ArticlePhotoacoustics2022
System-level optimization in spectroscopic photoacoustic imaging of prostate cancer.
Article in Photoacoustics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 21 citations in OpenAlex.
- No cancer left behind: a testbed and demonstration of concept for photoacoustic tumor bed inspection.Computer assisted surgery (Abingdon, England) · 2026Article
- Sparse scanning encoding and neural network decoding for compressed photoacoustic microscopy.Photoacoustics · 2025Article
- Spectroscopic photoacoustic denoising framework using hybrid analytical and data-free learning method.Photoacoustics · 2025Article
- Automatic photoacoustic monitoring of perinatal brain hypoxia with superior sagittal sinus detection.Journal of biomedical optics · 2025Article
- Score-based generative model-assisted information compensation for high-quality limited-view reconstruction in photoacoustic tomography.Photoacoustics · 2024Article
- Sparse-view reconstruction for photoacoustic tomography combining diffusion model with model-based iteration.Photoacoustics · 2023Article
- High-contrast spectroscopic photoacoustic characterization of thermal tissue ablation in the visible spectrum.Ultrasonography (Seoul, Korea) · 2023Article
- Synthetic radial aperture focusing to regulate manual volumetric scanning for economic transrectal ultrasound imaging.Ultrasonics · 2023Article
- Fast iterative reconstruction for photoacoustic tomography using learned physical model: Theoretical validation.Photoacoustics · 2023Article
- Nanotechnology for brain tumor imaging and therapy based on π-conjugated materials: state-of-the-art advances and prospects.Frontiers in chemistry · 2023Review
- Absolute Quantitative Photoacoustic Imaging for Contrast Agents Concentration Estimation Using a Spectral Decomposition Approach.Molecular imagingArticle
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents a system-level optimization of spectroscopic photoacoustic (PA) imaging for prostate cancer (PCa) detection in three folds. First, we present a spectral unmixing model to segregate spectral system error (SSE). We constructed two noise models (NMs) for the laser spectrotemporal fluctuation and the ultrasound system noise. We used these NMs in linear spectral unmixing to denoise and to achieve high temporal resolution. Second, we employed a simulation-aided wavelength optimization to select the most effective subset of wavelengths. NMs again were considered so that selected wavelengths were not only robust to the collinearity of optical absorbance, but also to noise. Third, we quantified the effect of frame averaging on improving spectral unmixing accuracy through theoretical analysis and numerical validation. To validate the whole framework, we performed comprehensive studies in simulation and an in vivo experiment which evaluated prostate-specific membrane antigen (PSMA) expression in PCa on a mice model. Both simulation analysis and in vivo studies confirmed that the proposed framework significantly enhances image signal-to-noise ratio (SNR) and spectral unmixing accuracy. It enabled more sensitive and faster PCa detection. Moreover, the proposed framework can be generalized to other spectroscopic PA imaging studies for noise reduction, wavelength optimization, and higher temporal resolution.
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