Convolutional neural network based attenuation correction for 123I-FP-CIT SPECT with focused striatum imaging

Abstract

Abstract SPECT imaging with 123I-FP-CIT is used for diagnosis of neurodegenerative disorders like Parkinson’s disease. Attenuation correction (AC) can be useful for quantitative analysis of 123I-FP-CIT SPECT. Ideally, AC would be performed based on attenuation maps ( μ -maps) derived from perfectly registered CT scans. Such μ -maps, however, are most times not available and possible errors in image registration can induce quantitative inaccuracies in AC corrected SPECT images. Earlier, we showed that a convolutional neural network (CNN) based approach allows to estimate SPECT-aligned μ -maps for full brain perfusion imaging using only emission data. Here we investigate the feasibility of similar CNN methods for axially focused 123I-FP-CIT scans. We tested our approach on a high-resolution multi-pinhole prototype clinical SPECT system in a Monte Carlo simulation study. Three CNNs that estimate μ -maps in a voxel-wise, patch-wise and image-wise manner were investigated. As the added value of AC on clinical 123I-FP-CIT scans is still debatable, the impact of AC was also reported to check in which cases CNN based AC could be beneficial. AC using the ground truth μ -maps (GT-AC) and CNN estimated μ -maps (CNN-AC) were compared with the case when no AC was done (No-AC). Results show that the effect of using GT-AC versus CNN-AC or No-AC on striatal shape and symmetry is minimal. Specific binding ratios (SBRs) from localized regions show a deviation from GT-AC ≤ 2.5% for all three CNN-ACs while No-AC systematically underestimates SBRs by 13.1%. A strong correlation ( r ≥ 0.99) was obtained between GT-AC based SBRs and SBRs from CNN-ACs and No-AC. Absolute quantification (in kBq ml−1) shows a deviation from GT-AC within 2.2% for all three CNN-ACs and of 71.7% for No-AC. To conclude, all three CNNs show comparable performance in accurate μ -map estimation and 123I-FP-CIT quantification. CNN-estimated μ -map can be a promising substitute for CT-based μ -map.