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

Published: 24 Feb 2023, Last Modified: 28 Feb 2023Accepted by TMLREveryoneRevisionsBibTeX
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.
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: We marked all changes in blue. In the main manuscript, we added a new quantitative metric, 95-percentile Hausdorff distance (HD95), as shown in Table 4. In addition, we made several changes and additions in the manuscript (in blue) in response to reviewers' feedback. These changes are also mentioned in our OpenReview responses. There are no changes in the supplementary material.
Code: https://github.com/berkanlafci/oadat
Assigned Action Editor: ~Matthew_Blaschko1
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Submission Number: 665