Work

SPADE: Spectroscopic Photoacoustic Denoising using an Analytical and Data-free Enhancement Framework

Year: 2024

Type: preprint

Abstract: Spectroscopic photoacoustic (sPA) imaging uses multiple wavelengths to differentiate chromophores based on their unique optical absorption spectra. This technique has been widely applied in areas such as vascular mapping, tumor detection, and therapeutic monitoring. However, sPA imaging is highly susceptible to noise, leading to poor signal-to-noise ratio (SNR) and compromised image quality. Traditional denoising techniques like frame averaging, though effective in improving SNR, can be impractical for dynamic imaging scenarios due to reduced frame rates. Advanced methods, including learning-based approaches and analytical algorithms, have demonstrated promise but often require extensive training data and parameter tuning, limiting their adaptability for real-time clinical use. In this work, we propose a sPA denoising using a tuning-free analytical and data-free enhancement (SPADE) framework for denoising sPA images. This framework integrates a data-free learning-based method with an efficient BM3D-based analytical approach while preserves spectral linearity, providing noise reduction and ensuring that functional information is maintained. The SPADE framework was validated through simulation, phantom, ex vivo, and in vivo experiments. Results demonstrated that SPADE improved SNR and preserved spectral information, outperforming conventional methods, especially in challenging imaging conditions. SPADE presents a promising solution for enhancing sPA imaging quality in clinical applications where noise reduction and spectral preservation are critical.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2412.12068

Authors: Lin, Fangzhou, Gao, Shang, Tang, Yichuan, Ma, Xihan, Murakami, Ryo, Zhang, Ziming, Obayemi, John D., Soboyejo, Winston W., Zhang, Haichong K.

Institution: Worcester Polytechnic Institute

Language: English

Topic: Image and Signal Denoising Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Worcester Polytechnic Institute, National Institutes of Health, National Cancer Institute, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Biomedical Imaging and Bioengineering

Awards: MR-Guided Precision Conformal Ablation Therapy for Brain Tumors, Multiparametric Photoacoustic Imaging-Based Identification of Aggressive Prostate Cancer, Physician Assistance Technology in Image-guided Robotic Intervention of Prostate, Automatic Wide-Field Optical Coherence Tomography for Assessment of Transplant Kidney Viability, Enhanced imaging and treatment of aggressive subtypes of prostate cancer

OpenAlex ID: W4405561624

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