{'fraction_non_alphanumeric': 0.0136986301369863, 'fraction_numerical': 0.0, 'mean_word_length': 5.414634146341464, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Reconstruction of PET Images using Anatomical Adaptive Parameters and hybrid regularization

Positron Emission Tomography (PET) is a technique of nuclear medicine used to obtain metabolic images of the body. PET scanners are used in research, treatment and monitoring of several diseases by providing images of metabolic activity associated with the ailments. However, data produced by PET is heavily corrupted by noise and other sources of errors causing a degradation in the quality of final reconstructed images. In order to improve the image reconstruction process a new reconstruction algorithm which addresses the problem from a variational perspective, is presented in this paper. We proposed to use a modified version of the Total Variation regularization by including a second term in order to deal better with the noise, also we propose to balance both regularizing terms by calculating weighs adapted to the PET images trough the use of anatomical information from another medical modality such as computer tomography (CT) or magnetic resonance imaging  (MRI). Results on simulated images show that our proposed method is more effective in dealing with the heavy noise and preserving small structures, like possible lesions than the expectation maximization method, commonly used in commercial scanners.