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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Chris Wagner: The role of Eden Radioisotopes in the future of nuclear medicine
Chris Wagner has more than 40 years of experience in nuclear medicine, beginning as a clinical practitioner before moving into leadership roles at companies like Mallinckrodt (now Curium) and Nordion. His knowledge of both the clinical and the manufacturing sides of nuclear medicine laid the groundwork for helping to found Eden Radioisotopes, a start-up venture that intends to make diagnostic and therapeutic raw material medical isotopes like molybdenum-99 and lutetium-177.
John C. Wagner, Alireza Haghighat
Nuclear Science and Engineering | Volume 128 | Number 2 | February 1998 | Pages 186-208
Technical Paper | doi.org/10.13182/NSE98-2
Articles are hosted by Taylor and Francis Online.
Although the Monte Carlo method is considered to be the most accurate method available for solving radiation transport problems, its applicability is limited by its computational expense. Thus, biasing techniques, which require intuition, guesswork, and iterations involving manual adjustments, are employed to make reactor shielding calculations feasible. To overcome this difficulty, we have developed a method for using the SN adjoint function for automated variance reduction of Monte Carlo calculations through source biasing and consistent transport biasing with the weight window technique. We describe the implementation of this method into the standard production Monte Carlo code MCNP and its application to a realistic calculation, namely, the reactor cavity dosimetry calculation. The computational effectiveness of the method, as demonstrated through the increase in calculational efficiency, is demonstrated and quantified. Important issues associated with this method and its efficient use are addressed and analyzed. Additional benefits in terms of the reduction in time and effort required of the user are difficult to quantify but are possibly as important as the computational efficiency. In general, the automated variance reduction method presented is capable of increases in computational performance on the order of thousands, while at the same time significantly reducing the current requirements for user experience, time, and effort. Therefore, this method can substantially increase the applicability and reliability of Monte Carlo for large, real-world shielding applications.