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

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. PMID: 33238249 [PubMed - as supplied by publisher]
Source: Physics in Medicine and Biology - Category: Physics Authors: Tags: Phys Med Biol Source Type: research