TY - GEN
T1 - Sparse decomposition learning based dynamic MRI reconstruction
AU - Zhu, Peifei
AU - Zhang, Qieshi
AU - Kamata, Sei Ichiro
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Dynamic MRI is widely used for many clinical exams but slow data acquisition becomes a serious problem. The application of Compressed Sensing (CS) demonstrated great potential to increase imaging speed. However, the performance of CS is largely depending on the sparsity of image sequence in the transform domain, where there are still a lot to be improved. In this work, the sparsity is exploited by proposed Sparse Decomposition Learning (SDL) algorithm, which is a combination of low-rank plus sparsity and Blind Compressed Sensing (BCS). With this decomposition, only sparsity component is modeled as a sparse linear combination of temporal basis functions. This enables coefficients to be sparser and remain more details of dynamic components comparing learning the whole images. A reconstruction is performed on the undersampled data where joint multicoil data consistency is enforced by combing Parallel Imaging (PI). The experimental results show the proposed methods decrease about 15∼20% of Mean Square Error (MSE) compared to other existing methods.
AB - Dynamic MRI is widely used for many clinical exams but slow data acquisition becomes a serious problem. The application of Compressed Sensing (CS) demonstrated great potential to increase imaging speed. However, the performance of CS is largely depending on the sparsity of image sequence in the transform domain, where there are still a lot to be improved. In this work, the sparsity is exploited by proposed Sparse Decomposition Learning (SDL) algorithm, which is a combination of low-rank plus sparsity and Blind Compressed Sensing (BCS). With this decomposition, only sparsity component is modeled as a sparse linear combination of temporal basis functions. This enables coefficients to be sparser and remain more details of dynamic components comparing learning the whole images. A reconstruction is performed on the undersampled data where joint multicoil data consistency is enforced by combing Parallel Imaging (PI). The experimental results show the proposed methods decrease about 15∼20% of Mean Square Error (MSE) compared to other existing methods.
KW - Compressed Sensing (CS)
KW - Dynamic MRI
KW - Parallel imaging (PI)
KW - Sparse Decomposition Learning (SDL)
UR - http://www.scopus.com/inward/record.url?scp=84924347653&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84924347653&partnerID=8YFLogxK
U2 - 10.1117/12.2180534
DO - 10.1117/12.2180534
M3 - Conference contribution
AN - SCOPUS:84924347653
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Seventh International Conference on Machine Vision, ICMV 2014
A2 - Vuksanovic, Branislav
A2 - Zhou, Jianhong
A2 - Verikas, Antanas
A2 - Radeva, Petia
PB - SPIE
T2 - 7th International Conference on Machine Vision, ICMV 2014
Y2 - 19 November 2014 through 21 November 2014
ER -