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Postprocessing techniques in acoustic resolution optoacoustic microscopy applied to human and murine skin
Acoustic resolution-optoacoustic microscopy (AR_OAM) images human and murine skin at good lateral resolutions and high depths. Applying appropriate postprocessing techniques to AR-OAM holds promise for improved skin imaging.
Imaging skin is crucial for clinical diagnosis and treatment monitoring. Optoacoustic (OA) imaging has shown great potential in this field allowing early detection of cancer, classification of melanoma, and diagnosing different diseases [1-3].
The technical implementation as an acoustic resolution-optoacoustic microscope (AR-OAM) provides sufficient penetration depth while working at high spatial resolutions. AR-OAM thereby requires sophisticated postprocessing of acquired datasets to sharpen the resulting three-dimensional images and restore high lateral resolutions especially in deep layers. Different algorithms were perviously presented for this purpose [4-5].
[1] Hao F. Zhang, Konstantin Maslov, George Stoics, and Lihong V. Wang: Functional photoacoustic microscopy for high-resolution and noninvasive in vivo imaging. Nature Biotechnology 24 (7), 2006.
[2] Edward Y. Zhang et al.: Multimodal photoacoustic and optical coherence tomography scanner using an all optical detection scheme for 3D morphological skin imaging. Biomedical Optics Express 2 (8), 2011.
[3] Jung-Taek Oh et al.: Three dimensional imaging of skin melanoma in vivo by dual-wavelength photoacoustic microscopy. Journal of Biomedical Optics 11 (3), 2006.
[4] Jake Turner, Hector Estrada, Moritz Kneipp, and Daniel Razansky: Improved optoacoustic microscopy through three-dimensional spatial impulse response synthetic aperture focusing technique. Optics Letters 39 (12), 2014.
[5] De Cai, Zhongfei Li, Yao Li, Zhendong Guo, Sung-Liang Chen: Photoacoustic microscopy in vivo using synthetic aperture focusing technique combined with three-dimensional deconvolution. Optics Express 25 (2), 2017.
Imaging skin is crucial for clinical diagnosis and treatment monitoring. Optoacoustic (OA) imaging has shown great potential in this field allowing early detection of cancer, classification of melanoma, and diagnosing different diseases [1-3].
The technical implementation as an acoustic resolution-optoacoustic microscope (AR-OAM) provides sufficient penetration depth while working at high spatial resolutions. AR-OAM thereby requires sophisticated postprocessing of acquired datasets to sharpen the resulting three-dimensional images and restore high lateral resolutions especially in deep layers. Different algorithms were perviously presented for this purpose [4-5].
[1] Hao F. Zhang, Konstantin Maslov, George Stoics, and Lihong V. Wang: Functional photoacoustic microscopy for high-resolution and noninvasive in vivo imaging. Nature Biotechnology 24 (7), 2006.
[2] Edward Y. Zhang et al.: Multimodal photoacoustic and optical coherence tomography scanner using an all optical detection scheme for 3D morphological skin imaging. Biomedical Optics Express 2 (8), 2011.
[3] Jung-Taek Oh et al.: Three dimensional imaging of skin melanoma in vivo by dual-wavelength photoacoustic microscopy. Journal of Biomedical Optics 11 (3), 2006.
[4] Jake Turner, Hector Estrada, Moritz Kneipp, and Daniel Razansky: Improved optoacoustic microscopy through three-dimensional spatial impulse response synthetic aperture focusing technique. Optics Letters 39 (12), 2014.
[5] De Cai, Zhongfei Li, Yao Li, Zhendong Guo, Sung-Liang Chen: Photoacoustic microscopy in vivo using synthetic aperture focusing technique combined with three-dimensional deconvolution. Optics Express 25 (2), 2017.
The student is expected to implement, test, and quantitatively compare different postprocesisng approaches to improve reconstruction of three-dimensional skin datasets acquired with an existing microscopy system. Experience in programming (Python, C++, or MATLAB) and the ability to work independently are required. Previous insights in hardware interfacing or mechatronics would be of advantage.
The student is expected to implement, test, and quantitatively compare different postprocesisng approaches to improve reconstruction of three-dimensional skin datasets acquired with an existing microscopy system. Experience in programming (Python, C++, or MATLAB) and the ability to work independently are required. Previous insights in hardware interfacing or mechatronics would be of advantage.
Please send send brief introduction, CV, and transcript of records from your current studies to Urs Hofmann: hofmannu@student.ethz.ch
Please send send brief introduction, CV, and transcript of records from your current studies to Urs Hofmann: hofmannu@student.ethz.ch