Volume rendering: is this localization-based super-resolution?

Project outcome published in Biophysical Journal in 2010.

  • Esposito A*, Choimet JB, Skepper JN, Mauritz JMA, Lew VL, Kaminski CF, Tiffert T, “Quantitative imaging of human red blood cells infected with Plasmodium falciparum“, Biophys. J., 99(3):953-960

Most papers have an untold backstory that we cannot reveal in it so to focus on a main message and the most relevant discoveries. This one has a little one I wish to share. Volumetric imaging of red blood cells is not the most difficult thing I have ever done. However, accurate morphological and volumetric imaging of red blood cells infected by Plasmodium falciparum, the causative pathogen of malaria, caused me a few headaches. Let’s forget the time spent waiting for the cultures growing at the right speed to deliver bugs at the right stage of development, undecided if to sleep before or after the experiment, and always getting the decision wrong. Let’s not speak for now about the optimization of the sample preparation that that by trying and failing lead to other interesting observations. And here we focus on the very simple concept of accurate volume rendering.

In one way or another, volume rendering and estimation will require some sort of thresholding on the data so to discriminate the object from the background. As imaging conditions change even slightly from experiment to experiment, setting this threshold might confound the final outcomes. When you deal also with a sample that undergoes major morphological transitions, a simple problem soon became one for which I spent a lot of time to identify a solution for. As it happens, one perhaps does not find the best, most elegant or even the simplest solution, but the solution that they can find with their skills and tools. Mine was a brute-force solution of isosurface volume rendering, iteratively deformed by local refitting of a random sample of vertices in order to respect a specific model set for the transition of object to background. This was a method that permitted us to preserve high resolution morphological descriptions, at high accuracy and reproducibility for volume rendering.

This work was carried out while many of my colleagues were focusing on super-resolution, e.g. maximizing the spatial resolution in optical microscopy. Then, it was simple to notice that fitting a surface onto volumetric data delivers volume estimates at higher precisions than what the optical resolution of a microscope should permit. Indeed, whenever you have a model for an object, in my case the boundary of a red blood cell, in single-molecule super-resolution methods the point-spread-function of an emitter, it is possible to fit this model with a precision that is not (fully) constrained by diffraction, but – in the right conditions – only by the signal-to-noise ratio, the analytical tools and the adequacy of the model for the object.

In this Biophysical Journal paper, we focused on the biological application and, together with other published work, on the modelling of homeostasis of infected red blood cells. Also to avoid criticisms from referees, probably legitimate ones, I decided not to mention the concept of super-resolution. As my research focus is on biochemical resolution and its utilization to understand cellular decisions in cancer, I will not pursue this work any further, but I thought to write this little story.

While writing this brief story, I recalled my friend Alberto Diaspro often citing Toraldo di Francia on resolving power and information. I believe that my work was far from being breakthrough from an optical standpoint, but I wished to use it as a reminder of a fundamental issue that, often in biomedical applications, get forgotten. The resolution at which we can observe a phenomenon, irrespective of the tools used, depends both on the qualities of the instrument used and the quality of prior information we can utilize to interpret the data. Once technology permitted to image single emitters in fluorescence microscopy, the prior of point-like sources could be use to analyse images so to reveal the fullness of the information content of an image that is carried by photons.

In an experiment, information content is the most precious thing. Irrespective of the methodologies used, our protocols are designed to maximize signal-to-noise ratios and, thus, maximize information content, precision and resolution. However, as trivial as these statements are, in the biomedical sciences we often do not follow through the process of maximizing information content. Significant information can be provided by our a priori constrains and models. Moreover, a thorough understanding of information theory related to a specific assay can provide levels of precision and resolution that go beyond what we assume, at first, possible. However, priors and information theory are far too often neglected. This happens out of necessity as most people do not have the training and understanding of both biological and physical processes, and even those that might, have to invest their limited resources carefully. I wish that in the future there will be more collaborative work between the life sciences, physicists and mathematicians, aimed to better understand how to extract maximum information from experiments in the biomedical areas.

So… was our volumetric imaging super-resolution? I am not sure I care to really answer, but I wished to provoke some thoughts and make you think a little bit about the relevance of information theory in biomedical research.

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Author: Alessandro

Please visit my website to know more about me and my research http://www.quantitative-microscopy.org

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