Model-based compressive diffusion tensor imaging

Lingling Pu, Theodore P. Trouard, Lee Ryan, Chuan Huang, Maria I. Altbach, Ali Bilgin

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

10 Scopus citations


Diffusion tensor imaging (DTI) is a Magnetic Resonance Imaging (MRI) technique that can reveal in vivo tissue microstructure by measuring diffusion of water in tissue. DTI has become an important tool in many clinical applications, such as assessment of white matter maturation, locating white matter lesions, and providing anatomical connectivity information. However, DTI usually requires long examination times due to the repetitive nature of the acquisition and is very sensitive to motion. These drawbacks have become the largest obstacles to full utilization of DTI. In this work, we propose to overcome these obstacles by using a model-based compressive imaging approach. Our approach consist of models to efficiently represent diffusion-encoded images and the corresponding recovery schemes based on compressive sensing (CS) principles. Our results indicate that the proposed model-based approach can allow reliable recovery of DTI signal from undersampled measurements and outperforms conventional CS recovery.

Original languageEnglish (US)
Title of host publication2011 8th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI'11
Number of pages4
StatePublished - 2011
Event2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 - Chicago, IL, United States
Duration: Mar 30 2011Apr 2 2011

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Other2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
Country/TerritoryUnited States
CityChicago, IL


  • Compressive sensing
  • DTI

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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