Accurate patient alignment without unnecessary imaging using patient-specific 3D CT images synthesized from 2D kV images

Background In radiotherapy, 2D orthogonally projected kV images are used for patient alignment when 3D-on-board imaging (OBI) is unavailable. However, tumor visibility is constrained due to the projection of patient’s anatomy onto a 2D plane, potentially leading to substantial setup errors. In treat...

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Bibliographic Details
Published in:Communications medicine 2024-11, Vol.4 (1), p.241-10, Article 241
Main Authors: Ding, Yuzhen, Holmes, Jason M., Feng, Hongying, Li, Baoxin, McGee, Lisa A., Rwigema, Jean-Claude M., Vora, Sujay A., Wong, William W., Ma, Daniel J., Foote, Robert L., Patel, Samir H., Liu, Wei
Format: Article
Language:English
Subjects:
631/1647/245/1847
631/1647/245/2221
631/67/1536
692/4028/67/1536
Artificial intelligence
Deep learning
Dosimetry
Head & neck cancer
Low income groups
Medical imaging
Medicine
Medicine & Public Health
Patients
Planning
Radiation therapy
Three dimensional imaging
Tumors
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Summary:Background In radiotherapy, 2D orthogonally projected kV images are used for patient alignment when 3D-on-board imaging (OBI) is unavailable. However, 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. A solution to this dilemma is to reconstruct 3D CT from kV images obtained at the treatment position. Methods We propose a dual-models framework built with hierarchical ViT blocks. Unlike a proof-of-concept approach, our framework considers kV images acquired by 2D imaging devices in the treatment room as the solo input and can synthesize accurate, full-size 3D CT within milliseconds. Results We demonstrate the feasibility of the proposed approach on 10 patients with head and neck (H&N) cancer using image quality (MAE:  97%) and patient position uncertainty (shift error: 
ISSN:2730-664X
2730-664X
DOI:10.1038/s43856-024-00672-y