comunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
comunitat-uji-handle4:
INVESTIGACION
Resum
The use of iterative algebraic methods applied to the reconstruction of Computed Tomography
(CT) Medical Images is proliferating to reconstruct high-quality CT images using far fewer views
than through analytical ... [+]
The use of iterative algebraic methods applied to the reconstruction of Computed Tomography
(CT) Medical Images is proliferating to reconstruct high-quality CT images using far fewer views
than through analytical methods. This would imply reducing the dose of X-rays applied to patients who require this medical test. Least-squares methods are a promising approach to reconstruct the images with few projections obtaining high quality. In addition, since these techniques
involve a high computational load, it is necessary to develop efficient methods that make use of
high-performance computing (HPC) tools to accelerate reconstructions. In this paper, three LeastSquares methods are analyzed: LSMB (Least-Squares Model Based), LSQR (Least Squares QR)
and LSMR (Least Squares Minimal Residual), to determine whether the LSMB method provides
a faster convergence and thus lower computational times. Moreover, a block version of both the
LSQR and LSMR methods was implemented. With them, multiple right-hand sides (multiple
slices) can be solved at the same time, taking advantage of the parallelism obtained with the
implementation of the methods using the Intel Math Kernel Library (MKL). The two implementations are compared in terms of convergence, time, and quality of the images obtained, reducing
the number of projections and combining them with a regularization and acceleration technique.
The experiments show how the implementations are scalable and obtain images of good quality
from a reduced number of views, being the LSQR method better suited for this application. [-]
Entitat finançadora
Universitat Politècnica de València | Agencia Estatal de Investigación | European Union NextGenerationEU/PRTR
Codi del projecte o subvenció
TED2021-131091B-I00 | MCIN/AEI/10.13039/501100011033
Drets d'accés
info:eu-repo/semantics/embargoedAccess