Nuclear Techniques ›› 2017, Vol. 40 ›› Issue (4): 40302-040302.doi: 10.11889/j.0253-3219.2017.hjs.40.040302


Evaluation of two motion correction methods for simultaneous PET/MRI brain imaging

XIE Weiwei1,2, HU Lingzhi3, CAO Xuexiang3, CHU Xu1, CHEN Qun1,2,3   

  1. 1 Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China;
    2 University of Chinese Academy of Sciences, Beijing 100049, China;
    3 Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201807, China
  • Received:2017-01-13 Revised:2017-02-28 Online:2017-04-10 Published:2017-04-07
  • Supported by:

    Supported by Key Projects of Chinese Academy of Sciences (No.Y325511211), National Key Research and Development Program Digital Diagnostic Equipment R&D Pilot (No.2016YFC0103900)


Background: Simultaneous position emission tomography (PET) /magnetic resonance imaging (MRI) plays an important role in diagnosis of many brain diseases. However, brain motion caused by epilepsy, Parkinson's disease or muscle contraction and relaxation in head and neck is inevitable during the scanning. Motion artifact is one of the key factors that affect the quality of PET brain imaging. With PET/MRI, it becomes possible to use motion information obtained with MRI to correct for the PET image artifacts due to the high resolution of MRI. Purpose: This study aims to verify line of response (LOR) based motion correction method is more accurate than frame-based motion correction method in PET/MRI brain imaging, considering more precise information from MRI than previous methods. Methods: Rigid motions of NEMA (National Electrical Manufacturers Association) phantom and XCAT (the 4D extended cardiac-torso) phantom were simulated by using Monte Carlo method, i.e., the medical imaging simulation software GATE (GEANT4 application for tomographic emission). PET data was corrected using the two methods within open source reconstruction software STIR (software for tomographic image reconstruction). The reconstructed images of NEMA imaging quality (IQ) phantom were evaluated by contrast recovery coefficient (CRC) curves and the images of XCAT phantom were evaluated using full width at half maximum (FWHM) measurement of the lesion. The rigid phantom motion information was corrected by registering MRI images using gradient echo quick 3D sequence during PET/MRI scanning simultaneously, because MRI features high speed in imaging and high spatial resolution. Then, PET data was reconstructed using MRI derived motion vector to verify and evaluate the accuracy of these two motion correction methods. Results: FWHM values of reconstruction results compensated by both methods were significantly lower than the ones without motion correction. LOR based FWHM values were lower than those corrected by the frame-based methods both in XCAT simulation data and experiment data. Similarly, for NEMA IQ simulation data, the CRC curves had a higher upward tendency of both hot and cold spheres than the ones without motion correction, and the CRC curves of all spheres from LOR based method were higher than frame-based ones. Conclusion: By quantitative and qualitative analysis of both simulation and experiment corrected data, we concluded that both methods can compensate motion artifacts, and the LOR based method outperforms frame-based method for PET data compensation in simultaneous PET/MRI scanning.

Key words: PET/MRI, Brain motion correction, Gradient echo quick 3D sequence

CLC Number: 

  • TL99