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Abstract #4873

Parameter-Free Reconstruction of Highly Undersampled MR Angiography Images Using Gradient Descent with Sparsification

Nicole Seiberlich1, Hyun J. Jeong2, Timothy J. Carroll2, Mark A. Griswold1,3

1Radiology, Case Western Reserve University, Cleveland, OH, United States; 2Radiology, Northwestern University, Chicago, IL, United States; 3Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States

Gradient Descent with Sparsification, a novel image reconstruction technique, has been applied to the generation of images from highly undersampled MR Angiography data. Unlike other techniques, this method can be implemented using no external para-meters, allowing completely unsupervised reconstructions. The extremely high acceleration factors shown here are made possible by initializing a given time frame with the previous frame, such that only differences must be reconstructed. Temporal resolutions of 180ms/frame have been achieved by undersampling the collected data by a factor of R~75 (using 4 projections per partition per frame) with no venous contamination and little residual streaking or blurring.