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PIDA-Net: A prior image-guided deformable attention network for high-quality 4D-CBCT reconstruction
Daochi Qu1, Zifeng Li1, Zhanghua Luo1
1School of Biomedical Engineering, Southern Medical University, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, China.
Summary
This study introduces a new AI method, PIDA-Net, to improve the quality of four-dimensional cone beam computed tomography (4D-CBCT) images used in radiation therapy. The network reduces artifacts and enhances anatomical accuracy for better tumor targeting.
Area of Science:
- Medical Imaging
- Radiotherapy Technology
- Artificial Intelligence in Medicine
Background:
- High-quality four-dimensional cone beam computed tomography (4D-CBCT) is crucial for accurate tumor localization and motion management in thoracic and abdominal radiotherapy.
- Conventional 4D-CBCT reconstructions are often degraded by streak artifacts and noise due to undersampled projection data.
Purpose of the Study:
- To develop a novel deep learning framework, the prior image-guided deformable attention network (PIDA-Net), for generating high-fidelity 4D-CBCT images.
- To enhance anatomical accuracy and reduce artifacts in 4D-CBCT reconstructions.
Main Methods:
- A prior reference image (averaged or planning CT) was constructed.
- A dual-branch convolutional network with a deformable attention module was employed to fuse features from 4D-CBCT data and the reference image.
- The network dynamically weighted relevant anatomical features for context-aware information fusion.
Main Results:
- The PIDA-Net successfully generated high-fidelity 4D-CBCT reconstructions.
- Significant suppression of streak artifacts was achieved.
- Preservation of fine anatomical details was demonstrated in both simulated and clinical datasets.
Conclusions:
- The PIDA-Net offers a promising solution for improving 4D-CBCT image quality in radiotherapy.
- This method enhances image fidelity, potentially leading to more accurate tumor localization and improved patient outcomes.
