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Enhanced 3D neuron imaging could diagnose brain disease

Posted: 13 September 2018 | | No comments yet

Researchers have combined an open source software programme and commercially available hardware to image neuron activity at high resolution…

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Researchers have combined commercially available hardware and open-source software, called PySight to improve rapid 2D and 3D brain imaging. 

They integrated the fastest 3D imaging solution with advances in microscopy aiding scientists to understand brain dynamics better and helping to discover new treatments.

The researchers describe PySight, a programme serving as an add-on for laser scanning microscopes. Utilising both software and hardware, the scientists improved the quality of 2D and 3D imaging of neuronal activity.

Multiphoton microscopy is a laser-based technique that can image deep into tissue and is often used to study the rapid activity patterns of neurons, blood vessels and other cells at high resolution over time. This microscopic method uses laser pulses to excite fluorescent probes, eliciting the emission of photons, some of which are detected and used to form 2D and 3D images.

As scientists try to capture the full breadth of neuronal activity, imaging has become quicker and quicker. As the time between images lessened, the scientists had to identify how to get meaningful images under the dim conditions of taking an image shorter exposure times.

“To tackle this challenge, microscopists have used a detector-readout method called photon counting,” said research team leader Dr Pablo Blinder. “However, because its implementation required extensive electronics knowledge and custom components, photon counting has never been widely adopted. In addition, commercially available photon counting solutions were ill-suited to perform very fast imaging such as required for 3D imaging. PySight’s easy installation procedure and its integration with state-of-the-art hardware eliminate such concerns.”

The improved sensitivity of this technique could facilitate rapid intraoperative identification of malignant cells in human patients, and could also improve performance of light detection and ranging, or LIDAR.

The researchers look to improve the PySight software, and look to add support for other microscopy imaging methods such as fluorescence lifetime imaging.

The study was published in The Optical Society’s journal Optica.

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