Selecting projection views based on error equidistribution for computed tomography

Nonuniform sampling is a useful technique to optimize the acquisition of projections with a limited budget. Existing methods for selecting important projection views have limitations, such as relying on blueprint images or excessive computing resources.

We aim to develop a simple nonuniform sampling method for selecting informative projection views suitable for practical CT applications. The proposed algorithm is inspired by two key observations: projection errors contain angle-specific information, and adding views around error peaks effectively reduces errors and improves reconstruction. Given a budget and an initial view set, the proposed method involves: estimating projection errors based on current set of projection views, adding more projection views based on error equidistribution to smooth out errors, and final image reconstruction based on the new set of projection views. This process can be recursive, and the initial view can be obtained uniformly or from a prior for greater efficiency.

Comparison with popular view selection algorithms using simulated and real data demonstrates consistently superior performance in identifying optimal views and generating high-quality reconstructions. Notably, the new algorithm performs well in both PSNR and SSIM metrics while being computationally efficient, enhancing its practicality for CT optimization.

A projection view selection algorithm based on error equidistribution is proposed, offering superior reconstruction quality and efficiency over existing methods. It is ready for real CT applications to optimize dose utilization.