ArticleJournal of biomedical optics2020
Photoacoustic image improvement based on a combination of sparse coding and filtering.
Article in Journal of biomedical optics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Benchmarking deep learning-based reconstruction methods for photoacoustic computed tomography with clinically relevant synthetic datasets.Journal of biomedical optics · 2027Article
- RPCA-based thermoacoustic imaging for microwave ablation monitoring.Photoacoustics · 2024Article
- Radiation-induced acoustic signal denoising using a supervised deep learning framework for imaging and therapy monitoring.Physics in medicine and biology · 2023Article
- An extremum-guided interpolation for sparsely sampled photoacoustic imaging.Photoacoustics · 2023Article
- Fast iterative reconstruction for photoacoustic tomography using learned physical model: Theoretical validation.Photoacoustics · 2023Article
- Achieving depth-independent lateral resolution in AR-PAM using the synthetic-aperture focusing technique.Photoacoustics · 2022Review
- Photoacoustic-MR Image Registration Based on a Co-Sparse Analysis Model to Compensate for Brain Shift.Sensors (Basel, Switzerland) · 2022Article
- Review of consensus test methods in medical imaging and current practices in photoacoustic image quality assessment.Journal of biomedical optics · 2021Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
significancePhotoacoustic imaging (PAI) has been greatly developed in a broad range of diagnostic applications. The efficiency of light to sound conversion in PAI is limited by the ubiquitous noise arising from the tissue background, leading to a low signal-to-noise ratio (SNR), and thus a poor quality of images. Frame averaging has been widely used to reduce the noise; however, it compromises the temporal resolution of PAI.
aimWe propose an approach for photoacoustic (PA) signal denoising based on a combination of low-pass filtering and sparse coding (LPFSC). APPROACH: LPFSC method is based on the fact that PA signal can be modeled as the sum of low frequency and sparse components, which allows for the reduction of noise levels using a hybrid alternating direction method of multipliers in an optimization process.
resultsLPFSC method was evaluated using in-silico and experimental phantoms. The results show a 26% improvement in the peak SNR of PA signal compared to the averaging method for in-silico data. On average, LPFSC method offers a 63% improvement in the image contrast-to-noise ratio and a 33% improvement in the structural similarity index compared to the averaging method for objects located at three different depths, ranging from 10 to 20 mm, in a porcine tissue phantom.
conclusionsThe proposed method is an effective tool for PA signal denoising, whereas it ultimately improves the quality of reconstructed images, especially at higher depths, without limiting the image acquisition speed.
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