2016, Förster, Ronny, Wicker, Kai, Müller, Walter, Jost, Aurélie, Heintzmann, Rainer

The reconstruction process of structured illumination microscopy (SIM) creates substantial artefacts if the specimen has moved during the acquisition. This reduces the applicability of SIM for live cell imaging, because these artefacts cannot always be recognized as such in the final image. A movement is not necessarily visible in the raw data, due to the varying excitation patterns and the photon noise. We present a method to detect motion by extracting and comparing two independent 3D wide-field images out of the standard SIM raw data without needing additional images. Their difference reveals moving objects overlaid with noise, which are distinguished by a probability theory-based analysis. Our algorithm tags motion-artefacts in the final high-resolution image for the first time, preventing the end-user from misinterpreting the data. We show and explain different types of artefacts and demonstrate our algorithm on a living cell.

2018, Diederich, Benedict, Wartmann, Rolf, Schadwinkel, Harald, Heintzmann, Rainer

Cellphones equipped with high-quality cameras and powerful CPUs as well as GPUs are widespread. This opens new prospects to use such existing computational and imaging resources to perform medical diagnosis in developing countries at a very low cost. Many relevant samples, like biological cells or waterborn parasites, are almost fully transparent. As they do not exhibit absorption, but alter the light’s phase only, they are almost invisible in brightfield microscopy. Expensive equipment and procedures for microscopic contrasting or sample staining often are not available. Dedicated illumination approaches, tailored to the sample under investigation help to boost the contrast. This is achieved by a programmable illumination source, which also allows to measure the phase gradient using the differential phase contrast (DPC) [1, 2] or even the quantitative phase using the derived qDPC approach [3]. By applying machine-learning techniques, such as a convolutional neural network (CNN), it is possible to learn a relationship between samples to be examined and its optimal light source shapes, in order to increase e.g. phase contrast, from a given dataset to enable real-time applications. For the experimental setup, we developed a 3D-printed smartphone microscope for less than 100 $ using off-the-shelf components only such as a low-cost video projector. The fully automated system assures true Koehler illumination with an LCD as the condenser aperture and a reversed smartphone lens as the microscope objective. We show that the effect of a varied light source shape, using the pre-trained CNN, does not only improve the phase contrast, but also the impression of an improvement in optical resolution without adding any special optics, as demonstrated by measurements.