Massively parallelizable list-mode reconstruction using a Monte Carlo-based elliptical Gaussian model

Sportelli, Giancarlo; Ortuño Fisac, Juan Enrique; Vaquero, Juan José; Desco, Manuel y Santos Lleo, Andres de (2013). Massively parallelizable list-mode reconstruction using a Monte Carlo-based elliptical Gaussian model. "Medical Physics", v. 40 (n. 1); pp.. ISSN 0094-2405. https://doi.org/10.1118/1.4771936.

Descripción

Título: Massively parallelizable list-mode reconstruction using a Monte Carlo-based elliptical Gaussian model
Autor/es:
  • Sportelli, Giancarlo
  • Ortuño Fisac, Juan Enrique
  • Vaquero, Juan José
  • Desco, Manuel
  • Santos Lleo, Andres de
Tipo de Documento: Artículo
Título de Revista/Publicación: Medical Physics
Fecha: 2013
Volumen: 40
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Ingeniería Electrónica
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Purpose: A fully three-dimensional (3D) massively parallelizable list-mode ordered-subsets expectation-maximization (LM-OSEM) reconstruction algorithm has been developed for high-resolution PET cameras. System response probabilities are calculated online from a set of parameters derived from Monte Carlo simulations. The shape of a system response for a given line of response (LOR) has been shown to be asymmetrical around the LOR. This work has been focused on the development of efficient region-search techniques to sample the system response probabilities, which are suitable for asymmetric kernel models, including elliptical Gaussian models that allow for high accuracy and high parallelization efficiency. The novel region-search scheme using variable kernel models is applied in the proposed PET reconstruction algorithm. Methods: A novel region-search technique has been used to sample the probability density function in correspondence with a small dynamic subset of the field of view that constitutes the region of response (ROR). The ROR is identified around the LOR by searching for any voxel within a dynamically calculated contour. The contour condition is currently defined as a fixed threshold over the posterior probability, and arbitrary kernel models can be applied using a numerical approach. The processing of the LORs is distributed in batches among the available computing devices, then, individual LORs are processed within different processing units. In this way, both multicore and multiple many-core processing units can be efficiently exploited. Tests have been conducted with probability models that take into account the noncolinearity, positron range, and crystal penetration effects, that produced tubes of response with varying elliptical sections whose axes were a function of the crystal's thickness and angle of incidence of the given LOR. The algorithm treats the probability model as a 3D scalar field defined within a reference system aligned with the ideal LOR. Results: This new technique provides superior image quality in terms of signal-to-noise ratio as compared with the histogram-mode method based on precomputed system matrices available for a commercial small animal scanner. Reconstruction times can be kept low with the use of multicore, many-core architectures, including multiple graphic processing units. Conclusions: A highly parallelizable LM reconstruction method has been proposed based on Monte Carlo simulations and new parallelization techniques aimed at improving the reconstruction speed and the image signal-to-noise of a given OSEM algorithm. The method has been validated using simulated and real phantoms. A special advantage of the new method is the possibility of defining dynamically the cut-off threshold over the calculated probabilities thus allowing for a direct control on the trade-off between speed and quality during the reconstruction.

Más información

ID de Registro: 28896
Identificador DC: http://oa.upm.es/28896/
Identificador OAI: oai:oa.upm.es:28896
Identificador DOI: 10.1118/1.4771936
URL Oficial: http://scitation.aip.org/content/aapm/journal/medphys/40/1/10.1118/1.4771936
Depositado por: Memoria Investigacion
Depositado el: 30 Jun 2014 16:16
Ultima Modificación: 22 Sep 2014 11:43
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