- By:
- Lin, Jingsong; Ziabari, Amir K; Singanallur Vaidyanathan, Venkatakrishnan ; Rahman, Obaidullah ; Buzzard, Gregery; Bouman, Charles
- Journal Name:
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Page Number:
- 13-23
- Volume:
- TBD
- Publication Date:
- March 12, 2026
- Conference Name:
- International Conference on Computational Photography (ICCP)
- Conference Location:
- Toronto, Canada
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/TPAMI.2025.3600072
Abstract
Standard computed tomography (CT) reconstruction algorithms such as filtered back projection (FBP) and Feldkamp-Davis-Kress (FDK) require many views for producing high-quality reconstructions, which can slow image acquisition and increase cost in non-destructive evaluation (NDE) applications. Over the past 20 years, a variety of methods have been developed for computing high-quality CT reconstructions from sparse views. However, the problem of how to select the best views for CT reconstruction remains open. In this paper, we present a novel view covariance loss (VCL) function that measures the joint information of a set of views by approximating the normalized mean squared error (NMSE) of the reconstruction. We present fast algorithms for computing the VCL along with an algorithm for selecting a subset of views that approximately minimizes its value. Our experiments on simulated and measured data indicate that for a fixed number of views our proposed view covariance loss selection (VCLS) algorithm results in reconstructions with lower NRMSE, fewer artifacts, and greater accuracy than current alternative approaches.
