Patient-specific 3D CT Images Reconstruction from 2D KV images via Vision Transformer-based Deep-Learning

Yuzhen Ding; Jason Holmes; Baoxin Li; Carlos E. Vargas; Sujay A. Vora; William W. Wong; Mirek Fatyga; Robert L. Foote; Samir H. Patel; Wei Liu

doi:10.1364/DH.2023.HTu2D.2

Optica Imaging Congress (3D, COSI, DH, FLatOptics, IS, pcAOP)
Technical Digest Series (Optica Publishing Group, 2023),
paper HTu2D.2
•https://doi.org/10.1364/DH.2023.HTu2D.2

Patient-specific 3D CT Images Reconstruction from 2D KV images via Vision Transformer-based Deep-Learning

Yuzhen Ding, Jason Holmes, Baoxin Li, Carlos E. Vargas, Sujay A. Vora, William W. Wong, Mirek Fatyga, Robert L. Foote, Samir H. Patel, and Wei Liu

Digital Holography and Three-Dimensional Imaging 2023

Boston, Massachusetts United States
14–17 August 2023
ISBN: 978-1-957171-28-9

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RadIT: Imaging for Medicine (HTu2D)

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Y. Ding, J. Holmes, B. Li, C. E. Vargas, S. A. Vora, W. W. Wong, M. Fatyga, R. L. Foote, S. H. Patel, and W. Liu, "Patient-specific 3D CT Images Reconstruction from 2D KV images via Vision Transformer-based Deep-Learning," in Optica Imaging Congress (3D, COSI, DH, FLatOptics, IS, pcAOP), Technical Digest Series (Optica Publishing Group, 2023), paper HTu2D.2.

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Abstract

The potential large setup uncertainties caused by limited-visibility kV images in proton machines urges a 3D-CT-reconstruction approach. A network with transformer blocks was developed and achieved a <40HU MAE. The network was accurate and efficient.

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