Damien Racine

The development and use of mathematical model observer (MO) requires a good level of experience and some expertise in computer science as well as in medical imaging. For more than 5 years, I have been working on the development of anthropomorphic MOs[1, 2]. I have also been working on the objective characterization of image quality in CT, whether using metrics in Fourier space (with up-to-date metrics) or in image space [3]. Also with MOs, I studied the benefit of different iterative reconstructions provided by manufacturers using homogeneous backgrounds [3]. This work led to major improvements to the acquisition protocols, and allowed us to answer specific clinical questions from the radiologists. For example, we have assessed the diagnostic accuracy of various standard abdominal protocols using MO method to estimate the potential of detecting low contrast focal lesion in liver [4]. Moreover, I have been involved in preparing and teaching a post-graduate module for medical physicist experts (MPE). This module was dedicated to model observers used for CT imaging. It is part of the European Network for Training and Education of Medical Physics Experts (http://eutempe-net.eu/) and I took part in the writing of a review paper summarizes the methods used to assess image quality in CT [5]. [1] Racine D., Ba A. H., Ott J. G., Bochud F. O., & Verdun F. R. 'Objective Assessment of Low Contrast Detectability in Computed Tomography with Channelized Hotelling Observer'. Physica Medica: PM: An International Journal Devoted to the Applications of Physics to Medicine and Biology: Official Journal of the Italian Association of Biomedical Physics (AIFB) 32 (1): 76–83, 2016 https://doi.org/10.1016/j.ejmp.2015.09.011 [2] Racine D., Viry A., Becce F., Schmidt S., Ba A., Bochud F. O., Edyvean S., Schegerer A., & Verdun F. R. 'Objective Comparison of High-Contrast Spatial Resolution and Low-Contrast Detectability for Various Clinical Protocols on Multiple CT Scanners'. Medical Physics 44 (9): e153–63, 2017 https://doi.org/10.1002/mp.12224 [3] Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: A phantom study. Racine D, Becce F, Viry A, Monnin P, Thomsen B, Verdun FR, Rotzinger DC. Phys Med. 2020 Aug;76:28-37. doi: 10.1016/j.ejmp.2020.06.004. Epub 2020 Jun 20. PMID: 32574999 [4] Racine D., Ryckx N., Ba A., Becce F., Viry A., Verdun F. R., & Schmidt S. 'Task-Based Quantification of Image Quality Using a Model Observer in Abdominal CT: A Multicentre Study'. European Radiology 28 (12): 5203–10, 2018 https://doi.org/10.1007/s00330-018-5518-8 [5] Verdun F. R., Racine D., Ott J. G., Tapiovaara M. J., Toroi P., Bochud F. O., Veldkamp W. J. H., Schegerer A., Bouwman R. W., Giron I. H., Marshall N. W., & Edyvean S. 'Image Quality in CT: From Physical Measurements to Model Observers'. Physica Medica: 31 (8): 823–43, 2015 https://doi.org/10.1016/j.ejmp.2015.08.007