least one processor to process a tagged magnetic resonance image by:
• receiving a plurality of magnetic resonance images associated with different time slices of a cardiac cycle, wherein each magnetic resonance image includes a plurality of tags, and wherein each magnetic resonance image includes a plurality of pixels, each pixel including an associated gray level value;
• generating a spatiotemporal three dimensional (3D) model based on the magnetic resonance images including a plurality of voxels based on the pixels of the magnetic resonance images, wherein each voxel includes an associated gray level based on the gray levels of the pixels, and wherein each voxel corresponds with a tag or background;
• analyzing the voxels of the 3D model using a first order model and a second order model to classify each voxel as corresponding to a tag or background;
• adjusting the gray level associated with each voxel based on the classification of the voxel;
• and
• deconstructing the 3D model into processed tagged magnetic resonance images, wherein the gray level values of each pixel is adjusted based on the adjusted gray level of the voxels.

A program product, comprising:

• a computer readable medium;
• and
• program code stored on the computer readable medium and configured upon execution by at least one processor process a tagged magnetic resonance image by:
• receiving a plurality of magnetic resonance images associated with different time slices of a cardiac cycle, wherein each magnetic resonance image includes a plurality of tags, and wherein each magnetic resonance image includes a plurality of pixels, each pixel including an associated gray level value;
• generating a spatiotemporal three dimensional (3D) model based on the magnetic resonance images including a plurality of voxels based on the pixels of the magnetic resonance images, wherein each voxel includes an associated gray level based on the gray levels of the pixels, and wherein each voxel corresponds with a tag or background;
• analyzing the voxels of the 3D model using a first order model and a second order model to classify each voxel as corresponding to a tag or background;
• adjusting the gray level associated with each voxel based on the classification of the voxel;
• and
• deconstructing the 3D model into processed tagged magnetic resonance images, wherein the gray level values of each pixel is adjusted based on the adjusted gray level of the voxels.