Cargando…

Sparse-view tomography via displacement function interpolation

Sparse-view tomography has many applications such as in low-dose computed tomography (CT). Using under-sampled data, a perfect image is not expected. The goal of this paper is to obtain a tomographic image that is better than the naïve filtered backprojection (FBP) reconstruction that uses linear in...

Descripción completa

Detalles Bibliográficos
Autor principal: Zeng, Gengsheng L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Singapore 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7099552/
https://www.ncbi.nlm.nih.gov/pubmed/32240401
http://dx.doi.org/10.1186/s42492-019-0024-7
Descripción
Sumario:Sparse-view tomography has many applications such as in low-dose computed tomography (CT). Using under-sampled data, a perfect image is not expected. The goal of this paper is to obtain a tomographic image that is better than the naïve filtered backprojection (FBP) reconstruction that uses linear interpolation to complete the measurements. This paper proposes a method to estimate the un-measured projections by displacement function interpolation. Displacement function estimation is a non-linear procedure and the linear interpolation is performed on the displacement function (instead of, on the sinogram itself). As a result, the estimated measurements are not the linear transformation of the measured data. The proposed method is compared with the linear interpolation methods, and the proposed method shows superior performance.