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Background <p>PET data-driven attenuation correction (<b>AC</b>) 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 [<sup>18</sup>F]FDG neuroimaging on whole-body PET/CT.</p> Methods <p>Our approach, TRansmission-aided μ-map reconstruction (<b>TRU</b>) AC, includes 1) a low-profile and physically fixed transmission source filled with ~ 14&#xa0;MBq of <sup>18</sup>F, 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 [<sup>18</sup>F]FDG PET/CT. The clinically-consistent protocol included a CT and 10-minute brain-focused PET exam. Using this 10&#xa0;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.</p> Results <p>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%.</p> Conclusions <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月17日

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  • Sima Lotfimarangloo,
  • Syed T. Rahman,
  • Mi-Ae Park,
  • Baowei Fei,
  • Ramsey D. Badawi,
  • Spencer L. Bowen

摘要

Background

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.

Methods

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.

Results

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%.

Conclusions

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.