A multi-level canonical correlation analysis scheme for standard-dose PET image estimation

Le An, Pei Zhang, Ehsan Adeli-Mosabbeb, Yan Wang, Guangkai Ma, Feng Shi, David S. Lalush, Weili Lin, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages1-9
Number of pages9
Volume9467
ISBN (Print)9783319281933
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event1st International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2015 - Munich, Germany
Duration: 2015 Oct 92015 Oct 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9467
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2015
CountryGermany
CityMunich
Period15/10/915/10/9

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Fingerprint Dive into the research topics of 'A multi-level canonical correlation analysis scheme for standard-dose PET image estimation'. Together they form a unique fingerprint.

  • Cite this

    An, L., Zhang, P., Adeli-Mosabbeb, E., Wang, Y., Ma, G., Shi, F., Lalush, D. S., Lin, W., & Shen, D. (2015). A multi-level canonical correlation analysis scheme for standard-dose PET image estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9467, pp. 1-9). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9467). Springer Verlag. https://doi.org/10.1007/978-3-319-28194-0_1