Work

Dynamic MRI Using SmooThness Regularization on Manifolds (SToRM)

Year: 2015

Type: article

Abstract: We introduce a novel algorithm to recover real time dynamic MR images from highly under-sampled k- t space measurements. The proposed scheme models the images in the dynamic dataset as points on a smooth, low dimensional manifold in high dimensional space. We propose to exploit the non-linear and non-local redundancies in the dataset by posing its recovery as a manifold smoothness regularized optimization problem. A navigator acquisition scheme is used to determine the structure of the manifold, or equivalently the associated graph Laplacian matrix. The estimated Laplacian matrix is used to recover the dataset from undersampled measurements. The utility of the proposed scheme is demonstrated by comparisons with state of the art methods in multi-slice real-time cardiac and speech imaging applications.

Source: IEEE Transactions on Medical Imaging

DOI: 10.1109/tmi.2015.2509245

Authors: Sunrita Poddar, Mathews Jacob

Institution: University of Iowa

Language: English

Topic: Advanced MRI Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: green

Funders: American Cancer Society, Office of Naval Research, National Institute of Biomedical Imaging and Bioengineering, Division of Computing and Communication Foundations

Awards: G1410151463, G2254959514, G7350337303, Novel Computational Framework for Free-Breathing & Ungated Dynamic MRI