Fast Line-Based Imaging of Small Sample Features

This project aims to reduce the time required to attain more detailed scans of small interesting regions present in a quick first-pass sample image. In particular, we concentrate on high fidelity imaging of small sample features via hyperspectral Raman imaging (e.g., small scale compositional variations in bone tissue). The current standard procedure for high quality hyperspectral Raman imaging of small sample features consists of four steps: first-pass imaging, detail identification, planning, and finally detail imaging. traditionally, detail imaging and planning have been carried out manually by human personnel - after acquiring some quick low-quality data in first-pass imaging, a researcher looks for interesting features (detail identification) and decides how to acquire higher-quality data for the interesting features (planning), which is done in the final detail imaging phase. In this paper we discuss automating the detail identification and planning steps, resulting in a decrease of the procedure's total integration time. We fix an arbitrary way to automate detail identification and compare several different planning methods. Our primary result is a method guaranteed to return a least cost (e.g., minimum integration time/number of scans) detail image under a general cost model. Because of their generality, the methodologies developed here may prove widely useful to basic biomedical scientists as well as to researchers in the pharmaceutical industry.