Reconstruction of Irregularly-Sampled Volumetric Data in Efficient Box Spline Spaces
| Autor: | Alexander Singh Alvarado, Alireza Entezari, Xie Xu |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Diagnostic Imaging
Carps Databases Factual Geometry Iterative reconstruction Signal-To-Noise Ratio law.invention law Lattice (order) Image Processing Computer-Assisted Animals Humans Computer Simulation Cartesian coordinate system Electrical and Electronic Engineering Mathematics Box spline Radiological and Ultrasound Technology Signal reconstruction Numerical analysis Bandwidth (signal processing) Signal Processing Computer-Assisted Computer Science Applications Spline (mathematics) Algorithm Algorithms Software |
| Zdroj: | IEEE Transactions on Medical Imaging. 31:1472-1480 |
| ISSN: | 1558-254X 0278-0062 |
| Popis: | We present a variational framework for the reconstruction of irregularly-sampled volumetric data in, nontensor-product, spline spaces. Motivated by the sampling-theoretic advantages of body centered cubic (BCC) lattice, this paper examines the BCC lattice and its associated box spline spaces in a variational setting. We introduce a regularization scheme for box splines that allows us to utilize the BCC lattice in a variational reconstruction framework. We demonstrate that by choosing the BCC lattice over the commonly-used Cartesian lattice, as the shift-invariant representation, one can increase the quality of signal reconstruction. Moreover, the computational cost of the reconstruction process is reduced in the BCC framework due to the smaller bandwidth of the system matrix in the box spline space compared to the corresponding tensor-product B-spline space. The improvements in accuracy are quantified numerically and visualized in our experiments with synthetic as well as real biomedical datasets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|