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。

用于头颈部质子治疗计划的高质量 MR 到 CT 综合的扩散薛定谔桥模型

Diffusion Schrödinger Bridge Models for High-Quality MR-to-CT  Synthesis for Head and Neck Proton Treatment Planning

Purpose: In some proton therapy facilities, patient alignment relies on two
2D orthogonal kV images, taken at fixed, oblique angles, as no 3D on-the-bed
imaging is available. The visibility of the tumor in kV images is limited since
the patient's 3D anatomy is projected onto a 2D plane, especially when the
tumor is behind high-density structures such as bones. This can lead to large
patient setup errors. A solution is to reconstruct the 3D CT image from the kV
images obtained at the treatment isocenter in the treatment position.
Methods: An asymmetric autoencoder-like network built with vision-transformer
blocks was developed. The data was collected from 1 head and neck patient: 2
orthogonal kV images (1024x1024 voxels), 1 3D CT with padding (512x512x512)
acquired from the in-room CT-on-rails before kVs were taken and 2
digitally-reconstructed-radiograph (DRR) images (512x512) based on the CT. We
resampled kV images every 8 voxels and DRR and CT every 4 voxels, thus formed a
dataset consisting of 262,144 samples, in which the images have a dimension of
128 for each direction. In training, both kV and DRR images were utilized, and
the encoder was encouraged to learn the jointed feature map from both kV and
DRR images. In testing, only independent kV images were used. The full-size
synthetic CT (sCT) was achieved by concatenating the sCTs generated by the
model according to their spatial information. The image quality of the
synthetic CT (sCT) was evaluated using mean absolute error (MAE) and
per-voxel-absolute-CT-number-difference volume histogram (CDVH).
Results: The model achieved a speed of 2.1s and a MAE of <40HU. The CDVH
showed that <5% of the voxels had a per-voxel-absolute-CT-number-difference
larger than 185 HU.
Conclusion: A patient-specific vision-transformer-based network was developed
and shown to be accurate and efficient to reconstruct 3D CT images from kV
images.

该研究开发了一种基于视觉转换的非对称自编码器网络，可以从 2D 的 kV 图像中重建出 3D CT 图像，实现了患者定位精度的提高。