With the GPU computing becoming main-stream, iterative tomographic reconstruction (IR) is becoming a com- putationally viable alternative to traditional single-shot analytical methods such as filtered back-projection. IR liberates one from the continuous X-ray source trajectories required for analytical reconstruction. We present a family of novel X-ray source trajectories for large-angle CBCT. These discrete (sparsely sampled) trajectories optimally fill the space of possible source locations by maximising the degree of mutually independent information. They satisfy a discrete equivalent of Tuy’s sufficiency condition and allow high cone-angle (high-flux) tomog- raphy. The highly isotropic nature of the trajectory has several advantages: (1) The average source distance is approximately constant throughout the reconstruction volume, thus avoiding the differential-magnification artefacts that plague high cone-angle helical computed tomography; (2) Reduced streaking artifacts due to e.g. X-ray beam-hardening; (3) Misalignment and component motion manifests as blur in the tomogram rather than double-edges, which is easier to automatically correct; (4) An approximately shift-invariant point-spread-function which enables filtering as a pre-conditioner to speed IR convergence. We describe these space-filling trajectories and demonstrate their above-mentioned properties compared with a traditional helical trajectories.
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 flux 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).