[{"innerBlocks":[],"name":"core\/freeform","postId":1742,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"An enhanced form of three-dimensional imaging that combines commercial hardware and open-source software may aid brain imaging.

Researchers say the combination can improve rapid two-dimensional and three-dimensional imaging of the brain and other tissues. The findings were released in an article in Optica, the Optical Society\u2019s journal for research.

The integration of fast 3D imaging technology could support an advance in microscopy that could help scientists better understand brain dynamics and discover new treatments for health problems such as stroke, epilepsy and dementia.

Researchers say the open-source software, called PySight, acts as a photon counting add-on for laser scanning microscopes. Because it can image deep into tissue, a laser-based technique known as multiphoton microscopy is often used to study the rapid activity of neurons, blood vessels and other cells at high resolution over time. The method uses laser pulses that excite fluorescent probes, eliciting the emission of photons, some of which are detected and used to form 2D and 3D images.

Also See:<\/b> Brain MRI images can predict cognitive function after heart attack<\/a>

The technology is facing challenges, however, because trying to capture the full breadth of neuron activity forces take images faster, and because of that, fewer photons are available to form images\u2014much like trying to take a picture under dim lighting with faster exposure times.

\u201cTo overcome this hurdle, microscopists have used a detector-readout method called photon counting,\u201d says research team leader Pablo Blinder from Tel Aviv University in Israel. \u201cHowever, because its implementation required extensive electronics knowledge and custom components, photon counting has never been widely adopted. In addition, commercially available solutions were ill-suited to perform very fast imaging such as required for 3D imaging.\u201d

PsSight offers improved sensitivity that can pick up more photons to enable improved imaging, researchers say. That capability, paired with commercially available imaging equipment, could support rapid intraoperative identification of malignant cells in human patients with multiphoton microscopy.

\u201cPySight only stores the precise detection time of each photon, allowing researchers to conduct rapid imaging of large volumes over long sessions, without compromising spatial or temporal resolution.\" Blinder says.

To reconstruct a multidimensional image, knowing when each photon hits the detector isn\u2019t enough. It\u2019s necessary to also know where it originated in the brain. \u201cThis is even trickier if you want to simplify the system and avoid synchronizing the different scanning elements,\u201d he adds. \u201cTo accomplish this, the software reads a list of photon arrival times along timing signals from the scanning elements, determines the origin of each photon within the sample and generates 3D movies that can span three or more dimensions.\u201d

\u201cBecause the software is available to the public, it should greatly aid labs previously deterred by the high technical barrier that accompanied 3D imaging,\u201d Blinder concludes. Because of its generic application interface, PySight could also be used to interpret similar photon detection time lists from suitable hardware devices.

To improve the PySight software, researchers say they hope to add support for other microscopy imaging methods such as fluorescence lifetime imaging.
","saveContent":"An enhanced form of three-dimensional imaging that combines commercial hardware and open-source software may aid brain imaging.

Researchers say the combination can improve rapid two-dimensional and three-dimensional imaging of the brain and other tissues. The findings were released in an article in Optica, the Optical Society\u2019s journal for research.

The integration of fast 3D imaging technology could support an advance in microscopy that could help scientists better understand brain dynamics and discover new treatments for health problems such as stroke, epilepsy and dementia.

Researchers say the open-source software, called PySight, acts as a photon counting add-on for laser scanning microscopes. Because it can image deep into tissue, a laser-based technique known as multiphoton microscopy is often used to study the rapid activity of neurons, blood vessels and other cells at high resolution over time. The method uses laser pulses that excite fluorescent probes, eliciting the emission of photons, some of which are detected and used to form 2D and 3D images.

Also See:<\/b> Brain MRI images can predict cognitive function after heart attack<\/a>

The technology is facing challenges, however, because trying to capture the full breadth of neuron activity forces take images faster, and because of that, fewer photons are available to form images\u2014much like trying to take a picture under dim lighting with faster exposure times.

\u201cTo overcome this hurdle, microscopists have used a detector-readout method called photon counting,\u201d says research team leader Pablo Blinder from Tel Aviv University in Israel. \u201cHowever, because its implementation required extensive electronics knowledge and custom components, photon counting has never been widely adopted. In addition, commercially available solutions were ill-suited to perform very fast imaging such as required for 3D imaging.\u201d

PsSight offers improved sensitivity that can pick up more photons to enable improved imaging, researchers say. That capability, paired with commercially available imaging equipment, could support rapid intraoperative identification of malignant cells in human patients with multiphoton microscopy.

\u201cPySight only stores the precise detection time of each photon, allowing researchers to conduct rapid imaging of large volumes over long sessions, without compromising spatial or temporal resolution.\" Blinder says.

To reconstruct a multidimensional image, knowing when each photon hits the detector isn\u2019t enough. It\u2019s necessary to also know where it originated in the brain. \u201cThis is even trickier if you want to simplify the system and avoid synchronizing the different scanning elements,\u201d he adds. \u201cTo accomplish this, the software reads a list of photon arrival times along timing signals from the scanning elements, determines the origin of each photon within the sample and generates 3D movies that can span three or more dimensions.\u201d

\u201cBecause the software is available to the public, it should greatly aid labs previously deterred by the high technical barrier that accompanied 3D imaging,\u201d Blinder concludes. Because of its generic application interface, PySight could also be used to interpret similar photon detection time lists from suitable hardware devices.

To improve the PySight software, researchers say they hope to add support for other microscopy imaging methods such as fluorescence lifetime imaging.
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Open-source software may aid brain imaging to find disease treatments

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