Accurate Patient Alignment without Unnecessary Imaging Dose via Synthesizing Patient-specific 3D CT Images from 2D kV Images
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908393765076992 |
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| author | Ding, Yuzhen Holmes, Jason M. Feng, Hongying Li, Baoxin McGee, Lisa A. Rwigema, Jean-Claude M. Vora, Sujay A. Ma, Daniel J. Foote, Robert L. Patel, Samir H. Liu, Wei |
| author_facet | Ding, Yuzhen Holmes, Jason M. Feng, Hongying Li, Baoxin McGee, Lisa A. Rwigema, Jean-Claude M. Vora, Sujay A. Ma, Daniel J. Foote, Robert L. Patel, Samir H. Liu, Wei |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_19338 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accurate Patient Alignment without Unnecessary Imaging Dose via Synthesizing Patient-specific 3D CT Images from 2D kV Images Ding, Yuzhen Holmes, Jason M. Feng, Hongying Li, Baoxin McGee, Lisa A. Rwigema, Jean-Claude M. Vora, Sujay A. Ma, Daniel J. Foote, Robert L. Patel, Samir H. Liu, Wei Signal Processing Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | Accurate Patient Alignment without Unnecessary Imaging Dose via Synthesizing Patient-specific 3D CT Images from 2D kV Images |
| topic | Signal Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.19338 |