Journal papers (since 2020)

  • N. Painchaud, N. Duchateau, O. Bernard and P-M. Jodoin (2022) Echocardiography Segmentation with Enforced Temporal Consistency, IEEE Transactions on Medical Imaging, p.1-12; https://doi.org/10.1109/TMI.2022.3173669

  • E. Evain, Y. Sun, K. Faraz, D. Garcia, E. Saloux, M. De Craene and O. Bernard (2022) Motion estimation by deep learning in 2D echocardiography: synthetic dataset and validation, IEEE Transactions on Medical Imaging, p.1-12; https://doi.org/10.1109/tmi.2022.3151606

  • Y. Sun, F. Vixège, K. Faraz, S. Mendez, F. Nicoud, D. Garcia and O. Bernard (2022) A pipeline for the generation of synthetic cardiac color Doppler, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 69, p.932-941; https://doi.org/10.1109/TUFFC.2021.3136620

  • J. Lu, F. Millioz, D. Garcia, S. Salles, D. Ye and D. Friboulet (2022) Complex convolutional neural networks for ultrafast ultrasound imaging reconstruction from In-Phase/Quadrature signal, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 69(2), p.592-603; https://doi.org/10.1109/TUFFC.2021.3127916

  • L. Penarrubia, N. Pinon, E. Roux, E.E. Dávila Serrano, J.C. Richard, M. Orkisz, D. Sarrut (2022) Improving motion-mask segmentation in thoracic CT with multiplanar U-nets, Medical Physics, 49(1), p.420-431; https://doi.org/10.1002/mp.15347

  • M. Di Folco, P. Moceri, P. Clarysse, N. Duchateau (2022) Characterizing interactions between cardiac shape and deformation by non-linear manifold learning, Medical Image Analysis, 75, p.102278; https://doi.org/10.1016/j.media.2021.102278

  • A. Ramalli, E. Boni, E. Roux, H. Liebgott and P. Tortoli (2022) Design, implementation, and medical applications of 2-D ultrasound sparse arrays, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, p.in press; https://doi.org/10.1109/TUFFC.2022.3162419

  • Y. Vindas, B. Kévin Guépié, M. Almar, E. Roux, P. Delachartre (2022) Semi-automatic data annotation based on feature-space projection and local quality metrics: An application to cerebral emboli characterization, Medical Image Analysis, 79, p.1361-8415; https://doi.org/10.1016/j.media.2022.102437

  • O. Rouvière, R. Souchon, C. Lartizien et al. (2022) Detection of ISUP ≥2 prostate cancers using multiparametric MRI: prospective multicentre assessment of the non-inferiority of an artificial intelligence system as compared to the PI-RADS V.2.1 score (CHANGE study), BMJ Open, 12, p.e051274; https://doi.org/10.1136/bmjopen-2021-051274

  • A. Duran, G. Dussert, O. Rouvière, T. Jaouen, P.M. Jodoin, C. Lartizien (2022) ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scans, Medical Image Analysis, 77, p.102347; https://doi.org/10.1016/j.media.2021.102347

  • S. Wang, F. Varray, W. Liu, P. Clarysse, I.E. Magnin (2022) Measurement of local orientation of cardiomyocyte aggregates in human left ventricle free wall samples using X-ray phase-contrast microtomography, Medical Image Analysis, 75, p.102269; https://doi.org/10.1016/j.media.2021.102269

  • S. Guo, L. Wang, Q. Chen, L. Wang, J. Zhang, Y.M. Zhu (2022) Multimodal MRI Image Decision Fusion-Based Network for Glioma Classification, Frontiers in oncology, 12, p.819673; https://doi.org/10.3389/fonc.2022.819673

  • J. He, L. Wang, Y. Cao, R. Wang, Y.M. Zhu (2022) Learn Less, Infer More: Learning in the Fourier Domain for Quantitative Susceptibility Mapping, Frontiers in neuroscience, 16, p.837721; https://doi.org/10.3389/fnins.2022.837721

  • X.S Zhang, E.H Liu, X.Y Wang, X.X. Zhou, H.X. Zhang, Y.M. Zhu, X.Q. Sang, Z.X. Kuai (2022) Short-Term Repeatability of in Vivo Cardiac Intravoxel Incoherent Motion Tensor Imaging in Healthy Human Volunteers, Journal of Magnetic Resonance Imaging, 55(3), p.854-865; https://doi.org/10.1002/jmri.27847

  • L. Wang, Y. Hong, Y.B. Qin, X.Y. Cheng, F. Yang, J. Yang, Y.M. Zhu (2022) Connecting macroscopic diffusion metrics of cardiac diffusion tensor imaging and microscopic myocardial structures based on simulation, Medical Imaging, 77, p.102325; https://doi.org/10.1016/j.media.2021.102325

  • X.X. Zhou, X.Y. Wang, E.H. Liu, L. Zhang, H.X. Zhang, X.S. Zhang, Y.M. Zhu, Z.X. Kuai (2022) An Unsupervised Deep Learning Approach for Dynamic-Exponential Intravoxel Incoherent Motion MRI Modeling and Parameter Estimation in the Liver, Journal of Magnetic Resonance Imaging, p.1-10; https://doi.org/10.1002/jmri.28074

  • S. Boccalini, S.A. Si-Mohamed, H. Lacombe, A. Diaw, M. Varasteh, P.A. Rodesch, M. Villien, M. Sigovan, R. Dessouky, P. Coulon, Y. Yagil, E. Lahoud, K. Erhard, G. Rioufol, G. Finet, E. Bonnefoy-Cudraz, C. Bergerot, L. Boussel, P.C. Douek (2022) First In-Human Results of Computed Tomography Angiography for Coronary Stent Assessment With a Spectral Photon Counting Computed Tomography, Investigative radiology, 57(4), p.212–221; https://doi.org/10.1097/RLI.0000000000000835

  • F. Vixege, A. Berod, Y. Sun, S. Mendez, O. Bernard, N. Ducros, P.Y. Courand, F. Nicoud and D. Garcia (2021) Physics-constrained intraventricular vector flow mapping by color Doppler, Submitted to Physics in Medicine & Biology, 66; https://doi.org/10.1088/1361-6560/ac3ffe

  • S. Qorchi, D. Vray, M. Orkisz (2021) Estimating arterial wall deformations from automatic key-point detection and matching, Ultrasound in medicine and biology, 47(5), p.1367-1376; https://doi.org/10.1016/j.ultrasmedbio.2021.01.001

  • P. Moceri, N. Duchateau, D. Baudouy, F. Squara, S.S. Bun, E. Ferrari, M. Sermesant (2021) Additional prognostic value of echocardiographic follow-up in pulmonary hypertension - role of 3D right ventricular area strain, European Heart Journal - Cardiovascular Imaging, p.1-10; https://doi.org/10.1093/ehjci/jeab240

  • P. Moceri, N. Duchateau, B. Sartre, D. Baudouy, F. Squara, M. Sermesant, E. Ferrari (2021) Value of 3D right ventricular function over 2D assessment in acute pulmonary embolism, Echocardiography, 38, p.1694-1701; https://doi.org/10.1111/echo.15167

  • P.N. Jone, N. Duchateau, Z. Pan, D.D. Ivy, P. Moceri (2021) Right ventricular area strain from 3D echocardiography: mechanistic insight of right ventricular dysfunction in pediatric pulmonary hypertension, Journal of Heart and Lung Transplantation, 40, p.138-148; https://doi.org/10.1016/j.healun.2020.11.005

  • P. Moceri, N. Duchateau, S. Gillon, L. Jaunay, D. Baudouy, F. Squara, E. Ferrari, M. Sermesant (2021) Three-dimensional right ventricular shape and strain in congenital heart disease patients with right ventricular chronic volume loading, European Heart Journal - Cardiovascular Imaging, 22, p.1174-1181; https://doi.org/10.1093/ehjci/jeaa189

  • K. Faraz, T. Grenier, C. Ducottet, T. Epicier (2021) A Machine Learning pipeline to track the dynamics of a population of nanoparticles during in situ Environmental Transmission Electron Microscopy in gases, Microscopy and Microanalysis, 27(S1), p.2236-2237; https://doi.org/10.1017/S1431927621008060

  • H.T. Nguyen, T. Grenier, B. Leporq, et al. (2021) Quantitative Magnetic Resonance Imaging Assessment of the Quadriceps Changes during an Extreme Mountain Ultramarathon, Medicine & Science in Sports & Exercice, 53(4), p.869-881; https://doi.org/10.1249/MSS.0000000000002535

  • N. Loiseau-Witon, R. Kéchichian, S. Valette, A. Bartoli (2021) Learning 3D medical image keypoint descriptors with the triplet loss, International Journal of Computer Assisted Radiology and Surgery, 17(1), p.141-146; https://doi.org/10.1007/s11548-021-02481-3

  • O. Merveille, T. Lampert, J. Schmitz, G. Forestier, F. Feuerhake, C. Wemmert (2021) An automatic framework for fusing information from differently stained consecutive digital whole slide images: A case study in renal histology, Computer Methods and Programs in Biomedicine, 208, p.106157; https://doi.org/10.1016/j.cmpb.2021.106157

  • N. Debs, T.H. Cho, D. Rousseau, Y. Berthezene, M. Buisson, O. Eker, L. Mechtouff, N. Nighoghossian, M. Ovize, C. Frindel (2021) Impact of the reperfusion status for predicting the final stroke infarct using deep learning, NeuroImage - Clinical, 29, p.102548; https://doi.org/10.1016/j.nicl.2020.102548

  • T. Jourdan, N. Debs, C. Frindel (2021) The contribution of machine learning in the validation of commercial wearable sensors for gait monitoring in matients - A systematic review, Sensors, 21, p.4808; https://doi.org/10.3390/s21144808

  • C. Dauleac, E. Bannier, F. Cotton, C. Frindel (2021) Effect of distortion corrections on the tractography quality in spinal cord diffusion-weighted imaging, Magnetic Resonance in Medicine, 85, p.3241-3255; https://doi.org/10.1002/mrm.28665

  • A. Ahmad, G. Vanel, S. Camarasu-Pop, A. Bonnet, C. Frindel, D. Rousseau (2021) MicroVIP - Microscopy image simulation on the Virtual Imaging Platform, SoftwareX, p.100854; https://doi.org/10.1016/j.softx.2021.100854

  • P. Niu, Lihuiwang, B. Xie, M. Robini, L. Boussel, P. Douek, Y. Zhu, F. Yang (2021) Improved Image Reconstruction Using Multi-Energy Information in Spectral Photon-Counting CT, IEEE Access, 9, p.97981-97989; https://doi.org/10.1109/ACCESS.2021.3083505

  • J. Karkouri, H. Ratiney, M. Viallon, R. Prost and F. Millioz (2021) Time Undersampled Acquisition for Multidimensional Sparse Signals With Application to Magnetic Resonance Spectroscopic Imaging, IEEE Transactions on Signal Processing, 69, p.5289-5298; https://doi.org/10.1109/TSP.2021.3112931

  • T. Jourdan, A. Boutet, A. Bahi and C. Frindel (2021) Privacy-preserving IoT Framework for Activity Recognition in Personal Healthcare Monitoring, ACM Transactions on Computing Healthcare, 2(1), p.1-22; https://doi.org/10.1145/3416947

  • R.Y. Parbhudayal, C. Seegers, P. Croisille, P. Clarysse, et al. (2021) Regional myocardial function at preclinical disease stage of hypertrophic cardiomyopathy in female gene variant carriers, Internation Journal of Cardiovascular Imaging, 37(6), p.2001-2010; https://doi.org/10.1007/s10554-020-02156-1

  • Y. Qin, H. Zheng, Y. Gu, X. Huang, L. Wang, F. Yao, Y.M. Zhu, G.Z. Yang (2021) Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT, IEEE Transactions on Medical Imaging, 40(6), p.1603-1617; https://doi.org/10.1109/TMI.2021.3062280

  • X.S. Zhang, X.Q Sang, Z.X. Kuai, H.X. Zhang, J. Lou, Q. Lu, Y.M. Zhu (2021) Investigation of intravoxel incoherent motion tensor imaging for the characterization of the in vivo human heart, Magnetic Resonance in Medicine, 85(3), p.1414-1426; https://doi.org/10.1002/mrm.28523

  • S. Huang, M. Sigovan, B. Sixou (2021) Reconstruction of vascular blood flow in a vessel from tomographic projections, Biomedical Physics & Engineering Express, 7(6), p.1414-1426; https://doi.org/10.1088/2057-1976/ac2dd6

  • S.A. Si-Mohamed, M. Sigovan, J.C. Hsu, V. Tatard-Leitman, L. Chalabreysse, P.C. Naha, T. Garrivier, R. Dessouky, M. Carnaru, L. Boussel, D.P. Cormode, P.C. Douek (2021) In Vivo Molecular K-Edge Imaging of Atherosclerotic Plaque Using Photon-counting CT, Radiology, 300, p.98-107; https://doi.org/10.1148/radiol.2021203968

  • S. Si-Mohamed, S. Boccalini, P.A. Rodesch, R. Dessouky, E. Lahoud, T. Broussaud, M. Sigovan, D. Gamondes, P. Coulon, Y. Yagil, L. Boussel, P.C. Douek (2021) Feasibility of lung imaging with a large field-of-view spectral photon-counting CT system, Diagnostic and Interventional Imaging, 102(5), p.305-312; https://doi.org/10.1016/j.diii.2021.01.001

  • S. Boccalini, S. Si-Mohamed, R. Dessouky, M. Sigovan, L. Boussel, P.C. Douek (2021) Feasibility of human vascular imaging of the neck with a large field-of-view spectral photon-counting CT system, Diagnostic and Interventional Imaging, 102(5), p.329-332; https://doi.org/10.1016/j.diii.2020.12.004

  • R. Dessouky, V. De Stasio, S. Boccalini, S. Si-Mohamed, T. Broussaud, P.C. Douek, M. Sigovan (2021) Comparison of free breathing 3D mDIXON with single breath-hold 3D inversion recovery sequences for the assessment of Late Gadolinium Enhancement, European Journal of Radiology, 134, p.109427; https://doi.org/10.1016/j.ejrad.2020.109427

  • C. Caredda, L. Mahieu-Williame, R. Sablong, M. Sdika, F.C. Schneider, J. Guyotat, B. Montcel (2021) Intraoperative Resting-State Functional Connectivity Based on RGB Imaging, Diagnostics, 11, p.2067; https://doi.org/10.3390/diagnostics11112067

  • C. Caredda, L. Mahieu-Williame, R. Sablong, M. Sdika, J. Guyotat, B. Montcel (2021) Real Time Intraoperative Functional Brain Mapping Based on RGB Imaging, IRBM, 42(3), p.189-197; https://doi.org/10.1016/j.irbm.2020.04.004

  • M. Martin, B. Sciolla, M. Sdika, P. Quétin, P. Delachartre (2021) Automatic segmentation and location learning of neonatal cerebral ventricles in 3D ultrasound data combining CNN and CPPN, Computers in Biology and Medicine, 131, p.104268; https://doi.org/10.1016/j.compbiomed.2021.104268

  • P.P. Sengupta, S. Shrestha, B. Berthon, E. Messas, E. Donal, G. Tison, J.K. Min, J. D'hooge, J.U. Voigt, J. Dudley, J. Verjans, K. Shameer, K. Johnson, L. Lovstakken, M. Tabassian, M. Piccirilli, M. Pernot, N. Yanamala, N. Duchateau, N. Kagiyama, O. Bernard, P. Slomka, R. Deo and R. Arnaout (2020) Proposed requirements for cardiovascular imaging related machine learning evaluation (PRIME) checklist, JACC Cardiovascular Imaging, 13, p.2017-2035; https://doi.org/10.1016/j.jcmg.2020.07.015

  • N.Painchaud, Y.Skandarani, T.Judge, O.Bernard, A.Lalande, P-M. Jodoin (2020) Cardiac Segmentation with Strong Anatomical Guarantees, IEEE Transactions on Medical Imaging, 39(11), p.3703-3713; arXiv:2006.08825; https://doi.org/10.1109/TMI.2020.3003240

  • S. Leclerc, E. Smistad, A. Østvik, F. Cervenansky, F. Espinosa, T. Espeland, E. A. R. Berg, T. Grenier, C. Lartizien, P.-M. Jodoin, L. Lovstakken, and O. Bernard (2020) LU-Net: a multistage attention network to improve the robustness of segmentation of left ventricular structures in 2-D echocardiography, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67(12), p.2519-2530; arXiv:2004.02043; https://doi.org/10.1109/TUFFC.2020.3003403

  • E. Evain, K. Faraz, T. Grenier, D. Garcia, M. De Craene, O. Bernard (2020) A Pilot Study on Convolutional Neural Networks for Motion Estimation from Ultrasound Images, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67(12), p.2565-2573; https://doi.org/10.1109/TUFFC.2020.2976809

  • E. Smistad, A. Østvik, I. Mjåland Salte, D. Melichova, T. Mi Nguyen, H. Brunvand, T. Edvardsen, L. Leclerc, O. Bernard, B. Grenne, L. Løvstakken (2020) Real-time automatic ejection fraction and foreshortening detection using deep learning, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67(12), p.2595-2604; https://doi.org/10.1109/TUFFC.2020.2981037

  • J. Lu, F. Millioz, D. Garcia, S. Salles, W. Liu and D. Friboulet (2020) Reconstruction for Diverging-Wave Imaging Using Deep Convolutional Neural Networks, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67(12), p.2481-2492; https://doi.org/10.1109/TUFFC.2020.2986166

  • L. Chauvelot, L. Bitker, F. Dhelft, M. Mezidi, M. Orkisz, E. Davila Serrano, L. Penarrubia, H. Yonis, P. Chabert, L. Folliet, G. David, J. Provoost, P. Lecam, L. Boussel, J.C. Richard (2020) Quantitative-analysis of computed tomography in COVID-19 and non COVID-19 ARDS patients: A case-control study, Journal of Critical Care, 60, p.169-176; https://doi.org/10.1016/j.jcrc.2020.08.006

  • F. Yousefi Rizi, J. Au, H. Yli-Ollila, S. Golemati, M. Makūnaitė, M. Orkisz, N. Navab, M. MacDonald, T. Marja Laitinen, H. Behnam, Z. Gao, A. Gastounioti, R. Jurkonis, D. Vray, T. Laitinen, A. Sérusclat, K.S. Nikita, G. Zahnd (2020) Carotid wall longitudinal motion in ultrasound imaging: an expert consensus review, Ultrasound in medicine and biology, 47(5), p.2605-2624; https://doi.org/10.1016/j.ultrasmedbio.2020.06.006

  • N. Duchateau, A. King, M. De Craene (2020) Machine learning approaches for myocardial motion and deformation analysis, Frontiers in Cardiovascular Medicine, 6(190), p.1-15; https://doi.org/10.3389/fcvm.2019.00190

  • N. Duchateau, F. Loncaric, M. Cikes, A. Doltra, M. Sitges, B. Bijnens (2020) Variability in the assessment of myocardial strain patterns: implications for adequate interpretation, Ultrasound in Medicine and Biology, 46, p.244-254; https://doi.org/10.1016/j.ultrasmedbio.2019.10.013

  • P. Roca, A. Attye, L. Colas, A. Tucholka, et al. (2020) Artificial intelligence to predict clinical disability in patients with multiple sclerosis using FLAIR MRI, Diagnostic and Interventional Imaging, 101(12), p.795-802; https://doi.org/10.1016/j.diii.2020.05.009

  • J.C. Brisset, S. Kremer, S. Hannoun, F. Bonneville, et al. (2020) New OFSEP recommendations for MRI assessment of multiple sclerosis patients: Special consideration for gadolinium deposition and frequent acquisitions, Journal of Neuroradiology, 47(4), p.250-258; https://doi.org/10.1016/j.neurad.2020.01.083

  • R. Manet, L. Gergelé, T. Grenier, Z.H. Czosnyka, M. Czosnyka (2020) Development of normal pressure hydrocephalus following post-traumatic external hydrocephalus in an adult patient, British Journal of Neurosurgery, p.1-4; https://doi.org/10.1080/02688697.2020.1738340

  • R. Agier, S. Valette, R. Kéchichian, L. Fanton, R. Prost (2020) Hubless keypoint-based 3D deformable groupwise registration, Medical Image Analysis, 59, p.101564; https://doi.org/10.1016/j.media.2019.101564

  • M. Robini, F. Yang, Y. Zhu (2020) A stochastic approach to full inverse treatment planning for charged-particle therapy, Journal of Global Optimization, 77, p.853-893; https://doi.org/10.1007/s10898-020-00902-2

  • C. Dauleac, C. Frindel, P. Mertens, T. Jacquesson, F. Cotton (2020) Overcoming challenges of the human spinal cord tractography for routine clinical use: a review, Neuroradiology, 62, p.1079–1094; https://doi.org/10.1007/s00234-020-02442-8

  • A. Ahmad, C. Frindel, D. Rousseau (2020) Detecting differences of fluorescent markers distribution in single cell microscopy: textural or pointillist feature space?, Frontiers in Robotics and AI, 7, p.1–10; https://doi.org/10.3389/frobt.2020.00039

  • P. Leclerc, C. Ray, L. Mahieu-Williame, L. Alston, C. Frindel, P.F. Brevet, D. Meyronet, J. Guyotat, B. Montcel, D. Rousseau (2020) Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy, Nature, scientific report, 10, p.1462; https://doi.org/10.1038/s41598-020-58299-7

  • N. Debs, P. Rasti, L. Victor, T.H. Cho, C. Frindel, D. Rousseau (2020) Simulated perfusion MRI data to boost training of convolutional neural networks for lesion fate prediction in acute stroke, Computers in Biology and Medicine, 116, p.103579; https://doi.org/10.1016/j.compbiomed.2019.103579

  • N. Debs, C. Frindel, L. Mechtouff, M. Buisson, R. Riva, Y. Berthezene, N. Nighoghossian, D. Rousseau, O. Eker, T.H. Cho (2020) Voxel-based prediction of diffusion lesion reversal in ischemic stroke patients treated with thrombectomy, International Journal of Stroke, 15, p.289-290;

  • Z. Alaverdyan, J. Jung, R. Bouet, C. Lartizien (2020) Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: Application to epilepsy lesion screening, Medical Image Analysis, 60, p.101618; https://doi.org/10.1016/j.media.2019.101618

  • G.K. Rumindo, J. Ohayon, P. Croisille, P. Clarysse (2020) In vivo estimation of normal left ventricular stiffness and contractility based on routine cine MR acquisition, Medical Engineering & Physics, 85, p.16-26; https://doi.org/10.1016/j.medengphy.2020.09.003

  • A. Debbich, A. Ben Abdallah, M. Maatouk, B. Hmida, M. Sigovan, P. Clarysse, M.H. Bedoui (2020) A Spatiotemporal exploration and 3D modeling of blood flow in healthy carotid artery bifurcation from two modalities: Ultrasound-Doppler and phase contrast MRI, Computers in Biology and Medicine, 118, p.103644; https://doi.org/10.1016/j.compbiomed.2020.103644

  • Y. He, W. Jiao, Y. Shi, J. Lian, B. Zhao, W. Zou, Y.M. Zhu, Y. Zheng (2020) Segmenting Diabetic Retinopathy Lesions in Multispectral Images Using Low-Dimensional Spatial-Spectral Matrix Representation, IEEE Journal of Biomedical and Health Informatics, 24(2), p.493-502; https://doi.org/10.1109/JBHI.2019.2912668

  • C. Ye, D. Xu, Y. Qin, L. Wang, W. Li, Z. Kuai, Y.M. Zhu (2020) Accurate intravoxel incoherent motion parameter estimation using Bayesian fitting and reduced number of low b-values, Medical Physics, 47(9), p.4372-4385; https://doi.org/10.1002/mp.14233

  • L. Mechtouff, M. Sigovan, P.C. Douek, et al. (2020) Simultaneous assessment of microcalcifications and morphological criteria of vulnerability in carotid artery plaque using hybrid 18F-NaF PET/MRI, Journal of Nuclear Cardiology, 29, p.1064-1074; https://doi.org/10.1007/s12350-020-02400-0

  • S. Si-Mohamed, N. Chebib, M. Sigovan, L. Zumbihl, S. Turquier, S. Boccalini, L. Boussel, J.F. Mornex, V. Cottin, P.C. Douek (2020) In vivo demonstration of pulmonary microvascular involvement in COVID-19 using dual-energy computed tomography, European Respiratory Journal, 56(4), p.2002608; https://doi.org/10.1183/13993003.02608-2020

  • B. Sixou, M. Sigovan, L. Boussel (2020) Contrast enhanced tomographic reconstruction of vascular blood flow based on the Navier-Stokes equation, Inverse Problems in Science and Engineering, 28(9), p.1287-1306; https://doi.org/10.1080/17415977.2020.1724108

  • A. Debbich, A. Ben Abdallah, M. Maatouk, B. Hmida, M. Sigovan, P. Clarysse, M. Hédi Bedoui (2020) A Spatiotemporal exploration and 3D modeling of blood flow in healthy carotid artery bifurcation from two modalities: Ultrasound-Doppler and phase contrast MRI, Computers in Biology and Medicine, 118, p.103644; https://doi.org/10.1016/j.compbiomed.2020.103644

  • R. Ameli, O. Eker, M. Sigovan, T.-H. Cho, L. Mechtouff, M. Hermier, L.-P. Berner, N. Nighoghossian, Y. Berthezene (2020) Multifocal arterial wall contrast – enhancement in ischemic stroke: A mirror of systemic inflammatory response in acute stroke, Revue Neurologique, 176(3), p.194-199; https://doi.org/10.1016/j.neurol.2019.07.022

  • G. Bratke, T. Hickethier, D. Bar-Ness, A.C. Bunck, D. Maintz, G. Pahn, P. Coulon, S. Si-Mohamed, P.C. Douek, M. Sigovan (2020) Spectral Photon-Counting Computed Tomography for Coronary Stent Imaging, Investigative Radiology, 55(2), p.61-67; https://doi.org/10.1097/RLI.0000000000000610

  • B. Leporq, A. Bouhamama, F. Pilleul, F. Lame, C. Bihane, M. Sdika, J.Y. Blay, O. Beuf (2020) MRI-based radiomics to predict lipomatous soft tissue tumors malignancy: a pilot study, Cancer Imaging, 20(78), p.61-67; https://doi.org/10.1186/s40644-020-00354-7

  • C. Caredda, L. Mahieu-Williame, R. Sablong, M. Sdika, J. Guyotat, B. Montcel (2020) Optimal Spectral Combination of a Hyperspectral Camera for Intraoperative Hemodynamic and Metabolic Brain Mapping, Applied Sciences, 10(15), p.5158; https://doi.org/10.3390/app10155158