MR-assisted PET data optimization for simultaneous dual-modality imaging in dementia

Recent advances have allowed the hardware integration of positron emission tomography (PET) and magnetic resonance imaging (MRI). The spatiotemporally correlated data acquisition opened up opportunities for numerous applications. Furthermore, the MRI data can be utilized to improve the PET scanner performance. While PET has many advantages, including the fact that it could provide a quantitative means to assess in vivo biological processes, its accuracy is confounded by several factors. For example, attenuation correction is required to account for the interactions of the annihilation photons in the subject; motion correction is needed to minimize image degradation due to subject movements; partial volume effects correction is required due to the relatively limited spatial resolution. Although many applications could benefit from these methodological improvements, in this thesis we focused on dementia. MRI and PET are widely used and provide complementary information in the assessment of these patients. Equally important, dementia is a great test situation for these methodological developments because the confounding factors mentioned above are especially pronounced in this patient population. In this work, we developed a unified protocol to address these limitations, an approach we termed MR-assisted PET data optimization. Specifically, we first developed methods to derive head attenuation maps from the morphological MR images. Next, we used temporally-correlated MR data for PET motion compensation and spatially-correlated MR data for anatomy-aided reconstruction. Finally, we demonstrated that after applying these tools to data acquired in dementia patients the PET data quantification was positively impacted and the image quality improved substantially..

Department dc:contributor.department

Harvard--MIT Program in Health Sciences and Technology.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Chen, Kevin Tze-Hsiang

Advisor dc:contributor.advisor

• Ciprian Catana.

• Harvard--MIT Program in Health Sciences and Technology.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/111254

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/111254