Since September 2016, I work as a lecturer in computer science at the university of Burgundy in the imagerie et vision artificielle (ImViA) laboratory. More specifically, I am part of the medical team called imagerie fonctionnelle et moléculaire et traitement des images médicales (IFTIM). My research interests include image processing, image registration, image segmentation, machine learning and deep learning. Currently, I am working on several projects:
Improving the estimation of dynamic parameters in PET imaging for breast cancer – funded by General Electric healthcare – Antoine Merlet – 2019/2022
18F-Fluorodeoxyglucose (FDG) Positron Emission Tomography (PET) is a gold standard for the evaluation of tumour metabolism and is now widely used in medical oncology for cancer detection, staging, and more recently therapy monitoring. The concomitant evaluation of tumour perfusion and tumour metabolism is promising for the monitoring of new therapies targeting both tumour perfusion and viability. However, the development of dynamic FDG-PET in clinical practice is challenging, due to the poor spatial resolution and signal to noise ratio of dynamic PET images. For example, classical reconstruction algorithms (MLEM) have shown to produce bias in short-duration frames in dynamic PET studies. Such a bias is very problematic for quantitative imaging, particularly when trying to derive an image-derived input function. Currently, both the acquisition procedure and the analysis methodology need to be improved in order to find robust and reproducible PET-based biomarkers that could be useful for the early evaluation of treatment response. The main objectives of this project are to improve the methods of dynamic FDG-PET acquisition and analysis with a new digital PET system. More specifically, it will focus on:
– Simulating pairs of dynamic pristine and PET-like images to mimic the new digital PET system.
– Developing a deep learning algorithm to denoise the dynamic PET-like images.
– Validating the developed denoising technique with the real 4D PET images currently obtained in clinical routine.
– Analysing the image data through textural features (TF) to describe global and local heterogeneities for both perfusion and metabolism.
– Using the TF analysis to predict the evaluation of treatment response with machine learning techniques.
Absolute quantification of tumour perfusion with dynamic Positron Emission Tomography – funded by the Ministry of Higher Education and Research – Nérée Payan – 2016/2020
18F-Fluorodeoxyglucose (FDG) Positron Emission Tomography (PET) is a gold standard for the evaluation of tumour metabolism and is now widely used in medical oncology for cancer detection, staging, and more recently therapy monitoring. Moreover, the dynamic acquisition of FDG PET immediately after injection allows tumour blood flow measurement. The evaluation of tumour perfusion in addition to tumour metabolism in a single scan is promising for monitoring of new therapies targeting both tumour perfusion and viability. However, the development of dynamic FDG PET in clinical practice is challenging, due to the poor spatial resolution and signal to noise ratio of dynamic PET images. It is thus vital to improve both the acquisition procedure and the analysis methodology in order to develop robust and reproducible PET- based biomarkers that could be useful for the early evaluation of treatment response. The main objectives of this project are to improve the methods of dynamic FDG PET acquisition and analysis. More specifically, it will focus on:
– Developing IT tools to optimize the quality of dynamic PET images (SNR improvement, motion correction…).
– Developing analytical models for data extraction (global and local blood flow, tumour permeability…).
– Validating pre-clinical and clinical data obtained in our department.
– Applying these new tools to a multicentre national study starting in September 2016 (PHRC PREMETHEP).
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