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Article

A Novel Kernel-Based Regularization Technique for PET Image Reconstruction

1
Electronics Department, University of Mohammed Boudiaf, 28000 M’sila, Algeria
2
Image Team, GREYC Laboratory, University of Caen Normandy, 14050 Caen CEDEX, France
3
Electronics Department, University of Mohamed El Bachir El Ibrahimi, 34030 Bordj Bou Arréridj, Algeria
*
Author to whom correspondence should be addressed.
Technologies 2017, 5(2), 37; https://doi.org/10.3390/technologies5020037
Received: 12 April 2017 / Revised: 11 June 2017 / Accepted: 16 June 2017 / Published: 19 June 2017
(This article belongs to the Special Issue Medical Imaging & Image Processing Ⅱ)
Positron emission tomography (PET) is an imaging technique that generates 3D detail of physiological processes at the cellular level. The technique requires a radioactive tracer, which decays and releases a positron that collides with an electron; consequently, annihilation photons are emitted, which can be measured. The purpose of PET is to use the measurement of photons to reconstruct the distribution of radioisotopes in the body. Currently, PET is undergoing a revamp, with advancements in data measurement instruments and the computing methods used to create the images. These computer methods are required to solve the inverse problem of “image reconstruction from projection”. This paper proposes a novel kernel-based regularization technique for maximum-likelihood expectation-maximization ( κ -MLEM) to reconstruct the image. Compared to standard MLEM, the proposed algorithm is more robust and is more effective in removing background noise, whilst preserving the edges; this suppresses image artifacts, such as out-of-focus slice blur. View Full-Text
Keywords: image reconstruction; positron emission tomography; post-reconstruction; pre-reconstruction; MLEM algorithm; EMG; kernel method; iterative algorithms image reconstruction; positron emission tomography; post-reconstruction; pre-reconstruction; MLEM algorithm; EMG; kernel method; iterative algorithms
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MDPI and ACS Style

Boudjelal, A.; Messali, Z.; Elmoataz, A. A Novel Kernel-Based Regularization Technique for PET Image Reconstruction. Technologies 2017, 5, 37. https://doi.org/10.3390/technologies5020037

AMA Style

Boudjelal A, Messali Z, Elmoataz A. A Novel Kernel-Based Regularization Technique for PET Image Reconstruction. Technologies. 2017; 5(2):37. https://doi.org/10.3390/technologies5020037

Chicago/Turabian Style

Boudjelal, Abdelwahhab, Zoubeida Messali, and Abderrahim Elmoataz. 2017. "A Novel Kernel-Based Regularization Technique for PET Image Reconstruction" Technologies 5, no. 2: 37. https://doi.org/10.3390/technologies5020037

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