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Abstract #0099

Low-dimensional-Structure Self-Learning & Thresholding (LOST): Regularization Beyond Compressed Sensing for MRI Reconstruction

Mehmet Akcakaya1, Tamer Basha1, Beth Goddu1, Lois Goepfert1, Kraig V. Kissinger1, Vahid Tarokh2, Warren J. Manning1, Reza Nezafat1

1Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; 2School of Engineering & Applied Sciences, Harvard University, Cambridge, MA, USA

We develop an improved image reconstruction technique for undersampled acquisitions that learns and utilizes the structure of images being reconstructed. The results of our retrospective study with coronary MRI imply that the proposed method achieves higher acceleration rates compared to conventional CS reconstructions. The pilot prospective acquisitions confirm this finding, and additionally show that our method provides superior image quality at higher rates compared to traditional parallel imaging reconstruction.