DATA
This project is the result of a wonderful collaboration across departments at King's (Neuroimaging, Biomedical Engineering, Forensic and Neurodevelomental Sciences), across universities in London (KCL, UCL) and beyond geographical boundaries of the UK (Paris, New York).
I am greatly indebted to many people contributing data to this project. The perinatal data was provided by the Developing Human Connectome Project. The children data was provided by the Insitute of Child Health at University College (UCL) and the adult dataset was made available by The Brain and Spine Institute (ICM) in Paris. We started with individual structural images obtained from Magnetic Resonance Imaging (MRI). All data are anonymized and cannot be associated with individual volunteers.
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Watch how the foetus jumps around the inside the womb in MRI scan footage in the video below. The full write up is avaiable from: https://www.bbc.co.uk/news/health-47638608.
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MRI imaging is very sesnisitve to movement during the scan and only recently did researchers develop methods to correct all the motion during fetal imaging. This allowd us to built a anatomically-correct model of the brains in 3D and print these models. Similar challanges are faced during scans with adults, however, there is obviously much less movement during the scans.
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TEMPLATES OF BRAINS
When we look at individual brains we are all different, but on average we pretty much look the same. This means that brains are combined to make one "typical" brain (called an average brain). Most brain imaging methods look at the average brain, This often makes it difficult to understand what is unique in every individual (i.e. interindividual variability). For the purpose of this exhibition, we were interested in the general process of ageing. Using advanced computer software we averaged the brains of different age groups (20-30, 30-40, 40-50, 50-60 and 60-70) to reduce variability. This allowed us to show the impact that ageing has on the brain. Results demonstrate the natural history, the engineering of a brain travelling through decades.
The structural data of the average brains was then transformed into a 3D representation of the brain using either freesurfer (developing HCP) or Brainvisa (adult brains) and saved as stereolithography (.stl) file that can be handled by the 3D printer.
The methods are described in one of our papers (Croxson, Forkel, Cerliani, Thiebaut de Schotten, 2018).
Printing brains
A total of four printers were used for this project. The main printer, a DMYCO 3D printer, was built by Maleha Al-Hamadani as part of her biomedical engineering degree at King's College London. The printing software is Repetier-Host V 1.1.0. The brains were printed using support material, 10% infill using 1.75 mm Polylactic Acid (PLA) filament, a thermoplastic polymer that is derived from renewable resources, more specifically from corn starch or sugar cane. The other printers used the software Ultimaker Cura with settings tailored to each printer. The second main printer was a home-built Teilmachr 503e from the Swiss company Teil3.
Total printing time was 196 hours.