Quantitative MR-PET imaging
MR based PET attenuation correction
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.
Figure: PET images in absolute units of kBq/ml reconstructed using: (column A) CT map: μ-mapct, (B) DIXON map: μ-mapdixon, (C) DIXON plus MR bone map: μ-mapdixon+bone, and (D) deep learning based map: μ-mapdl. The numerical values shown are the average PET radioactivity measured within the prostate ROI.
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