In radiotherapy, 2D orthogonally projected kV images are used for patient
alignment when 3D-on-board imaging(OBI) unavailable. But tumor visibility is
constrained due to the projection of patient's anatomy onto a 2D plane,
potentially leading to substantial setup errors. In treatment room with 3D-OBI
such as cone beam CT(CBCT), the field of view(FOV) of CBCT is limited with
unnecessarily high imaging dose, thus unfavorable for pediatric patients. A
solution to this dilemma is to reconstruct 3D CT from kV images obtained at the
treatment position. Here, we propose a dual-models framework built with
hierarchical ViT blocks. Unlike a proof-of-concept approach, our framework
considers kV images as the solo input and can synthesize accurate, full-size 3D
CT in real time(within milliseconds). We demonstrate the feasibility of the
proposed approach on 10 patients with head and neck (H&N) cancer using image
quality(MAE: <45HU), dosimetrical accuracy(Gamma passing rate (2%/2mm/10%)>97%)
and patient position uncertainty(shift error: <0.4mm). The proposed framework
can generate accurate 3D CT faithfully mirroring real-time patient position,
thus significantly improving patient setup accuracy, keeping imaging dose
minimum, and maintaining treatment veracity.

提出了使用双模型框架，通过层次化 ViT 模块从 kV 图像中实时重建准确的全尺寸 3D CT，以显著提高患者摆位精度并保持最小的成像剂量，同时维护治疗的准确性。