An Efficient Approach to Perform MR-assisted PET Data Optimization in Simultaneous PET/MR Neuroimaging Studies

Area of Science:

• Medical Imaging
• Nuclear Medicine
• Neuroscience

Background:

• Positron Emission Tomography (PET) offers quantitative radiotracer concentration but faces accuracy challenges from attenuation, motion, and resolution limits.
• Simultaneously acquired Magnetic Resonance (MR) sequences provide complementary anatomical information.

Purpose of the Study:

• To introduce MR-assisted PET data optimization (MaPET), an efficient method for PET attenuation correction (AC), motion correction, and anatomy-aided reconstruction.
• To enhance the diagnostic accuracy of PET imaging for neurological disorders.

Main Methods:

• Generated voxel-wise linear attenuation coefficient maps from MR volumes using an SPM8-based approach for AC.
• Utilized embedded MR navigators for event-based PET motion correction.
• Incorporated MR anatomy into PET image reconstruction via a kernel-based method.

Main Results:

• MaPET-optimized PET images demonstrated superior quality (higher SNR, lower coefficient of variation) compared to standard AC-only reconstructed images.
• Optimized images showed significantly greater effect sizes in distinguishing hypometabolic from normometabolic cortical regions.
• MaPET improved the ability to detect pathological changes in individual patients across different diagnostic groups.

Conclusions:

• Spatiotemporally correlated data from a single MR sequence can effectively correct for PET attenuation, motion, and partial volume effects.
• The MaPET method holds promise for more accurate assessment of pathological changes in dementia and other brain disorders.