A surprising use of 3D printing has been in creating life-like models of human body parts using MRI or CT scans. Surgeons and other medical professionals can use models to plan procedures or assist in research. However, there has been a problem. The body is a messy complex thing and there is a lot of data that comes out of a typical scan. Historically, someone had to manually identify structures on each slice — a very time-consuming process — or set a threshold value and hope for the best. A recent paper by a number of researchers around the globe shows how dithering scans can vastly improve results while also allowing for much faster processing times.
As an example, a traditional workflow to create a 3D printed foot model from scan data took over 30 hours to complete including a great deal of manual intervention. The new method produced a great model in less than an hour.
One thing the researchers note is that the technique should be easy to adopt since it uses all open source software and existing image processing algorithms. There are some limitations, though. There are several things that limit the resolution and can introduce inaccuracies. For example, MRI intensity versus actual tissue appearance is highly variable based on the scanning machine’s settings and operator.
The researchers also note that advances in scanning technology will make even better 3D printed models possible. Naturally though, these prints aren’t coming off a $150 hobby-grade printer. The Connex500 printer used costs a cool quarter of a million dollars. It can print up to 14 different materials in the same job and has a reasonably large build volume (500x400x200 mm). That price, however, doesn’t include the water station to wash away support material, so budget accordingly.
We couldn’t help but wonder if you will one day have a bad part of your body scanned, printed, and then you’ll get the new part to replace the old. It seems like if you have a model of a body part, it would be just a little math to print a perfect cast, brace, or splint, too. But, then again, we aren’t doctors.
Photo Credit: Steven Keating and Ahmed Hosny/Wyss Institute at Harvard University