Dual-Domain Self-Supervised Artifact Removal Framework for Photoacoustic Computed Tomography
Quick Answer
The proposed dual-domain self-supervised artifact removal framework utilizes a lightweight Siamese Neural Network to effectively reduce reconstruction artifacts in Photoacoustic Computed Tomography (PACT).
Quick Take
By integrating cross-domain fidelity and uncertainty-weighted consistency in its composite loss function, the method demonstrates significant artifact suppression across various experimental setups, achieving enhanced computational efficiency.
Key Points
- Employs a lightweight Siamese Neural Network for artifact removal in PACT.
- Integrates cross-domain fidelity and uncertainty-weighted consistency in loss function.
- Demonstrates significant artifact suppression in simulations and human data.
- Achieves exceptional computational efficiency through inverse operator acceleration.
- Validations include in vivo rat and human experimental data.
Paper Resources
Source Excerpt
Photoacoustic Computed Tomography (PACT) often faces severe challenges from reconstruction artifacts due to sparse detection conditions. In this work, based on the distinct differences in artifact patterns between back-projection-based and Fourier-based reconstruction algorithms, we propose a self-supervised artifact removal framework that employs a lightweight Siamese Neural Network and a composite loss function integrating cross-domain fidelity and uncertainty-weighted consistency, effectively
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