For PET/MR imaging and MR-guided radiation therapy, pseudo-CT image synthesis from MR images is essential for the quantitative merits of PET imaging and treatment planning. We have developed deep-learning methodologies and specific MR sequences to improve pseudo-CT image quality, through collaborations with colleagues at MGH and UCSF.
Related Publications:
- Gong K, Yang J, Kim K, El Fakhri G, Seo Y, Li Q. Attenuation Correction for Brain PET imaging using Deep Neural Network based on Dixon and ZTE MR images. Physics in Medicine & Biology. 2018 Jun 13; 63(12), p.125011.
- Han P, Horng D, Gong K, Petibon Y, Kim K, Li Q, Johnson K, El Fakhri G, Ouyang J, Ma C. MR-Based PET Attenuation Correction using Ultrashort Echo Time/Multi-Echo Dixon Acquisitions. Medical Physics. 2020 Jul;47(7):3064-77.
- Gong K, Yang J, Larson P, Behr S, Hope T, Seo Y, and Li Q. MR-based Attenuation Correction for Brain PET Using 3D Cycle-Consistent Adversarial Network. IEEE Transactions on Radiation and Plasma Medical Sciences. 2020 Jul 3;5(2):185-92.
- Gong K*, Han P*, Johnson K, El Fakhri G, Ma C, Li Q. Attenuation Correction Using Deep Learning and Integrated UTE/Multi-Echo Dixon Sequence: Evaluation in Amyloid and Tau PET Imaging. European journal of nuclear medicine and molecular imaging. 2021 May;48(5):1351-61.
Gong Lab @ UF BME 