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

Impact of Atlas-CT-Based Bone Anatomy Compensation on MR-Based Attenuation Correction for Brain PET Imaging in a Time-of-Flight PET/MRI System: A Direct Comparison to a Patient-CT-Based Approach

May 20, 2015, 9:50 AM
Room Maria Luisa

Room Maria Luisa

Talk 7 - Clinical MR-PET Session 10 - Clinical MR-PET


Jaewon Yang (UCSF)


An atlas-CT-based bone-anatomy compensation for MR-based attenuation correction (MRAC) in brain PET/MRI imaging is a current standard. However, the impact of an anatomical difference has not been clinically evaluated. Thus, we aim to evaluate the impact of the anatomical dissimilarity on MRAC. Whole-body FDG-PET/CT followed by PET/MRI were performed for twelve patients in an integrated TOF PET/MRI system. The MRAC utilized an atlas-CT (MRAC-atlas) as well as a patient-specific-CT (MRAC-patient) to produce AC maps (pseudoCT). Instead of using atlas-CT, the MRAC-patient approach derived pseudoCT from patient-specific-CT aligned to MR. For quantitative evaluation, CTAC was considered as gold standard for AC, and PET mean activity concentration values were measured and compared in eight 10 ml volumes-of-interest (VOI). PET activity concentration with MRAC, compared to CTAC, were systematically underestimated on average by 0.63±0.34 kBq/ml (4.0±2.2%) and 0.22±0.21 kBq/ml (1.4±1.5%) for the MRAC-atlas and the MRAC-patient, respectively: using the MRAC-atlas, the error was increased to 0.41±0.25 kBq/ml (2.6±1.8%) on average (p≈0). However, the error increase was patient-dependent (highest: 5.7% vs. lowest: 0.3%) and VOI dependent (highest 3.1% vs. lowest: 1.9%). For the first time, the atlas-CT-based MRAC was compared to the patient-specific-CT-based MRAC for brain PET imaging in an integrated TOF PET/MRI system. Overall, the MRAC-atlas achieves quantification accuracy similar to CTAC with a small but measurable difference of 5% in values, which is 2.6% higher than the error of the MRAC-patient.

Primary author


Dr Daniel Vigneron (UCSF) Dr Michel Tohme (GE Healthcare) Dr Sharmila Majumdar (UCSF) Dr Spencer Behr (UCSF) Dr Yiqiang Jian (GE Healthcare) Dr Youngho Seo (UCSF)

Presentation materials