Dictionary learning for compressive parameter mapping in magnetic resonance imaging

Benjamin Paul Berman; Mahesh Bharath Keerthivasan; Zhitao Li; Diego R. Martin; Maria I. Altbach; Ali Bilgin

doi:10.1117/12.2187088

24 August 2015 Dictionary learning for compressive parameter mapping in magnetic resonance imaging

Benjamin Paul Berman, Mahesh Bharath Keerthivasan, Zhitao Li, Diego R. Martin, Maria I. Altbach, Ali Bilgin

Proceedings Volume 9597, Wavelets and Sparsity XVI; 959707 (2015) https://doi.org/10.1117/12.2187088
Event: SPIE Optical Engineering + Applications, 2015, San Diego, California, United States

Abstract

Parameter mapping is a valuable quantitative tool for soft tissue contrast. Accelerated data acquisition is critical for clinical utility, which has lead to various novel reconstruction techniques. In this work, a model-based compressed sensing method is extended to include a sparse regularization that is learned from the principal component coefficient. The principal components for a range of T₂ decay curves are computed, and the coefficients of the principal components are reconstructed. These coefficient maps share coherent spatial structures, suggesting a patch{based dictionary is a well suited sparse transformation. This transformation is learned from the coefficients themselves. The proposed reconstruction is suited for non-Cartesian, multi-channel data. The dictionary constraint leads to parameter maps with less noise and less aliasing for high amounts of acceleration.

Citation Download Citation

Benjamin Paul Berman, Mahesh Bharath Keerthivasan, Zhitao Li, Diego R. Martin, Maria I. Altbach, and Ali Bilgin "Dictionary learning for compressive parameter mapping in magnetic resonance imaging", Proc. SPIE 9597, Wavelets and Sparsity XVI, 959707 (24 August 2015); https://doi.org/10.1117/12.2187088

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available