Article Test

Home  >  Medical Research Archives  >  Issue 149  > Development of a Solvability Map
Published in the Medical Research Archives
Nov 2022 Issue

Development of a Solvability Map

Published on Nov 28, 2022




From time to time, it is necessary to determine whether there are sufficient measurements for the image reconstruction task especially when a non-standard scanning geometry is used. When the imaging system can be approximately modeled as a system of linear equations, the condition number of the system matrix indicates whether the entire system can be stably solved as a whole. When the system as a whole cannot be stably solved, the Moore-Penrose pseudo inverse matrix can be evaluated through the singular value decomposition (SVD) and then a generalized solution can be obtained. However, these methods are not practical because they require the computer memory to store the whole system matrix, which is often too large to store. Also, we do not know if the generalized solution is good enough for the application in mind. This paper proposes a practical image solvability map, which can be obtained for any practical image reconstruction algorithm. This image solvability map measures the reconstruction errors for each location using a large number of computer-simulated random phantoms. In other words, the map is generated by a Monte Carlo approach.

Index Terms: Internal problem, Inverse problem, Image reconstruction, Biomedical imaging, Computed Tomography, Computer simulations, Monte Carlo

Author info

Gengsheng Zeng

Have an article to submit?

Submission Guidelines

Submit a manuscript

Become a member

Call for papers

Have a manuscript to publish in the society's journal?