AI Article Synopsis
- - The study aims to enhance the quality of images produced by optical-resolution photoacoustic microscopy (OR-PAM), which is often limited by a trade-off between spatial resolution and imaging speed when diagnosing bone diseases.
- - Researchers developed a new model called Photoacoustic Dense Attention U-Net (PADA U-Net) that reconstructs full images from under-sampled data, addressing the spatial-temporal limitations of OR-PAM.
- - Validation tests showed that PADA U-Net significantly improved image quality metrics compared to traditional methods, indicating its potential to aid in the early diagnosis and treatment of bone diseases.
Article Abstract
Objective: Bone diseases deteriorate the microstructure of bone tissue. Optical-resolution photoacoustic microscopy (OR-PAM) enables high spatial resolution of imaging bone tissues. However, the spatiotemporal trade-off limits the application of OR-PAM. The purpose of this study was to improve the quality of OR-PAM images without sacrificing temporal resolution.
Methods: In this study, we proposed the Photoacoustic Dense Attention U-Net (PADA U-Net) model, which was used for reconstructing full-scanning images from under-sampled images. Thereby, this approach breaks the trade-off between imaging speed and spatial resolution.
Results: The proposed method was validated on resolution test targets and bovine cancellous bone samples to demonstrate the capability of PADA U-Net in recovering full-scanning images from under-sampled OR-PAM images. With a down-sampling ratio of [4, 1], compared to bilinear interpolation, the Peak Signal-to-Noise Ratio and Structural Similarity Index Measure values (averaged over the test set of bovine cancellous bone) of the PADA U-Net were improved by 2.325 dB and 0.117, respectively.
Conclusion: The results demonstrate that the PADA U-Net model reconstructed the OR-PAM images well with different levels of sparsity. Our proposed method can further facilitate early diagnosis and treatment of bone diseases using OR-PAM.
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| http://dx.doi.org/10.1016/j.ultrasmedbio.2024.05.027 | DOI Listing |
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Reconstructing Cancellous Bone From Down-Sampled Optical-Resolution Photoacoustic Microscopy Images With Deep Learning.
Ultrasound Med Biol
September 2024
Academy for Engineering and Technology, Fudan University, Shanghai, China; Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, China.
Article Synopsis
- - The study aims to enhance the quality of images produced by optical-resolution photoacoustic microscopy (OR-PAM), which is often limited by a trade-off between spatial resolution and imaging speed when diagnosing bone diseases.
- - Researchers developed a new model called Photoacoustic Dense Attention U-Net (PADA U-Net) that reconstructs full images from under-sampled data, addressing the spatial-temporal limitations of OR-PAM.
- - Validation tests showed that PADA U-Net significantly improved image quality metrics compared to traditional methods, indicating its potential to aid in the early diagnosis and treatment of bone diseases.
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