Image Quality Improvement Techniques and Assessment Adequacy in Clinical Optoacoustic Imaging: A Systematic Review

Abstract

Optoacoustic imaging relies on the detection of optically induced acoustic waves to offer new possibilities in morphological and functional imaging. As the modality matures towards clinical application, research efforts aim to address multifactorial limitations that negatively impact the resulting image quality. In an endeavor to obtain a clear view on the limitations and their effects, as well as the status of this progressive refinement process, we conduct an extensive search for optoacoustic image quality improvement approaches that have been evaluated with humans in vivo, thus focusing on clinically relevant outcomes. We query six databases (PubMed, Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and Google Scholar) for articles published from 1 January 2010 to 31 October 2021, and identify 45 relevant research works through a systematic screening process. We review the identified approaches, describing their primary objectives, targeted limitations, and key technical implementation details. Moreover, considering comprehensive and objective quality assessment as an essential prerequisite for the adoption of such approaches in clinical practice, we subject 36 of the 45 papers to a further in-depth analysis of the reported quality evaluation procedures, and elicit a set of criteria with the intent to capture key evaluation aspects. Through a comparative criteria-wise rating process, we seek research efforts that exhibit excellence in quality assessment of their proposed methods, and discuss features that distinguish them from works with similar objectives. Additionally, informed by the rating results, we highlight areas with improvement potential, and extract recommendations for designing quality assessment pipelines capable of providing rich evidence.

Non-invasive multispectral optoacoustic tomography resolves intrahepatic lipids in patients with hepatic steatosis

“…Finally, some steps of the MSOT data analysis process (e.g., segmentation, interpretation) might be time-consuming or labor-intensive, especially in the case of large (e.g., hundreds or thousands of images) or noisy datasets. Therefore, several automatic segmentation or image improvement techniques have been developed, even if herein, due to the small number of participants and the high data quality, we opted for a manual segmentation approach [39] , [40] .…”

Development and implementation of optimized endogenous contrast sequences for delineation in adaptive radiotherapy on a 1.5T MR-linear-accelerator: a prospective R-IDEAL stage 0-2a quantitative/qualitative evaluation of in vivo site-specific quality-assurance using a 3D T2 fat-suppressed platform fo

“…It is up to the individual user or institution to decide on how these weights should be implemented. A similar multicategory grading system to the one in this study was described by Dimaridis et al., 52 in which several studies in the field of optoacoustic imaging were reviewed and graded based on author-defined criteria of the study quality. The criteria had multiple subgroups and were summed and normalized in a similar manner as this study.…”

Dermal features derived from optoacoustic tomograms via machine learning correlate microangiopathy phenotypes with diabetes stage

“…In particular, raster-scan optoacoustic mesoscopy (RSOM) has assessed diabetes-related changes in the dermal microvasculature 17 , whereas ultra-wideband RSOM has been applied to assess microvascular and inflammatory markers of skin disease through the entire dermal layer at resolutions in the 7–30 μm range (Fig. 1 ), providing information not accessible by any other non-invasive method today 18 – 21 . Hence, RSOM offers the opportunity to study the relation of skin features to diabetes complications.…”