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ABSTRACT
Positron emission tomography (PET) is an imaging procedure used mainly in the diagnosis and treatment of diseases. PET is also used in the preclinical research studies of small animals. However, researchers may have difficulty interpreting the particularly low-resolution images obtained via this procedure. This paper presents a new method of increasing the resolution of PET images through the use of super-resolution techniques. Aside from being resistant to the noise and other degradations that plague PET images, our proposed algorithm is also capable of preserving important structures (e.g. lesions). To this end, the proposed objective function includes a term based on the modified total variation model which allows the user to preserve texture and to deal with noise without incurring the artefacts that typically arise when the total variation norm is used. The present study shows the effectiveness of the method in recovering structures and details and indicates that, in most cases, it outperforms other state-of-the-art methods.
Acknowledgements
The authors are grateful to the program for teaching development for their support.
Disclosure statement
No potential conflict of interest was reported by the authors.
ORCID
Nelly Gordillo http://orcid.org/0000-0003-2931-7637
Liliana Avelar http://orcid.org/0000-0001-9490-2520