Strategies of statistical windows in PET image reconstruction to improve the user s real time experience

[EN] Nowadays, with the increase of the computational power of modern computers together with the state-of-the-art reconstruction algorithms, it is possible to obtain Positron Emission Tomography (PET) images in practically real time. These facts open the door to new applications such as radio-pharmaceuticals tracking inside the body or the use of PET for image-guided procedures, such as biopsy interventions, among others. This work is a proof of concept that aims to improve the user experience with real time PET images. Fixed, incremental, overlapping, sliding and hybrid windows are the different statistical combinations of data blocks used to generate intermediate images in order to follow the path of the activity in the Field Of View (FOV). To evaluate these different combinations, a point source is placed in a dedicated breast PET device and moved along the FOV. These acquisitions are reconstructed according to the different statistical windows, resulting in a smoother transition of positions for the image reconstructions that use the sliding and hybrid window.This study has been realized in the context of the European Union's Seventh Framework Programme for research. L. Moliner was financed with VALi+d program grant.Moliner Martínez, L.; Correcher Salvador, C.; Giménez-Alventosa, V.; Ilisie, V.; Álvarez-Gómez, JM.; Sánchez Góez, S.; Rodríguez-Álvarez, M. (2017). Strategies of statistical windows in PET image reconstruction to improve the user s real time experience. Journal of Physics: Conference Series (Online). 931:1-4. https://doi.org/10.1088/1742-6596/931/1/012025S1493

Study of CT/MRI Mutual Information Based Registration Applied in Brachytherapy

The present work aims to include magnetic resonance imaging (MRI) in a Medical Image-based Graphical platfOrm Brachytherapy module (AMIGOBrachy) which coupled to the Monte Carlo N-Particle (MCNP6) code allows absorbed dose calculations. Computed tomography (CT) and MRI images were registered using mutual information algorithms to improve tissue segmentation potentially leading to a more accurate treatment planning system