APA (7th ed.) Citation

Lafontaine, D., Augensen, F., Kesner, A., Vincent, R., Kirov, A., Krebs, S., . . . Humm, J. L. (2025). Machine-learning based quantification of lung shunt fraction from 99mTc-MAA SPECT/CT for selective internal radiation therapy of liver tumors using TriDFusion (3DF). EJNMMI Physics, 12(1), 1. https://doi.org/10.1186/s40658-025-00732-9

Chicago Style (17th ed.) Citation

Lafontaine, Daniel, Finn Augensen, Adam Kesner, Raoul Vincent, Assen Kirov, Simone Krebs, Heiko Schöder, and John L. Humm. "Machine-learning Based Quantification of Lung Shunt Fraction from 99mTc-MAA SPECT/CT for Selective Internal Radiation Therapy of Liver Tumors Using TriDFusion (3DF)." EJNMMI Physics 12, no. 1 (2025): 1. https://doi.org/10.1186/s40658-025-00732-9.

MLA (9th ed.) Citation

Lafontaine, Daniel, et al. "Machine-learning Based Quantification of Lung Shunt Fraction from 99mTc-MAA SPECT/CT for Selective Internal Radiation Therapy of Liver Tumors Using TriDFusion (3DF)." EJNMMI Physics, vol. 12, no. 1, 2025, p. 1, https://doi.org/10.1186/s40658-025-00732-9.