Super-resolution model-based iterative reconstruction for lens-coupled micro-CT imaging

Abstract Lens-coupled high-resolution micro-computed tomography (micro-CT) uses a visible light magnification system behind the x-ray path to achieve higher resolution imaging than conventional micro-CT. However, the spatial resolution is theoretically limited by optical diffraction and mechanical control precision. As a result, the current system resolution is still insufficient for some applications, such as the imaging of biological materials whose structures are on the nanometer scale. To overcome this limitation, a super-resolution algorithm can be employed to improve the image resolution beyond the theoretical upper bound of the ideal spatial resolution of the system. In this work, a super-resolution model-based iterative reconstruction (SR-MBIR) algorithm is proposed based on a lens-coupled high-resolution micro-CT system and a high-precision nano-stage attached to the rotation stage of the system. The algorithm employs a scanning program that dithers the object via the nano-stage to obtain multiple sets of projection images with sub-pixel information. The blur and noise statistical models are introduced into the physical model for iterative reconstruction, allowing for super-resolution, deblurring, and noise suppression. The results of simulation data and actual data show that the SR-MBIR algorithm has a prominent effect in improving image resolution. The reconstructed images have sharper edges, better details, higher signal-to-noise ratio, and can effectively suppress the systematic blur and noise in the imaging process, thus achieving superior interior reconstruction quality.