(function(doc, html, url) { var widget = doc.createElement("div"); widget.innerHTML = html; var script = doc.currentScript; // e = a.currentScript; if (!script) { var scripts = doc.scripts; for (var i = 0; i < scripts.length; ++i) { script = scripts[i]; if (script.src && script.src.indexOf(url) != -1) break; } } script.parentElement.replaceChild(widget, script); }(document, '

Motion history image (MHI) algorithm for monitoring cerebral ischemia of middle cerebral artery

What is it about?

The MHI is a view-based temporal template method and has been applied successfully to various Laser Speckle Contrast Imaging (LSCI) data to visualize the perfusion variation locations during different stimuli. It generates a single bidimensional map from an image sequence showing perfusion variations in time. In the generated image, each pixel is a function of recency of motion (perfusion variations in our case) of the image sequence.

Why is it important?

The MHI images clearly illustrates the locations where perfusion decreases during occlusion and that is more easily obtained in comparison to a visual inspection of all of the raw dynamic fluorescent images constituting the recordings.

Read more on Kudos…
The following have contributed to this page:
Mohammad Zaheer Ansari
' ,"url"));