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.

最近在质子治疗的进展中，基于磁共振的治疗计划正逐渐得到认可，以降低与传统基于 CT 的方法相比的额外辐射暴露。这种转变凸显了精确的磁共振与 CT 图像合成的关键性需求，对于精确的质子剂量计算至关重要。本研究引入了扩散薛定谔桥模型（DSBM），这是一种创新的高质量磁共振与 CT 图像合成方法。DSBM 学习了 MR 和 CT 数据分布之间的非线性扩散过程。该方法通过从先验分布而非高斯分布开始合成，提高了传统扩散模型的生成质量和效率。我们通过头颈癌数据集验证了 DSBM 的有效性，并通过图像级和剂量级评估证明了其优于传统图像合成方法。DSBM 在基于磁共振的质子治疗计划中的有效性突出了其在各种临床场景中作为有价值工具的潜力。