Tuesday, November 28 | 3:50 p.m.-4:00 p.m. | SSJ13-06 | Room N230B
Many common 3D printing materials might not be visible on CT scans because their radiodensity often matches that of soft tissue, say U.S. researchers."Like many radiology departments and hospitals in general, we are using our radiographic images to guide the creation of 3D-printed models," William Sensakovic, PhD, told AuntMinnie.com. "Unfortunately, few physicians consider the radiographic properties of the materials they are using."
Sensakovic and colleagues from Florida Hospital in Orlando recognized the importance of selecting 3D printing materials with high contrast compared with human tissue when creating implants or surgical tools.
The researchers used a 3D printer (Objet350 Connex3, Stratasys) to produce cubes using six common 3D printing materials and also silicone. They then scanned the materials at 80, 100, 120, and 140 kV on two different CT scanners (Ingenuity, Philips Healthcare; LightSpeed VCT, GE Healthcare).
All the 3D printing materials turned out to register between 40 and 140 Hounsfield units (HU), which does not match the Hounsfield units of water, fat, or bone.
"We found many of the materials have Hounsfield units similar to soft tissue, meaning that if used to make implants or surgical tools, they may become invisible and, thus, should likely be avoided," Sensakovic said.
The similar radiographic densities between 3D printing materials and other phantoms, however, suggest the materials are suitable for organ and muscle modeling, he noted.
"Though not currently provided by vendors, it will become important as the field matures for companies supplying 3D-printed materials to include radiographic properties, such as Hounsfield units, to aid in material selection," Sensakovic said. "This study is a first step in providing that information to physicians looking to utilize 3D printing in exciting new ways."


















![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&fit=crop&h=112&q=70&w=112)

