Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation

Yan Wang; Pei Zhang; Le An; Guangkai Ma; Jiayin Kang; Xi Wu; Jiliu Zhou; David S. Lalush; Weili Lin; Dinggang Shen

doi:10.1007/978-3-319-24888-2_16

Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation

Yan Wang, Pei Zhang, Le An, Guangkai Ma, Jiayin Kang, Xi Wu, Jiliu Zhou, David S. Lalush, Weili Lin, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer Verlag
Pages	127-135
Number of pages	9
Volume	9352
ISBN (Print)	9783319248875
DOIs	https://doi.org/10.1007/978-3-319-24888-2_16
Publication status	Published - 2015
Externally published	Yes
Event	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 - Munich, Germany Duration: 2015 Oct 5 → 2015 Oct 5

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9352
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Other

Other	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
Country	Germany
City	Munich
Period	15/10/5 → 15/10/5

Keywords

Incremental refinement
Multimodal MR images
Positron emission tomography (PET)
Sparse representation

ASJC Scopus subject areas

Computer Science(all)
Theoretical Computer Science

UN SDGs

This output contributes to the following Sustainable Development Goal(s)

Access to Document

10.1007/978-3-319-24888-2_16

Fingerprint Dive into the research topics of 'Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation'. Together they form a unique fingerprint.

Cite this

Wang, Y., Zhang, P., An, L., Ma, G., Kang, J., Wu, X., Zhou, J., Lalush, D. S., Lin, W., & Shen, D. (2015). Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9352, pp. 127-135). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9352). Springer Verlag. https://doi.org/10.1007/978-3-319-24888-2_16

Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation. / Wang, Yan; Zhang, Pei; An, Le; Ma, Guangkai; Kang, Jiayin; Wu, Xi; Zhou, Jiliu; Lalush, David S.; Lin, Weili; Shen, Dinggang.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9352 Springer Verlag, 2015. p. 127-135 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9352).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wang, Y, Zhang, P, An, L, Ma, G, Kang, J, Wu, X, Zhou, J, Lalush, DS, Lin, W & Shen, D 2015, Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9352, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9352, Springer Verlag, pp. 127-135, 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, Munich, Germany, 15/10/5. https://doi.org/10.1007/978-3-319-24888-2_16

Wang Y, Zhang P, An L, Ma G, Kang J, Wu X et al. Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9352. Springer Verlag. 2015. p. 127-135. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-24888-2_16

Wang, Yan ; Zhang, Pei ; An, Le ; Ma, Guangkai ; Kang, Jiayin ; Wu, Xi ; Zhou, Jiliu ; Lalush, David S. ; Lin, Weili ; Shen, Dinggang. / Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9352 Springer Verlag, 2015. pp. 127-135 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{0d2e0d808d034c3fb7e4c1f6dae9879b,

title = "Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation",

abstract = "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.",

keywords = "Incremental refinement, Multimodal MR images, Positron emission tomography (PET), Sparse representation",

author = "Yan Wang and Pei Zhang and Le An and Guangkai Ma and Jiayin Kang and Xi Wu and Jiliu Zhou and Lalush, {David S.} and Weili Lin and Dinggang Shen",

year = "2015",

doi = "10.1007/978-3-319-24888-2_16",

language = "English",

isbn = "9783319248875",

volume = "9352",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "127--135",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

note = "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 ; Conference date: 05-10-2015 Through 05-10-2015",

}

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 -

Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Cite this