Stratasys Ltd. (NASDAQ: SSYS), a manufacturer of polymer 3D printing solutions, and Siemens Healthineers, a global provider of medical imaging technology, have introduced patient-specific 3D printed phantoms designed to enhance computed tomography (CT) imaging. Research presented at the Radiological Society of North America (RSNA) annual meeting demonstrates the accuracy of these models, which replicate human anatomy with high precision.
This collaboration integrates Stratasys’ RadioMatrix materials and Digital Anatomy technology with Siemens Healthineers’ imaging algorithms. The combination enables medical researchers, educators, and radiologists to replace conventional phantoms with 3D printed alternatives that improve diagnostic precision and pre-surgical planning.
Current anatomical phantoms used in radiology lack the complexity required for detailed diagnostic imaging. The patient-specific phantoms developed by Stratasys and Siemens Healthineers replicate anatomical structures with precise radiopacity, allowing for greater consistency in imaging research.
Jesús Fernández Léon, Head of Computed Tomography Product & Clinical Marketing at Siemens Healthineers, stated that this integration provides a new level of precision in computed tomography. “The integration of 3D printing solutions to create patient-realistic CT phantoms, combined with Digital Anatomy® technology from Stratasys, represents a significant innovation in computed tomography,” Fernández Léon said. “This cooperation not only enhances our ability to assess and verify the performance of modern CT systems but also ensures that our algorithms can rely on a highly realistic depiction of human anatomy.”
The printed phantoms eliminate variability in imaging studies by providing repeatable datasets of identical anatomical structures. Hospitals and research institutions are expected to integrate these models into diagnostic workflows, training programs, and clinical studies.
Role of 3D Printed Phantoms in AI Development and Medical Imaging
Medical imaging researchers require large datasets to train AI algorithms for diagnostics. Variability in human scans and ethical limitations with cadaver studies introduce challenges in developing reliable models. The use of 3D printed phantoms allows for controlled testing environments where machine learning models can be trained with consistent anatomical representations.
These models have demonstrated deviations as low as single Hounsfield units (HU) in critical areas such as grey matter and veins. This precision allows researchers to refine CT algorithms with near-identical anatomical structures, improving detection accuracy for complex pathologies.
Erez Ben Zvi, Vice President of Healthcare at Stratasys, described the implications for the medical field. “The collaboration between Stratasys and Siemens Healthineers will pave the way for innovations that enhance imaging precision, improve training efficiency, and reduce reliance on cadavers,” Ben Zvi said.
Applications of 3D Printing in Medical Research
Titanium 3D printing has been applied in the development of patient-specific implants. Anatomic Implants, a medical device company, worked with AddUp, a metal additive manufacturing firm, to develop a metatarsophalangeal joint replacement. The implant was designed to closely replicate human anatomy and has been submitted for regulatory approval under the 510(k) process. AddUp’s FormUp 350 powder bed fusion (PBF) system was used to produce the required lattice structures for the implant.
Zylo3D, a company specializing in automated chairside 3D printing, has partnered with CAD-Ray, a digital dentistry provider, to integrate AI-driven workflows into dental practices. Zylo3D’s system is designed to automate the 3D printing of dental restorations, including nightguards and models, using an AI-driven interface. CAD-Ray has introduced intraoral scanning technology that has reduced scanner costs from $40,000 to $10,000, increasing accessibility for dental professionals.
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Featured image shows Axial CT scan comparison. Photo via Stratasys/Siemens Healthineers.
