Enhancing digital tomosynthesis (DTS) for lung radiotherapy guidance using patient-specific deep learning model.

Journal Article (Journal Article)

Digital tomosynthesis (DTS) has been proposed as a fast low-dose imaging technique for image-guided radiation therapy (IGRT). However, due to the limited scanning angle, DTS reconstructed by the conventional FDK method suffers from significant distortions and poor plane-to-plane resolutions without full volumetric information, which severely limits its capability for image guidance. Although existing deep learning-based methods showed feasibilities in restoring volumetric information in DTS, they ignored the inter-patient variabilities by training the model using group patients. Consequently, the restored images still suffered from blurred and inaccurate edges. In this study, we presented a DTS enhancement method based on a patient-specific deep learning model to recover the volumetric information in DTS images. The main idea is to use the patient-specific prior knowledge to train the model to learn the patient-specific correlation between DTS and the ground truth volumetric images. To validate the performance of the proposed method, we enrolled both simulated and real on-board projections from lung cancer patient data. Results demonstrated the benefits of the proposed method: (1) qualitatively, DTS enhanced by the proposed method shows CT-like high image quality with accurate and clear edges; (2) quantitatively, the enhanced DTS has low-intensity errors and high structural similarity with respect to the ground truth CT images; (3) in the tumor localization study, compared to the ground truth CT-CBCT registration, the enhanced DTS shows 3D localization errors of ≤0.7 mm and ≤1.6 mm for studies using simulated and real projections, respectively; and (4), the DTS enhancement is nearly real-time. Overall, the proposed method is effective and efficient in enhancing DTS to make it a valuable tool for IGRT applications.

Full Text

Duke Authors

Cited Authors

  • Jiang, Z; Yin, F-F; Ge, Y; Ren, L

Published Date

  • January 26, 2021

Published In

Volume / Issue

  • 66 / 3

Start / End Page

  • 035009 -

PubMed ID

  • 33238249

Pubmed Central ID

  • PMC7931663

Electronic International Standard Serial Number (EISSN)

  • 1361-6560

Digital Object Identifier (DOI)

  • 10.1088/1361-6560/abcde8


  • eng

Conference Location

  • England