Saturday, 25 February 2017

Next event on Friday!

Title (for now): Computational imaging;
Where and when: Friday 3rd March 2017, 1pm-2pm, Room 255 (reading room), Kelvin Building, U.o.G.;
Speakers: Miguel and Laura;
Coming soon: more details on the topic and the presented papers.

Monday, 13 February 2017

Friday 3.2.17

Today's event:  Hyperspectral imaging;
Presenters: Ross and Alex;
Presented: [1] Müller, Walter, et al. "Light sheet Raman micro-spectroscopy." Optica 3.4 (2016): 452-457; [2] Jahr, Wiebke, et al. "Hyperspectral light sheet microscopy." Nature communications 6 (2015); [3] Puttonen, Eetu, et al. "Artificial target detection with a hyperspectral LiDAR over 26-h measurement." Optical Engineering 54.1 (2015): 013105-013105.
Number of attendees: 15.

After realizing I hadn’t taken any photos during the Journal Club, I decided  to draw something to put on this post. I then couldn’t stop drawing and ended up with a few drawings that should help me summarize what we talked about this time.

The topic of the day was hyperspectral imaging. The first thing that comes to my mind when I hear words that contain "spectrum" is a rainbow, and combined with ‘imaging’ they make me think of a cube. 
This little cube illustrates the idea of taking an image, in x and y, at many different wavelengths. Thinking about the articles presented today, I should have added a forth dimension to it, but I didn’t find that very easy to draw! These articles in fact describe techniques that allow to add an additional third spatial dimension to the reconstructed images. Let’s see how they do it.

The first paper, presented by Aex, was "Light sheet Raman micro-spectroscopy", by Müller et al. 2016. The aim here is to reconstruct the image of an entire volume inside the microscopy sample (3 spatial dimensions), recording the Raman spectrum of each point in the reconstructed volume (1 spectral dimension). The scheme followed in this case can be summarized as:

-  Take an image of a single plane inside the sample;
-  Acquire Raman spectrum for each point in that plane;
-  Do the same for many planes;

In order to acquire, in one single shot, an entire 2D image inside the sample, they use SPIM (Selective Plane Illumination Microscopy). With SPIM, a thin sheet of light is used to illuminate the sample from the side. This allows to excite fluorescence only in a single plane inside the sample, which can then be recorded with a single shot of the camera.

Each point excited by the light-sheet emits a whole Raman spectrum, and to obtain one image for each wavelength the authors make use of an interferometer in the imaging arm:
The light collected by the imaging objective is divided into two beams, which are sent into the two arms of the interferometer and later recombined to form the image. Moving one of the two arms changes the path length difference between the two interfering beams, and it is possible to find the positions of the second arm that make the two beams interfere in such a way that only some wavelengths are let through (constructive interference) while others are blocked (destructive interference).

Keeping the light-sheet fixed on a plane in the sample, one image is taken for each different position of the second interferometer arm. I tried to represent this in figure 1 (see below), where the three images 1, 2 and 3 are taken respectively with position 1, 2 and 3 of the second arm of the interferometer. As said above, each arm position gives info about how much light is emitted by the illuminated plane within a particular set of wavelengths. Selecting the same pixel on each image (pixel A in figure 1), one can concentrate on a single point in the sample. To obtain the Raman spectrum emitted by this point, i.e. to see how much light is emitted at each single wavelength, one only has to Fourier transform the set of data acquired by moving the interferometer arm. Finally, repeating this procedure on many planes inside the sample allows to reconstruct an entire 3D volume of Raman spectra.

Figure 1

The second paper of the day, "Hyperspectral light sheet microscopy", by Jahr et al. 2015, also uses light-sheet microscopy, but in a different way.  The authors are in this case not interested in the Raman spectrum of the sample, but instead use samples in which different molecules are labelled with different fluorophores, each emitting in a particular wavelength range. They simultaneously excite all the fluorophores and want to collect, at the same time, light emitted from all of them.

Instead of illuminating one plane at a time, a focused beam is used to illuminate only a single line inside the sample. The emitted fluorescence is then diffracted onto the detector, which records, in one single image, the spectrum of the whole line. The illumination line is then scanned through an entire plane, and all the acquired spectra are combined in order to form many images, one for each wavelength, of the same plane (see figure 2 below). By doing this on many planes, a 4D volume(x,y,z and lambda) can then be reconstructed.

Figure 2

At this point, I went back to thinking of the small rainbow cube, and this is how I picture these first two papers deal with it:

In the first one an image is taken in x and y, and spectral info about the same plane is acquired sequentially. In the second one the spectrum of a line is acquired (x-lambda image) and the line is then scanned in y. In both of them the same process is then applied at different z positions in the sample.

The third paper of the day, presented by Ross, was "Artificial target detection with a hyperspectral LiDAR over 26-h measurement." by Puttonen et al.  2015. In this case not a plane, not a line, but a point is scanned in order to reconstruct an image. Also, no more microscopy here, but light-radar. In LiDAR, light is shone onto a target, which reflects it back; the reflected light is detected, and its time of arrival (relative to the time the light pulse had been sent) defines the distance of the target. Each material has different reflective properties, which means that by analyzing the spectrum of the reflected light one can identify what kind of material the target could be made of.

Figure 4:

By scanning the whole scene with the laser pulses and recording their "return times" it is possible to localize, in x,y and z, all the present objects. By analyzing the spectrum of the light each object reflects, the authors were also able do distinguish for example between man-made objects such as a chair and natural objects such as leaves of a tree.

I think this is all for now, hope you all enjoyed the Journal Club and if you couldn't make it come along next time, on the 3rd of March!

Ciao a tutti,

PS: I leave you with the scones and chocolate we had this time :)

Thursday, 2 February 2017

Next event: Tomorrow!!

Title: Hyperspectral Imaging;
Where and when: Friday 3rd February 2017, 1pm-2pm, Andy's office (Room 246b, Kelvin Building, U.o.G);
Presenters: Alex and Ross;

Last Journal Club with us for Alex, who's leaving soon. For this there will be extra cakes and goodbye-Alex-nooooo-don't-leave-us!-food :)

See you tomorrow,