Abstract
We describe a multifocal Raman micro-spectroscopy detection method based on a digital micromirror device, which allows for simultaneous “power-sharing” acquisition of Raman spectra from ad hoc sampling points. As the locations of the points can be rapidly updated in real-time via software control of a liquid-crystal spatial light modulator (LC-SLM), this technique is compatible with automated adaptive- and selective-sampling Raman spectroscopy techniques, the latter of which has previously been demonstrated for fast diagnosis of skin cancer tissue resections. We describe the performance of this instrument and show examples of multiplexed measurements on a range of test samples. Following this, we show the feasibility of reducing measurement time for power-shared multifocal Raman measurements combined with confocal auto-fluorescence imaging to provide guided diagnosis of tumours in human skin samples.
Highlights
Surgery is the mainstay of treatment for many cancers and the modality most likely to cure patients
In this paper we describe the performance of this new instrument, and demonstrate the feasibility of power-sharing multifocal multimodal spectral imaging (MSI) for the diagnosis of basal cell carcinoma (BCC) tumours in skin surgical resections
Spectra of tylenol samples were acquired using no spatial light modulation, in a single beam setup, with the digital micro-mirror device (DMD) behaving as a simple mirror
Summary
Surgery is the mainstay of treatment for many cancers and the modality most likely to cure patients. Adaptive sampling techniques can be used for measuring large samples by initially choosing random sampling locations, and subsequently generating points iteratively using interpolation information between measured points [11] Another selective-sampling approach is multimodal spectral imaging (MSI) based on integrated RMS with stratified sampling points generation by autofluorescence imaging (AF) [12]. Tissue AF imaging, which has high sensitivity, high speed and low specificity, was used as a first step to determine the key morphological features of the sample with high spatial resolution. This information was used to automatically select and prioritise the sampling points for RMS. The number of spectra required for diagnosis of tissue resections of ~1 cm was reduced to 800-3000, depending on the complexity of the tissue sample [12,13]
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