Impact of MR-Based Attenuation Correction on Neurologic PET Studies

Abstract

Hybrid PET and MR scanners have become a reality in recent years, with the benefits of reduced radiation exposure, reduction of imaging time, and potential advantages in quantification. Appropriate attenuation correction remains a challenge. Biases in PET activity measurements were demonstrated using the current MR-based attenuation-correction technique. We aimed to investigate the impact of using a standard MR-based attenuation correction technique on the clinical and research utility of a PET/MR hybrid scanner for amyloid imaging.

Methods: Florbetapir scans were obtained for 40 participants on a hybrid scanner with simultaneous MR acquisition. PET images were reconstructed using both MR- and CT-derived attenuation maps. Quantitative analysis was performed for both datasets to assess the impact of MR-based attenuation correction to absolute PET activity measurements as well as target-to-reference ratio (SUVR). Clinical assessment was also performed by a nuclear medicine physician to determine amyloid status based on the criteria in the Food and Drug Administration prescribing information for florbetapir.

Results: MR-based attenuation correction led to underestimation of PET activity for most parts of the brain, with a small overestimation for deep brain regions. There was also an overestimation of SUVRs with cerebellar reference. SUVR measurements obtained from the 2 attenuation-correction methods were strongly correlated. Clinical assessment of amyloid status resulted in identical classification as positive or negative regardless of the attenuation-correction methods.

Conclusion: MR-based attenuation correction causes biases in quantitative measurements. The biases may be accounted for by a linear model, although the spatial variation cannot be easily modeled. The quantitative differences, however, did not affect clinical assessment as positive or negative.

Keywords: PET/MR; amyloid imaging; attenuation correction.

Figure 1

Example images of a participant: A) T1-weighted MR; B) Dixon μ-map; C) CT μ-map; D) Non-attenuation corrected PET; E) PET with Dixon MRAC; F) PET with CTAC.

Figure 2

Average PET image intensity percent difference of MRAC vs. CTAC of the study cohort in atlas space. Left column: average T1 weighted MR in atlas space; middle column: average percent difference map in atlas space superimposed with MR; right column: average percent difference map in atlas space. Negative values indicate under estimation using MRAC against CTAC.

Figure 3

Average SUVR percent difference of MRAC vs. CTAC of the study cohort in atlas space. Left column: average T1 weighted MR in atlas space; middle column: average percent difference map in atlas space superimposed with MR; right column: average percent difference map in atlas space. Negative values indicate under estimation using MRAC against CTAC.

Figure 4

Comparison of mean cortical SUVR measurements obtained using MRAC vs. CTAC. The regions included in mean cortical SUVR has been defined previously (24).