Smart scanning for two: [(18)F]FDG dose optimization for pregnant patients using LAFOV PET/CT technology

双人智能扫描:利用LAFOV PET/CT技术优化孕妇的[(18)F]FDG剂量

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Abstract

PURPOSE: This study aimed to evaluate the image quality-dose trade-off in pregnant patients imaged with long-axial field-of-view [(18)F]FDG PET/CT and to identify the most predictive body composition metric for image quality to develop a pregnancy-tailored dosage model. METHODS: Patients imaged with [(18)F]FDG PET/CT according to local pregnancy protocols were included in this study. Using raw PET data, images of various degrees of image quality were reconstructed. Acceptable image quality was identified using signal-to-noise ratio (SNR) in the liver and Likert scores. The minimum required scan statistics was modelled based on SNR and patient body composition. F-tests were used to find the best-fitting model parameter out of weight, weight-to-height-ratio, body-mass-index, and body surface area (BSA). Foetal dose was estimated with PET conversion factors and size-specific CT dose index values. RESULTS: Eleven patients were included in image quality analysis and dosage model optimization. SNR strongly correlated with Likert scores (R² = 0.80), with 10.72 SNR indicating acceptable image quality. BSA best predicted image quality (R² = 0.85), outperforming weight (R² = 0.78), weight-to-height ratio (R² = 0.63), and body mass index (R² = 0.38). The proposed dosage model reduces activity by 41-96% compared to current local pregnancy and adult protocols, resulting in estimated foetal radiation doses of 0.066 mGy (PET) and 0.31 mGy (CT). CONCLUSION: BSA accurately predicted [(18)F]FDG PET/CT image quality in pregnant patients. The proposed dosage regimen allows significant dose reduction and can be used as a foundation for the development of pregnancy dosage protocols.

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