2026-08-04 マサチューセッツ大学アマースト校
＜関連情報＞
- https://www.umass.edu/news/article/ai-tool-computing-radiation-dose-created-umass-amherst-leads-way-personalizing
- https://ieeexplore.ieee.org/document/11573134
DiffuDose：前立腺癌に対する177Lu -PSMA放射性医薬品療法のための個別化線量測定のための拡散誘導モデル DiffuDose: A Diffusion-Guided Model for Personalized Dosimetry for 177Lu-PSMA Radiopharmaceutical Therapy for Prostate Cancer
Bowen Lei; Tzu-An Song; Ziyuan Zhou; Fan Yang; Vibha Balaji; Ziwei Liu,…
IEEE Transactions on Radiation and Plasma Medical Sciences Published:22 June 2026
DOI:https://doi.org/10.1109/TRPMS.2026.3705613
Abstract
Accurate voxel-wise personalized dosimetry is essential for maximizing therapeutic efficacy while minimizing off-target toxicity in 177Lu-PSMA radiopharmaceutical therapy. While Monte Carlo (MC) simulations can be used for dose calculations with gold-standard accuracy, their high computational cost limits clinical applicability. Conversely, analytical methods based on the Medical Internal Radiation Dose (MIRD) formalism can generate rapid dose estimates but lack spatial precision. We present DiffuDose, a novel AI-driven dosimetry framework that combines diffusion probabilistic models with multiscale information fusion and a voxel-wise fidelity constraint to generate accurate dose rate maps from post-therapy SPECT and CT images. DiffuDose is trained in two phases: a pretraining phase and an end-to-end joint training phase. Ablation studies confirm that multiscale information fusion and joint training are essential components of the framework. Evaluated on a clinical 177Lu-PSMA cohort, DiffuDose significantly outperformed existing deep learning models and MIRD calculations across multiple quantitative metrics, achieving accuracy levels comparable to MC dosimetry at a fraction of the computational cost