Quantitative MR-PET imaging

MR based PET attenuation correction

Zhaolin Chen

Accurate Magnetic Resonance (MR) imaging based attenuation correction is crucial for quantitative Positron Emission Tomography (PET) in simultaneous MR-PET imaging. However, due to a lack of robust MR bone imaging methods, MR based attenuation correction remains a critical issue in MR-PET image reconstruction.

In this project, we aim to substantially improve the quantitative accuracy of PET imaging in the simultaneous MR-PET scanner using template matching and deep learning methods.

A B C
CT image of bones in pelvis MR image of bones in pelvis Deep learning corrected MR bone image
Figure: CT image of the bones in the pelvis (A), MR image of the bones in the pelvis (B), Deep learning corrected MR bone image (C).

MR-PET head motion artefacts reduction

Zhaolin Chen, Shenjun Zhong

Head motion is a major source of image artefacts in neuroimaging studies and can lead to degradation of the quantitative accuracy of reconstructed PET images. Simultaneous MR-PET makes it possible to estimate head motion information from high-resolution MR images and then correct motion artefacts in PET images.

In this project, we aim to develop a motion correction strategy to provide motion estimation for both dynamic and static PET imaging. The MR-PET motion estimation/correction pipeline has been implemented as a fully automated software package, written in Python, which is available as web-service: http://mbi-tools.erc.monash.edu/motion_correction