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Minimum information for dielectric measurements of biological tissues (MINDER): A framework for repeatable and reusable data

  • University of Galway
  • RWTH Aachen University

Research output: Contribution to a Journal (Peer & Non Peer)Articlepeer-review

40 Citations (Scopus)

Abstract

The dielectric properties of biological tissues characterise the interaction of human tissues with electromagnetic (EM) fields. Accurate knowledge of the dielectric properties of tissues are vital in EM-based therapeutic and diagnostic techniques, and for assessing the safety of wireless devices. Despite the importance of these properties, the field has suffered from inconsistencies in reported data. The dielectric measurement process for tissues is known to be affected by both measurement confounders and clinical confounders; however, adequate metadata is often lacking in the literature. For this reason, this work proposes a standard, called Minimum Information for Dielectric Measurements of Biological Tissues (MINDER). In the MINDER model, the minimum types of raw data and metadata needed to interpret or replicate a dielectric study are identified and described. Alongside the minimum information model, a controlled vocabulary for metadata parameters is proposed. We also provide an example of this model applied to a dielectric measurement scenario on a biological tissue sample. The MINDER model enables reproducibility of measurements, ease of interpreting and re-using data, and comparison of data across studies. Further, this standard framework will support dielectric databases, with data searchable through metadata parameters such as temperature, frequency range, tissue type, and tissue state.

Original languageEnglish
Article numbere21201
JournalInternational Journal of RF and Microwave Computer-Aided Engineering
Volume28
Issue number3
DOIs
Publication statusPublished - 1 Mar 2018

Keywords

  • biological tissues
  • dielectric measurements
  • electromagnetic medical technologies
  • metadata standards
  • minimum information models

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