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Stratasys has launched full commercial availability of its new radiopaque 3D-printing material, RadioMatrix, in the United States—a move that could significantly change how radiology and medical imaging are taught. The article is a technology and industry report, so there is no lead academic author or journal associated with it.
RadioMatrix is designed for Stratasys’ PolyJet J750 and J850 Digital Anatomy printers, which already produce highly realistic models of organs, bones, and blood vessels. What sets the new material apart is that printed parts don’t just look and feel realistic—they also appear realistic on CT scans and X-rays. By carefully tuning the material’s radiopacity, users can match the Hounsfield unit (HU) values of real tissues such as bone, fat, or brain matter, sometimes with deviations as small as a single HU.
This precision opens the door to replacing or reducing reliance on cadavers and traditional imaging phantoms in training. Cadavers are expensive, scarce, and ethically complex, while standard phantoms often fail to reflect real human anatomy. With RadioMatrix, hospitals and training programs can print repeatable, customizable models, allowing students to practice on identical cases and researchers to run controlled experiments without waiting for donor material.
Early use in the UK has already shown promise, with 3D-printed cerebral angiography models helping train clinicians in imaging-guided procedures. In the US, where many institutions have limited access to cadavers, RadioMatrix offers a scalable alternative that could make advanced imaging education more accessible and consistent.
Beyond training, the technology may also accelerate medical device development and AI validation. Manufacturers can repeatedly test catheters or implants on identical printed anatomy, while researchers can create perfectly consistent datasets to train and validate imaging algorithms. According to Stratasys’ healthcare vice-president Erez Ben Zvi, the goal is to move beyond static phantoms and cadavers toward customizable, repeatable models that better reflect real clinical conditions.
