Diffusion Schrödinger Bridge Models for High-Quality MR-to-CT
  Synthesis for Head and Neck Proton Treatment Planning
Muheng Li Xia Li Sairos Safai Damien Weber Antony Lomax Ye Zhang
In recent advancements in proton therapy, MR-based treatment planning is
gaining momentum to minimize additional radiation exposure compared to
traditional CT-based methods. This transition highlights the critical need for
accurate MR-to-CT image synthesis, which is essential for precise proton dose
calculations. Our research introduces the Diffusion Schr\"odinger Bridge Models
(DSBM), an innovative approach for high-quality MR-to-CT synthesis. DSBM learns
the nonlinear diffusion processes between MR and CT data distributions. This
method improves upon traditional diffusion models by initiating synthesis from
the prior distribution rather than the Gaussian distribution, enhancing both
generation quality and efficiency. We validated the effectiveness of DSBM on a
head and neck cancer dataset, demonstrating its superiority over traditional
image synthesis methods through both image-level and dosimetric-level
evaluations. The effectiveness of DSBM in MR-based proton treatment planning
highlights its potential as a valuable tool in various clinical scenarios.