Diseases of the brain are often associated with typical vascular changes. Now, scientists at LMU University Hospital Munich, Helmholtz Research Centre for Environmental Health and the Technical University of Munich (TUM) have come up with a technique for visualising the structures of all the brain's blood vessels – right down to the finest capillaries – including any pathological changes. So far, they have used the technique, which is based on a combination of biochemical methods and artificial intelligence, to capture the whole brain vasculature of a mouse.
Changes in the blood vessels are a hallmark of numerous brain disorders – from traumatic brain injury to stroke. Even diseases such as Alzheimer's show changes in the fine capillaries. In short, analysing the blood vessels is key to understanding both normal and pathological brain function.
"Now we have come much closer to achieving that goal", explains Ali Ertürk, Director of the Institute for Tissue Engineering and Regenerative Medicine at Munich's Helmholtz Centre and Principal Investigator at the Institute for Stroke and Dementia Research at the LMU University Hospital Munich.
Making organs transparent
As a first step, Ertürk's team succeeded in visualising the vascular system of mouse brains with high-resolution fluorescent microscopy without having to cut up the specimens into small sections.
In order to do this, they refined the technique of tissue cleaning, in which biological tissues are treated with special dyes to render them transparent for fluorescent microscopy. "Previously, this technique could only be used to scan either the large vessels of the brain or the small ones", says Mihail Ivilinov Todorov, a doctoral student studying under Ertürk.
Therefore the Munich-based scientists took the new approach of combining two dyes. "That gave us some great images of the brain vasculature including the capillaries", adds the biologist.
Vascular network captured using artificial intelligence
Applying artificial intelligence, researchers from the team led by Björn Menze, Professor for Machine Learning in Biomedical Imaging at the Technical University of Munich, used these images to reconstruct the entire vascular network of the brain right down to its finest details.
Such a reconstruction yields more than just images – it also allows a quantitative analysis of the vascular structures. “For example, we can statistically record the diameters of the various blood vessels or their bifurcations for different areas of the brain”, says Johannes Paetzold, doctoral student in Menze’s group.
"Over the past few years, we have developed a deep learning algorithm that specialises in detecting blood vessels in medical images", Menze explains. "This was the first time we applied it to a whole brain." The algorithm was able to reliably distinguish between blood vessels and other tissue even though some areas in the original fluorescence images were not well-illuminated and some details were distorted due to light reflections or other errors.
Understanding and diagnosing brain disorders
Mihail Ivilinov Todorov plans to use the statistical data in order to investigate vascular changes caused by stroke, while Björn Menze is looking to study the global structures of the vascular system in order to understand the role of anatomical differences in brain disorders, for example.
Benefits for the patient
The method could also be used in everyday clinical practice: "With our system, we are likely to be able to analyse the small tissue specimens from human tumours with greater accuracy", Ertürk asserts. Cancerous tissue is permeated by blood vessels, and analysing their structure helps in staging a tumour.
"This may have an optimising effect on treatment", Ertürk adds. The biologist also plans to use the new method to realise his vision for the future: the production of human organs on a 3D printer. For that to happen, a knowledge of the organ's precise vascular structure – among many other things – will be vital.
Dr. Ali Ertürk
Helmholtz Centre Munich /
Principal Investigator at the Institute for Stroke and
Dementia Research at the LMU University Hospital Munich
Prof. Dr. Björn Menze
Technical University of Munich (TUM)
Professor for Machine Learning in Biomedical Imaging
Munich School of BioEngineering and Central Institute for
Translational Cancer Research (TranslaTUM)
Phone: +49 89 289 10930
Philipp Kressirer | idw - Informationsdienst Wissenschaft
Rising water temperatures could endanger the mating of many fish species
03.07.2020 | Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung
Moss protein corrects genetic defects of other plants
03.07.2020 | Rheinische Friedrich-Wilhelms-Universität Bonn
Solar cells based on perovskite compounds could soon make electricity generation from sunlight even more efficient and cheaper. The laboratory efficiency of these perovskite solar cells already exceeds that of the well-known silicon solar cells. An international team led by Stefan Weber from the Max Planck Institute for Polymer Research (MPI-P) in Mainz has found microscopic structures in perovskite crystals that can guide the charge transport in the solar cell. Clever alignment of these "electron highways" could make perovskite solar cells even more powerful.
Solar cells convert sunlight into electricity. During this process, the electrons of the material inside the cell absorb the energy of the light....
Empa researchers have succeeded in applying aerogels to microelectronics: Aerogels based on cellulose nanofibers can effectively shield electromagnetic radiation over a wide frequency range – and they are unrivalled in terms of weight.
Electric motors and electronic devices generate electromagnetic fields that sometimes have to be shielded in order not to affect neighboring electronic...
A promising operating mode for the plasma of a future power plant has been developed at the ASDEX Upgrade fusion device at Max Planck Institute for Plasma...
Live event – July 1, 2020 - 11:00 to 11:45 (CET)
"Automation in Aerospace Industry @ Fraunhofer IFAM"
The Fraunhofer Institute for Manufacturing Technology and Advanced Materials IFAM l Stade is presenting its forward-looking R&D portfolio for the first time at...
With an X-ray experiment at the European Synchrotron ESRF in Grenoble (France), Empa researchers were able to demonstrate how well their real-time acoustic monitoring of laser weld seams works. With almost 90 percent reliability, they detected the formation of unwanted pores that impair the quality of weld seams. Thanks to a special evaluation method based on artificial intelligence (AI), the detection process is completed in just 70 milliseconds.
Laser welding is a process suitable for joining metals and thermoplastics. It has become particularly well established in highly automated production, for...
02.07.2020 | Event News
19.05.2020 | Event News
07.04.2020 | Event News
03.07.2020 | Life Sciences
03.07.2020 | Studies and Analyses
03.07.2020 | Power and Electrical Engineering