TY - GEN
T1 - Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation
AU - Wang, Yan
AU - Zhang, Pei
AU - An, Le
AU - Ma, Guangkai
AU - Kang, Jiayin
AU - Wu, Xi
AU - Zhou, Jiliu
AU - Lalush, David S.
AU - Lin, Weili
AU - Shen, Dinggang
PY - 2015
Y1 - 2015
N2 - Positron emission tomography (PET) has been widely used in clinical diagnosis of diseases or disorders. To reduce the risk of radiation exposure, we propose a mapping-based sparse representation (m-SR) framework for prediction of standard-dose PET image from its low-dose counterpart and corresponding multimodal magnetic resonance (MR) images. Compared with the conventional patch-based SR, our method uses a mapping strategy to ensure that the sparse coefficients estimated from the low-dose PET and multimodal MR images could be directly applied to the prediction of standard-dose PET images. An incremental refinement framework is also proposed to further improve the performance. Finally, a patch selection based dictionary construction method is used to speed up the prediction process. The proposed method has been validated on a real human brain dataset, showing that our method can work much better than the state-of-the-art method both qualitatively and quantitatively.
AB - Positron emission tomography (PET) has been widely used in clinical diagnosis of diseases or disorders. To reduce the risk of radiation exposure, we propose a mapping-based sparse representation (m-SR) framework for prediction of standard-dose PET image from its low-dose counterpart and corresponding multimodal magnetic resonance (MR) images. Compared with the conventional patch-based SR, our method uses a mapping strategy to ensure that the sparse coefficients estimated from the low-dose PET and multimodal MR images could be directly applied to the prediction of standard-dose PET images. An incremental refinement framework is also proposed to further improve the performance. Finally, a patch selection based dictionary construction method is used to speed up the prediction process. The proposed method has been validated on a real human brain dataset, showing that our method can work much better than the state-of-the-art method both qualitatively and quantitatively.
KW - Incremental refinement
KW - Multimodal MR images
KW - Positron emission tomography (PET)
KW - Sparse representation
UR - http://www.scopus.com/inward/record.url?scp=84952067130&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84952067130&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-24888-2_16
DO - 10.1007/978-3-319-24888-2_16
M3 - Conference contribution
AN - SCOPUS:84952067130
SN - 9783319248875
VL - 9352
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 127
EP - 135
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
T2 - 6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015
Y2 - 5 October 2015 through 5 October 2015
ER -
