Human subject study of a transmission-aided attenuation correction for precision PET neuroimaging

PET data-driven attenuation correction (AC) methods, including deep learning, are attractive options for quantitative brain imaging on CT-less brain PET systems and low-dose PET/CT. However, current schemes have performance and practical limitations. We previously developed a CT-less transmission-aided AC that combines coincidences from a weak positron source, and the patient, to estimate attenuation with physics alone. In this work, we aim to optimize and assess this new AC method during human [¹⁸F]FDG neuroimaging on whole-body PET/CT.

Our approach, TRansmission-aided μ-map reconstruction (TRU) AC, includes 1) a low-profile and physically fixed transmission source filled with ~ 14 MBq of ¹⁸F, 2) a modified maximum likelihood reconstruction of attenuation and activity algorithm, and 3) scatter corrections using the exam data alone. We imaged N = 5 patients with the transmission source, immediately after their clinical [¹⁸F]FDG PET/CT. The clinically-consistent protocol included a CT and 10-minute brain-focused PET exam. Using this 10 minutes of patient PET data alone, radiotracer images were reconstructed with the vendor's algorithm and TRU-AC or CT-AC (reference standard), with all else matched. For quantitative analysis, we placed brain-structure volumes of interest with an atlas, and computed error in mean standardized uptake values of TRU-AC relative to CT-AC.

TRU-AC PET showed qualitatively strong agreement with CT-AC. For the VOI analysis, absolute relative error in standardized uptake values for TRU-AC was within 3.6%, across all brain structures and patients. Normalized root mean square error of activity bias for TRU-AC was 1.8%, and voxel-wise noise in the cerebellum showed a very minor increase of 0.2%. Bland-Altman analysis demonstrated that TRU-AC and CT-AC have statistically significant agreement, assuming a maximum allowed difference of ± 5%.

TRU-AC enables quantitative PET for human neuroimaging. This approach may particularly benefit exams where deep learning-based AC schemes show reduced performance, including those focused on radiotracer development, new patient cohorts, and/or pathologies that often lack sufficient training data.