Abstract
In order to obtain positron emission tomography (PET) image with diagnostic quality, we seek to estimate a standard-dose PET (S-PET) image from its low-dose counterpart (L-PET), instead of obtaining the S-PET image directly by injecting standard-dose radioactive tracer to the patient. Therefore, the risk of radiation exposure can be significantly reduced. To achieve this goal, one possible way is to first map both S-PET and L-PET data into a common space and then perform a patch-based estimation of S-PET from L-PET patches. However, the approach of using all training data to globally learn the common space may not lead to an optimal estimation of a particular target S-PET patch. In this paper, we introduce a data-driven multi-level Canonical Correlation Analysis (m-CCA) scheme to tackle this problem. Specifically, a subset of training data that are most useful in estimating a target S-PET patch are identified in each level, and using these selected training data in the subsequent level leads to more accurate common space mapping and improved estimation. In addition, we also leverage multi-modal magnetic resonance (MR) images to provide complementary information to the estimation from L-PET. Validation on a real human brain dataset demonstrates the advantage of our method as compared to other techniques.
| 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 | 1-9 |
| Number of pages | 9 |
| Volume | 9467 |
| ISBN (Print) | 9783319281933 |
| DOIs | |
| Publication status | Published - 2015 |
| Externally published | Yes |
| Event | 1st International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2015 - Munich, Germany Duration: 2015 Oct 9 → 2015 Oct 9 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 9467 |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 1st International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2015 |
|---|---|
| Country | Germany |
| City | Munich |
| Period | 15/10/9 → 15/10/9 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
A multi-level canonical correlation analysis scheme for standard-dose PET image estimation. / An, Le; Zhang, Pei; Adeli-Mosabbeb, Ehsan; Wang, Yan; Ma, Guangkai; Shi, Feng; 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. 9467 Springer Verlag, 2015. p. 1-9 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9467).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A multi-level canonical correlation analysis scheme for standard-dose PET image estimation
AU - An, Le
AU - Zhang, Pei
AU - Adeli-Mosabbeb, Ehsan
AU - Wang, Yan
AU - Ma, Guangkai
AU - Shi, Feng
AU - Lalush, David S.
AU - Lin, Weili
AU - Shen, Dinggang
PY - 2015
Y1 - 2015
N2 - In order to obtain positron emission tomography (PET) image with diagnostic quality, we seek to estimate a standard-dose PET (S-PET) image from its low-dose counterpart (L-PET), instead of obtaining the S-PET image directly by injecting standard-dose radioactive tracer to the patient. Therefore, the risk of radiation exposure can be significantly reduced. To achieve this goal, one possible way is to first map both S-PET and L-PET data into a common space and then perform a patch-based estimation of S-PET from L-PET patches. However, the approach of using all training data to globally learn the common space may not lead to an optimal estimation of a particular target S-PET patch. In this paper, we introduce a data-driven multi-level Canonical Correlation Analysis (m-CCA) scheme to tackle this problem. Specifically, a subset of training data that are most useful in estimating a target S-PET patch are identified in each level, and using these selected training data in the subsequent level leads to more accurate common space mapping and improved estimation. In addition, we also leverage multi-modal magnetic resonance (MR) images to provide complementary information to the estimation from L-PET. Validation on a real human brain dataset demonstrates the advantage of our method as compared to other techniques.
AB - In order to obtain positron emission tomography (PET) image with diagnostic quality, we seek to estimate a standard-dose PET (S-PET) image from its low-dose counterpart (L-PET), instead of obtaining the S-PET image directly by injecting standard-dose radioactive tracer to the patient. Therefore, the risk of radiation exposure can be significantly reduced. To achieve this goal, one possible way is to first map both S-PET and L-PET data into a common space and then perform a patch-based estimation of S-PET from L-PET patches. However, the approach of using all training data to globally learn the common space may not lead to an optimal estimation of a particular target S-PET patch. In this paper, we introduce a data-driven multi-level Canonical Correlation Analysis (m-CCA) scheme to tackle this problem. Specifically, a subset of training data that are most useful in estimating a target S-PET patch are identified in each level, and using these selected training data in the subsequent level leads to more accurate common space mapping and improved estimation. In addition, we also leverage multi-modal magnetic resonance (MR) images to provide complementary information to the estimation from L-PET. Validation on a real human brain dataset demonstrates the advantage of our method as compared to other techniques.
UR - http://www.scopus.com/inward/record.url?scp=84955284848&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84955284848&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-28194-0_1
DO - 10.1007/978-3-319-28194-0_1
M3 - Conference contribution
SN - 9783319281933
VL - 9467
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 9
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -