Experienced Researcher with a demonstrated history of working in the medical image device industry. Skilled in Machine/Deep Learning, Quantitative Imaging, C++ and Python programming, and Monte Carlo simulation with GEANT4 and GATE. Strong background in physics with a bachelor's degree in nuclear physics and a good research professional with a master’s degree focused in nuclear medical technology/ medical radiation.
Primary Affiliation: University of Geneva - Geneva, Geneva , Switzerland
A method for altering paths of optical photons that pass through a scintillator. The scintillator includes a plurality of vertical sides. The method includes forming a reflective belt inside the scintillator by creating a portion of the reflective belt inside the scintillator on a vertical plane parallel with a vertical side of the plurality of vertical sides. Creating the portion of the reflective belt includes generating a plurality of defects on the vertical plane.
Dedicated small-animal PET scanners functionality can be optimized by improving the sensitivity and spatial resolution of the scanner. Approximately most of the developed and commercially available small-animal PET scanners are equipped with pixelated scintillators; therefore, their spatial resolution is limited to the crystal pixel size. Complex fabrication, low-sensitivity, and disability in depth of interaction calculation (DOI) are the major disadvantages of pixelated crystals. However, monolithic scintillator crystals are known as one of the most commonly used substitutions, as they have higher sensitivity, DOI recognition, and lower cost. We already designed and implemented a dedicated small-animal PET scanner based on pixelated scintillator crystals and silicon photomultiplier (SiPM). In this study, we plan to present a new optimized design based on the monolithic crystal, with similar performance by the previous
The use of radiolabeled tracers in PET imaging raises concerns owing to potential risks from radiation exposure. Therefore, to reduce this potential risk in diagnostic PET imaging, efforts have been made to decrease the amount of radiotracer administered to the patient. However, decreasing the injected activity reduces the signal-to-noise Ratio (SNR) and deteriorates image quality, thus adversely impacting clinical diagnosis. Previously proposed techniques are complicated and slow, yet they yield satisfactory results at significantly low dose. In this work, we propose a deep learning algorithm to reconstruct full-dose (FD) from low-dose (LD) PET images using a fully convolutional encoder-decoder deep neural network model. The goal is to train a model to learn to reconstruct from images with only 5% of the counts to produce images corresponding to 100% of the dose. Brain PET/CT images of 140 patients acquired …