Abstract
There are many cases where one needs to limit the X-ray dose, or the number of projections, or both, for high frame rate (fast) imaging. Normally, it improves temporal resolution but reduces the spatial resolution of the reconstructed data. Fortunately, the redundancy of information in the temporal domain can be employed to improve spatial resolution. In this paper, we propose a novel regularizer for iterative reconstruction of time-lapse computed tomography. The non-local penalty term is driven by the available prior information and employs all available temporal data to improve the spatial resolution of each individual time frame. A high-resolution prior image from the same or a different imaging modality is used to enhance edges which remain stationary throughout the acquisition time while dynamic features tend to be regularized spatially. Effective computational performance together with robust improvement in spatial and temporal resolution makes the proposed method a competitive tool to state-of-the-art techniques.
Highlights
In many situations in X-ray tomographic imaging, it is not possible to collect enough data for good-quality reconstructions using conventional filtered backprojection techniques [1]
We propose a novel regularizer for iterative reconstruction of time-lapse computed tomography
We present the result of the final optimization procedure for α of prior image constrained compressed sensing (PICCS) and β of the NLST method
Summary
In many situations in X-ray tomographic imaging, it is not possible to collect enough data for good-quality reconstructions using conventional filtered backprojection techniques [1]. Examples can be found in medical imaging, where the accumulated dose must be kept to a minimum and in the imaging of quickly evolving events, where the time per projection or the number of projections must be severely reduced in order to capture the temporal dynamics of the scanned sample. In such cases, iterative techniques can provide better reconstructions [2]. The other modality dataset will have different image characteristics, such as intensity, resolution, geometry and noise variation This can restrict the ‘direct’ embedding of the prior information into the reconstruction process [8]
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