OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing

• URL: http://arxiv.org/abs/2206.08612v2
• Date: Wed, 3 May 2023 15:40:04 GMT
• Title: OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing
• Authors: Firat Ozdemir, Berkan Lafci, Xos\'e Lu\'is De\'an-Ben, Daniel Razansky, Fernando Perez-Cruz
• Abstract summary: Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion. OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues. No standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
• Score: 62.993663757843464
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by subsequent detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion. OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues. This enabled the exploration of a number of attractive new applications both in clinical and laboratory settings. However, no standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings. This complicates an objective comparison between new and established data processing methods, often leading to qualitative results and arbitrary interpretations of the data. In this paper, we provide both experimental and synthetic OA raw signals and reconstructed image domain datasets rendered with different experimental parameters and tomographic acquisition geometries. We further provide trained neural networks to tackle three important challenges related to OA image processing, namely accurate reconstruction under limited view tomographic conditions, removal of spatial undersampling artifacts and anatomical segmentation for improved image reconstruction. Specifically, we define 44 experiments corresponding to the aforementioned challenges as benchmarks to be used as a reference for the development of more advanced processing methods.

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