Fast compressive Raman bio-imaging via matrix completion

Fernando Soldevila,Hilton B. de Aguiar,Sylvain Gigan,Jonathan Dong,Enrique Tajahuerce

doi:10.1364/optica.6.000341

Abstract

Raman microscopy is a powerful method combining non-invasiveness with no special sample preparation. Because of this remarkable simplicity, it has been widely exploited in many fields, ranging from life and materials sciences, to engineering. Notoriously, due to the required imaging speeds for bio-imaging, it has remained a challenge how to use this technique for dynamic and large-scale imaging. Recently, compressive Raman has been put forward, allowing for fast imaging, therefore solving the issue of speed. Yet, due to the need of strong a priori information of the species forming the hyperspectrum, it has remained elusive how to apply this technique for microspectroscopy of (dynamic) biological tissues. Combining an original spectral under-sampling measurement technique with matrix completion framework for reconstruction, we demonstrate fast and inexpensive label-free molecular imaging of biological specimens (brain tissues and single cells). Therefore, our results open interesting perspectives for clinical and cell biology applications using the much faster compressive Raman framework.

Highlights

Compressive Raman has been suggested to overcome data size and imaging speed limitations [11,12,13,14]
The costly cameras of a conventional spectrometer are replaced by a digital micromirror device (DMD) that can select wavelength bins to be detected with a highly sensitive single-pixel detector
The algorithm outputs a singular value decomposition (SVD) of H, which we use for post-processing in a conventional manner used in Raman imaging

Summary

Introduction

Compressive Raman has been suggested to overcome data size and imaging speed limitations [11,12,13,14]. Compressive Raman is based on concepts of the emerging field of compressive sensing, which exploits new sampling paradigms based on experimental undersampling followed by computational reconstruction. Two strategies exist in compressive Raman: supervised [12, 15,16,17] and unsupervised compression [13, 18] Both concepts are based on the fact that the hyperspectrum H typically contains a small number of distinguishable chemical signatures, that is, it is extremely ”chemically sparse” (Fig. 1.B). This is equivalent to say that H is a low-rank matrix. DMD y x applications (for instance, this could avoid a specialist in vibrational spectroscopy for interpretation)

Methods

Results

Conclusion