​Non-destructive testing (NDT) is used in the nuclear, automotive, petroleum, and other industries to inspect a wide variety of parts for defects in their geometry or materials. In conventional NDT, the signals acquired are used to generate 2D images of the parts being inspected in real time. The jump from 2D to much more complex 3D imaging, however, remains a challenge. The main difficulty is that current NDT equipment simply cannot accommodate the large number of signals and processing power required. For the first time ever, this technological hurdle has been addressed. 

CEA-List scientists used a Fourier approach to 3D reconstruction to develop new algorithms that effectively bring the number of computational operations required to reconstruct the image to a bare minimum. They then integrated the algorithms into a prototype 4D (real-time 3D) imager.

And it worked: In tests, the prototype successfully detected cracks, porosities, and shrinkage intentionally introduced into steel blocks produced by additive manufacturing at the Additive Factory Hub (AFH). Even better, the prototype outperformed the state of the art, "seeing" porosities measuring just 0.6 mm in diameter and precision-locating them deep inside the material to within a tenth of a millimeter.

The researchers are now trying to make this new imaging technique faster and more powerful without compromising on image quality—the prerequisite to successful scale up and transfer of the technology.