Improving spatial-resolution in high cone-angle micro-CT by source deblurring

Micro scale computed tomography (CT) can resolve many features in cellular structures, bone formations, minerals properties and composite materials not seen at lower spatial-resolution. Those features enable us to build a more comprehensive model for the object of interest. CT resolution is limited by a fundamental trade off between source size and signal-to-noise ratio (SNR) for a given acquisition time. There is a limit on the X-ray ux that can be emitted from a certain source size, and fewer photons cause a lower SNR. A large source size creates penumbral blurring in the radiograph, limiting the effective spatial-resolution in the reconstruction. High cone-angle CT improves SNR by increasing the X-ray solid angle that passes through the sample. In the high cone-angle regime current source deblurring methods break down due to incomplete modelling of the physical process. This paper presents high cone-angle source de-blurring models. We implement these models using a novel multi-slice Richardson-Lucy (M-RL) and 3D Conjugate Gradient deconvolution on experimental high cone-angle data to improve the spatial-resolution of the reconstructed volume. In M-RL, we slice the back projection volume into subsets which can be considered to have a relative uniform convolution kernel. We compare these results to those obtained from standard reconstruction techniques and current source deblurring methods (i.e. 2D Richardson-Lucy in the radiograph and the volume respectively).