The application of microscopy in biomedical research has come a long way since Antonie van Leeuwenhoek discovered unicellular organisms. Countless innovations have positioned light microscopy as a cornerstone of modern biology and a method of choice for connecting omics datasets to their biological and clinical correlates. Still, regardless of how convincing published imaging data looks, it does not always convey meaningful information about the conditions in which it was acquired, processed, and analyzed. Adequate record-keeping, reporting, and quality control are therefore essential to ensure experimental rigor and data fidelity, allow experiments to be reproducibly repeated, and promote the proper evaluation, interpretation, comparison, and re-use. To this end, microscopy images should be accompanied by complete descriptions detailing experimental procedures, biological samples, microscope hardware specifications, image acquisition parameters, and image analysis procedures, as well as metrics accounting for instrument performance and calibration. However, universal, community-accepted Microscopy Metadata standards and reporting specifications that would result in Findable Accessible Interoperable and Reproducible (FAIR) microscopy data have not yet been established. To understand this shortcoming and to propose a way forward, here we provide an overview of the nature of microscopy metadata and its importance for fostering data quality, reproducibility, scientific rigor, and sharing value in light microscopy. The proposal for tiered Microscopy Metadata Specifications that extend the OME Data Model put forth by the 4D Nucleome Initiative and by Bioimaging North America [1-3] as well as a suite of three complementary and interoperable tools are being developed to facilitate the process of image data documentation and are presented in related manuscripts [4-6].

Background: Patient motion during emission imaging can create artifacts in the reconstructed emission distributions, which may mislead the diagnosis. For example, in myocardial-perfusion imaging, these artifacts can be mistaken for defects. Various software and hardware approaches have been developed to detect and compensate for motion. There are various ways of testing the effectiveness of motion correction methods applied in emission tomography, including the use of realistic digital anthropomorphic phantoms. Purpose: The purpose of this study was to create 3D digital anthropomorphic phantoms based on MRI data of volunteers undergoing a series of clinically relevant motions. These phantoms with combined position tracking were used to investigate both imaging-data-driven and motion tracking strategies to estimate and correct for patient motion. Methods: MRI scans were obtained of volunteers undergoing a series of clinically relevant movements. During the MRI, the motions were recorded by near-infra-red cameras tracking using external markers on the chest and abdomen. Individual-specific extended cardiac-torso (XCAT) phantoms were created fit to our volunteer MRI imaging data representing pre- and post-motion states. These XCAT phantoms were then used to generate activity and attenuation distributions. Monte Carlo methods will then be performed to simulate SPECT acquisitions, which will be used to evaluate various motion estimation and correction strategies. Results: Three volunteers were scanned in the MRI with concurrent external motion tracking. Each volunteer performed five separate motions including an axial slide, roll, shoulder twist, spine bend, and arm motion. These MRI scans were then manually digitalized into 3D anthropomorphic XCAT phantoms. Activity and attenuation distributions were created for each XCAT phantom, representing fifteen individual-specific motions. Conclusions: Our results will be combined with the external motion tracking data to determine if external motion tracking accurately reflects heart position in patients undergoing cardiac SPECT imaging. This data will also be used to evaluate other motion correction methods in the future.