Detection of Photoacoustic Transients Originating from Microstructures in Optically Diffuse Media such as Biological Tissue

The generation and detection of broadband photoacoustic (PA) transients may be used for on-axis monitoring or for imaging of optically different structures in the interior of diffuse bodies such as biological tissue. Various piezoelectric sensors are characterized and compared in terms of sensitivity, depth response, and directivity with respect to spherical broadband acoustic pulses. The influence on the sensor output of acoustic interference and refraction of the PA transients at the sample-sensor interface is discussed. Ring detectors are suitable for deep on-axis detection thanks to their strong directional sensitivity, and small disk sensors are most suited for 3-D imaging of microstructures such as the (micro)vascular system. Voltage and charge preamplification schemes are compared in terms of the signal-to-noise ratio (SNR). In all cases, the preamplifier noise turns out to be the limiting factor for the sensitivity. Based on experimental data, for several sensor types and optical wavelengths, the theoretical detectability of PA signals generated by blood-like absorbers in biological tissue is discusse

AI Technical Considerations:Data Storage, Cloud usage and AI Pipeline

Artificial intelligence (AI), especially deep learning, requires vast amounts of data for training, testing, and validation. Collecting these data and the corresponding annotations requires the implementation of imaging biobanks that provide access to these data in a standardized way. This requires careful design and implementation based on the current standards and guidelines and complying with the current legal restrictions. However, the realization of proper imaging data collections is not sufficient to train, validate and deploy AI as resource demands are high and require a careful hybrid implementation of AI pipelines both on-premise and in the cloud. This chapter aims to help the reader when technical considerations have to be made about the AI environment by providing a technical background of different concepts and implementation aspects involved in data storage, cloud usage, and AI pipelines

ASTRO Journals' Data Sharing Policy and Recommended Best Practices.

Transparency, openness, and reproducibility are important characteristics in scientific publishing. Although many researchers embrace these characteristics, data sharing has yet to become common practice. Nevertheless, data sharing is becoming an increasingly important topic among societies, publishers, researchers, patient advocates, and funders, especially as it pertains to data from clinical trials. In response, ASTRO developed a data policy and guide to best practices for authors submitting to its journals. ASTRO's data sharing policy is that authors should indicate, in data availability statements, if the data are being shared and if so, how the data may be accessed

Transformation and integration of heterogeneous health data in a privacy-preserving distributed learning infrastructure

Problem statement: A growing volume and variety of personal health data are being collected by different entities, such as healthcare providers, insurance companies, and wearable device manufacturers. Combining heterogeneous health data offers unprecedented opportunities to augment our understanding of human health and disease. However, a major challenge to research lies in the difficulty of accessing and analyzing health data that are dispersed in their format (e.g. CSV, XML), sources (e.g., medical records, laboratory data), representation (unstructured, structured), and governance (e.g., data collection and maintenance)[2]. Such considerations are crucial when we link and use personal health data across multiple legal entities with different data governance and privacy concerns

Annotation of existing databases using Semantic Web technologies:Making data more FAIR

Making data FAIR is an elaborate task. Hospitals and/or departments have to invest into technologies usually unknown and often do not have the resources to make data FAIR. Our work aims to provide a framework and tooling where users can easily make their data (more) FAIR. This framework uses RDF and OWL-based inferencing to annotate existing databases or comma-separated files. For every database, a custom ontology is build based on the database schema, which can be annotated to describe matching standardized terminologies. In this work, we describe the tooling developed, and the current implementation in an institutional datawarehouse pertaining over 3000 rectal cancer patients. We report on the performance (time) of the extraction and annotation process by the developed tooling. Furthermore, we do show that annotation of existing databases using OWL2-based reasoning is possible. Furthermore, we show that the ontology extracted from existing databases can provide a description framework to describe and annotate existing data sources. This would target mostly the “Interoperable” aspect of FAIR