Spectral deblurring: An algorithm for high-resolution, hybrid spectral CT
We are developing a hybrid, dual-source micro-CT system based on the combined use of an energy integrating (EID) x-ray detector and a photon counting x-ray detector (PCXD). Due to their superior spectral resolving power, PCXDs have the potential to reduce radiation dose and to enable functional and molecular imaging with CT. In most current PCXDs, however, spatial resolution and field of view are limited by hardware development and charge sharing effects. To address these problems, we propose spectral deblurring - a relatively simple algorithm for increasing the spatial resolution of hybrid, spectral CT data. At the heart of the algorithm is the assumption that the underlying CT data is piecewise constant, enabling robust recovery in the presence of noise and spatial blur by enforcing gradient sparsity. After describing the proposed algorithm, we summarize simulation experiments which assess the trade-offs between spatial resolution, contrast, and material decomposition accuracy given realistic levels of noise. When the spatial resolution between imaging chains has a ratio of 5:1, spectral deblurring results in a 52% increase in the material decomposition accuracy of iodine, gadolinium, barium, and water vs. linear interpolation. For a ratio of 10:1, a realistic representation of our hybrid imaging system, a 52% improvement was also seen. Overall, we conclude that the performance breaks down around high frequency and low contrast structures. Following the simulation experiments, we apply the algorithm to ex vivo data acquired in a mouse injected with an iodinated contrast agent and surrounded by vials of iodine, gadolinium, barium, and water.
