Abstract #4584

Sparse Image Reconstruction Using the Generalized Sampling Theorem for MR Angiography

Nicole Seiberlich¹, Hyun Jeong², Timothy J. Carroll^2,3, Mark A. Griswold¹

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

GST-MRA, a novel method to reconstruct highly undersampled sparse images using ideas from the Generalized Sampling Theorem, is introduced here. In order to reduce the number of image pixels to be reconstructed, a soft mask is created using the composite image, and only those pixels which contain signal in this mask are reconstructed. This method is demonstrated for the reconstruction of high frame rate images of an AVM patient with a 2D acceleration factor of >20. In addition, parallel imaging in the form of coil sensitivity maps can also be incorporated into the method, further increasing the reconstruction fidelity.