TY - GEN
T1 - Data-driven adaptation of a union of sparsifying transforms for blind compressed sensing MRI reconstruction
AU - Ravishankar, Saiprasad
AU - Bresler, Yoram
N1 - Publisher Copyright:
© 2015 SPIE.
PY - 2015
Y1 - 2015
N2 - Compressed Sensing has been demonstrated to be a powerful tool for magnetic resonance imaging (MRI), where it enables accurate recovery of images from highly undersampled k-space measurements by exploiting the sparsity of the images or image patches in a transform domain or dictionary. In this work, we focus on blind compressed sensing, where the underlying sparse signal model is a priori unknown, and propose a framework to simultaneously reconstruct the underlying image as well as the unknown model from highly undersampled measurements. Specifically, our model is that the patches of the underlying MR image(s) are approximately sparse in a transform domain. We also extend this model to a union of transforms model that is better suited to capture the diversity of features in MR images. The proposed block coordinate descent type algorithms for blind compressed sensing are highly efficient. Our numerical experiments demonstrate the superior performance of the proposed framework for MRI compared to several recent image reconstruction methods. Importantly, the learning of a union of sparsifying transforms leads to better image reconstructions than a single transform.
AB - Compressed Sensing has been demonstrated to be a powerful tool for magnetic resonance imaging (MRI), where it enables accurate recovery of images from highly undersampled k-space measurements by exploiting the sparsity of the images or image patches in a transform domain or dictionary. In this work, we focus on blind compressed sensing, where the underlying sparse signal model is a priori unknown, and propose a framework to simultaneously reconstruct the underlying image as well as the unknown model from highly undersampled measurements. Specifically, our model is that the patches of the underlying MR image(s) are approximately sparse in a transform domain. We also extend this model to a union of transforms model that is better suited to capture the diversity of features in MR images. The proposed block coordinate descent type algorithms for blind compressed sensing are highly efficient. Our numerical experiments demonstrate the superior performance of the proposed framework for MRI compared to several recent image reconstruction methods. Importantly, the learning of a union of sparsifying transforms leads to better image reconstructions than a single transform.
KW - Compressed sensing
KW - Dictionary learning
KW - Magnetic resonance imaging
KW - Medical imaging
KW - Sparse representations
KW - Sparsifying transforms
UR - http://www.scopus.com/inward/record.url?scp=84951335997&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84951335997&partnerID=8YFLogxK
U2 - 10.1117/12.2188952
DO - 10.1117/12.2188952
M3 - Conference contribution
AN - SCOPUS:84951335997
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Wavelets and Sparsity XVI
A2 - Goyal, Vivek K.
A2 - Van De Ville, Dimitri
A2 - Van De Ville, Dimitri
A2 - Papadakis, Manos
A2 - Van De Ville, Dimitri
A2 - Papadakis, Manos
A2 - Goyal, Vivek K.
A2 - Van De Ville, Dimitri
PB - SPIE
T2 - Wavelets and Sparsity XVI
Y2 - 10 August 2015 through 12 August 2015
ER -