A theoretical‐experimental methodology for assessing the sensitivity of biomedical spectral imaging platforms, assays, and analysis methods

Abstract

Spectral imaging technologies have been used for many years by the remote sensing community. More recently, these approaches have been applied to biomedical problems, where they have shown great promise. However, biomedical spectral imaging has been complicated by the high variance of biological data and the reduced ability to construct test scenarios with fixed ground truths. Hence, it has been difficult to objectively assess and compare biomedical spectral imaging assays and technologies. Here, we present a standardized methodology that allows assessment of the performance of biomedical spectral imaging equipment, assays, and analysis algorithms. This methodology incorporates real experimental data and a theoretical sensitivity analysis, preserving the variability present in biomedical image data. We demonstrate that this approach can be applied in several ways: to compare the effectiveness of spectral analysis algorithms, to compare the response of different imaging platforms, and to assess the level of target signature required to achieve a desired performance. Results indicate that it is possible to compare even very different hardware platforms using this methodology. Future applications could include a range of optimization tasks, such as maximizing detection sensitivity or acquisition speed, providing high utility for investigators ranging from design engineers to biomedical scientists.

Label‐free spectroscopic tissue characterization using fluorescence excitation‐scanning spectral imaging

“…Four spectral analysis algorithms, previously used for remote sensing applications, were evaluated for their ability to detect pure signatures within tissue‐specific hyperspectral image data: CEM, nonnegatively constrained linear spectral unmixing (LSU), MF and SAM (Figure 4). We have previously investigated the sensitivity of these algorithms for unmixing a mixture of fluorophores in a spectral imaging approach . Additionally, our group has recently shown the ability to correctly unmix known concentrations of autofluorescent molecules (NADH and FAD) in solution .…”

“…Four spectral analysis techniques were used to visualize the contribution of each library component: CEM, MF, SAM and nonnegatively constrained LSU. We have previously shown the ability to assess the performance of these analysis algorithms and compare the effectiveness of each in determining the abundance of a target fluorescent signature . CEM and MF are conceptually similar, in which target detection is maximized while background noise is suppressed .…”

“…Unsupervised clustering techniques, such as machine learning and principal component analysis (PCA), have been integrated into image processing suites for remote sensing applications (eg, the ENVI software package), but there are currently no unsupervised spectral image processing approaches targeted for the biomedical imaging community. We have previously assessed the sensitivity and utility of a suite of four spectral analysis algorithms used in remote sensing for biomedical imaging applications: nonnegatively constrained linear unmixing, constrained energy minimization (CEM), matched filtering (MF) and spectral angle mapper (SAM) .…”

Compact and ultracompact spectral imagers: technology and applications in biomedical imaging

“…And yet, spectral imaging devices are still not widely used in the biomedical field. 6,9 With this review paper, we hope that researchers can (1) understand the technological trends of upcoming spectral cameras, (2) understand new specific applications that portable spectral imaging unlocked, and (3) evaluate proper spectral imaging systems for their specific applications. Section 4 reviews the acquisition methods.…”

“…Since the last review of medical hyperspectral imaging from our research group, 7 several new applications of spectral imaging in the medical and biological field became possible due to developments of compact and ultracompact spectral cameras. And yet, spectral imaging devices are still not widely used in the biomedical field 6 , 9 . With this review paper, we hope that researchers can (1) understand the technological trends of upcoming spectral cameras, (2) understand new specific applications that portable spectral imaging unlocked, and (3) evaluate proper spectral imaging systems for their specific applications.…”