Dynamic image reconstruction with tight frame learning

摘要

A computer-implemented method for learning a tight frame includes acquiring undersampled k-space data over a time period using an interleaved process. An average of the undersampled k-space data is determined and a reference image is generated based on the average of the undersampled k-space data. Next, a tight frame operator is determined based on the reference image. Then, a reconstructed image data is generated from the undersampled k-space data via a sparse reconstruction which utilizes the tight frame operator.

主权项

1. A computer-implemented method for reconstructing an image based on a learned tight frame, the method comprising:
acquiring undersampled k-space data over a time period using an interleaved process; determining an average of the undersampled k-space data; generating a reference image based on the average of the undersampled k-space data; determining a tight frame operator based on the reference image; and generating a reconstructed image data from the undersampled k-space data via a sparse reconstruction which utilizes the tight frame operator, wherein determining a tight frame operator based on the reference image comprises: determining a reference vector based on the reference image; initializing one or more tight frame filters using an existing tight frame system; and performing an iterative process comprising:
defining an analysis operator based on the tight frame filters,
determining a coefficient vector comprising a plurality of tight frame coefficients by applying the analysis operator to the reference vector,updating the coefficient vector by applying a hard thresholding operator to the tight frame coefficients, and updating the tight frame filters based on the updated coefficient vector.