May 17 – 20, 2015
La Biodola, Isola d'Elba
Europe/Rome timezone

The Potential of TOF PET-MRI for Reducing Artifacts in PET Images

May 19, 2015, 4:00 PM
1h 30m
Room Elena

Room Elena

Poster 7 - Clinical MR-PET Session 8 - Poster Session I

Speaker

Andrei Iagaru (Stanford University)

Description

Aim: Here we evaluated the potential of TOF PET/MRI to reduce various PET image artifacts, by comparing the images to non-TOF PET/MRI, TOF PET/CT and non-TOF PET/CT. Methods and materials: All patients underwent a single-injection of FDG, followed first by PET-CT and subsequently by PET-MRI scan. The PET/CT exams were performed using a GE Discovery 690 PET/CT scanner The PET/MRI images were acquired on a GE Signa PET/MRI scanner. All PET images were reconstructed with and without the TOF data. Visual analysis of these series was performed for dental metal / breathing artifcats and 3) artifacts caused by high excretion of FDG in the bladder. PET image quality was evaluated using a 3-point scale (1 - clinically significant artifact; 2 – non clinically-significant artifact; and 3 - no artifact). Results: Data from 18 oncologic patients (mean ± SD age: 55 ± 10 years; female 7, male 11) were used. The average scores of TOF PET/MRI, non-TOF PET/MRI, TOF PET/CT and non-TOF PET/CT for dental artifacts were 3.0, 2.8, 2.4 and 2.3, respectively; for breathing artifacts were 3.0, 2.5, 2.5 and 2.3, respectively; and for pelvic artifacts were 2.9, 1.6, 2.1 and 1.4, respectively. TOF PET/MRI had the highest image quality scores among the 4 series of PET data analyzed for these types of artifacts. Conclusion: TOF PET/MRI showed promising results in reduction of various PET artifacts in this cohort, when compared to non-TOF PET/MRI, TOF PET/CT and non-TOF PET/CT. This may be due to the better timing resolution (<400 ps) for the PET/MR system compared to the PET/CT system (>600 ps).

Primary author

Andrei Iagaru (Stanford University)

Co-authors

Amir Barkhodari (Stanford University) Craig Levin (Stanford University) Dawn Holley (Stanford University) Mehran Jamali (Stanford University) Ryogo Minamimoto (Stanford University) Zaharchuk Greg (Stanford University)

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