Publications
per-Axon diffusivities on Human Connectome data with zonal harmonics modeling
Pizzolato, M., Canales-Rodriguez, E. J., Andersson, M., & Dyrby, T. B. (2022). Axial and radial axonal diffusivities from single encoding strongly diffusion-weighted MRI. Arxiv (https://arxiv.org/abs/2207.02526)
The axial and radial diffusivities of axons in the brain's white matter are estimated from two-shell PGSE data from the MGH Adult Diffusion dataset of the Human Connectome Project.
This is the first method that enables estimating the axial diffusivity of axons from single encoding diffusion-weighted data. This is based on modeling the axonal signal with zonal harmonics.
per-Axon T2 with spherical variance
Pizzolato, M., Andersson, M., Canales-Rodriguez, E. J., Thiran, J. P., & Dyrby, T. B. (2021). Axonal T2 estimation using the spherical variance of the strongly diffusion-weighted MRI signal. Magnetic resonance imaging (https://doi.org/10.1016/j.mri.2021.11.012)
Axonal T2 can be estimated by isolating the axonal signal contribution using a strong diffusion weighting (high b-value). However, cellular compartments and vacuoles can also be present. As these can be described by isotropic diffusion signal contributions, using the variance - instead of the mean as conventionally done - we lose sensitivity to them, thus effectively isolating the anisotropic axonal contributions that can be used to calculate the axonal T2.
MUltidimensional DIffusion (MUDI) Challenge
Pizzolato, M., Palombo, M., Bonet-Carne, E., Tax, C.M.W., Grussu, F., Ianus, A., Bogusz, F., Pieciak, T., Ning, L., Larochelle, H., Descoteaux, M., Chamberland, M., Blumberg, S.B., Mertzanidou, T., Alexander, D.C., Afzali, M., Aja-Fernández, S., Jones, D.K., Westin, C.F., Rathi, Y., Baete, S.H, Cordero-Grande, L., Ladner, T., Slator, P.J., Hajnal, J.V., Thiran, J.P, Price, A.N., Sepehrband, F., Zhang, F., & Hutter, J. (2020). Acquiring and Predicting Multidimensional Diffusion (MUDI) Data: An Open Challenge. In E. Bonet-Carne et al. (eds.), Computational Diffusion MRI, Mathematics and Visualization (soon at https://doi.org/10.1007/978-3-030-52893-5_17). Link to pdf. DTU Orbit.
Results of the challenge organized within MICCAI 2019 for predicting densely samples relaxation and diffusion data.
Adaptive Phase Correction for Diffusion-Weighted Images
Pizzolato M, Gilbert G, Thiran JP, Descoteaux M, & Deriche R. (2020). Adaptive Phase Correction for Diffusion-Weighted Images. NeuroImage Vol. 206, 116274. (https://doi.org/10.1016/j.neuroimage.2019.116274).
A method for estimating the phase of MRI images while accounting for the acquisition noise. This is used for phase-correcting diffusion-weighted images in order to remove the Rician bias.
Perfusion Deconvolution in Dynamic Susceptibility Contrast Imaging
Pizzolato M, Boutelier T, & Deriche R (2017). Perfusion deconvolution in DSC-MRI with dispersion-compliant bases. Medical Image Analysis, vol. 36, pp. 197-215, 2017. (https://doi.org/10.1016/j.media.2016.12.001)
A non-parametric method to estimate blood perfusion metrics in the brain while accounting for dispersion of the contrast agent (bolus dispersion).
Orientation-dispersed apparent axon diameter
Pizzolato M, Wassermann D, Deriche R, Thiran JP, Fick R (2019). Orientation-dispersed apparent axon diameter via multi-stage spherical mean optimization. Computational Diffusion MRI 2018. MICCAI 2018. Granada, Spain, pp. 91-101. Mathematics and Visualization. Springer, Cham. 2018 https://doi.org/10.1007/978-3-030-05831-9_8 https://infoscience.epfl.ch/record/257227
A method to estimate the apparent diameter of axons in the brain's white matter, using the diffusion MRI spherical mean information as part of the optimization used for estimating the parameters.
IMPORTANT ANNOUNCEMENT
The article uploaded at https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8759338 is an INCORRECT version of the manuscript. There has been a misunderstanding in the article version with IEEE and hopefully the issue will be solved soon. In the meantime, please find the correct version at https://infoscience.epfl.ch/record/265384/files/paper_corrected.pdf
Pizzolato M, Deriche R, Canales-Rodriguez EJ, & Thiran JP (2019). Spatially varying Monte Carlo SURE for the regularization of biomedical images. IEEE International Symposium on Biomedical Imaging (ISBI), 2019. 10.1109/ISBI.2019.8759338 https://infoscience.epfl.ch/record/265384
Spatially varying SURE
All
Journal Articles
Canales-Rodríguez, E. J., Pizzolato, M., Zhou, F. L., Barakovic, M., Thiran, J. P., Jones, D. K., Parker, G. J. M., & Dyrby, T. B. (2024). Pore size estimation in axon-mimicking microfibres with diffusion-relaxation MRI. Magn Reson Med. (http://doi.org/10.1002/mrm.29991)
Barakovic, M., Pizzolato, M., Tax, C. M., Rudrapatna, U., Magon, S., Dyrby, T. B., Granziera, C., Thiran, J.P., Jones, D., & Canales-Rodríguez, E.J. (2023). Estimating axon radius using diffusion-relaxation MRI: calibrating a surface-based relaxation model with histology. (10.3389/fnins.2023.1209521)
Girard, G., Rafael-Patiño, J., Truffet, R., Aydogan, D. B., Adluru, N., Nair, V. A., ..., Pizzolato, M., Caruyer, E., & Thiran, J. P. (2023). Tractography passes the test: results from the diffusion-simulated connectivity (DiSCo) challenge. NeuroImage, 120231. (https://doi.org/10.1016/j.neuroimage.2023.120231)
Pizzolato, M., Canales-Rodríguez, E. J., Andersson, M., & Dyrby, T. B. (2023). Axial and radial axonal diffusivities and radii from single encoding strongly diffusion-weighted MRI. Medical Image Analysis, 86, 102767. (https://doi.org/10.1016/j.media.2023.102767)
Fischi, E., Girard, G., Koch, P. J., Pizzolato, M., Bruegger, J., Piredda, G. F., Hilbert, T., Cadic-Melchior, A. G., Beanato, E., Park, C. H., Morishita, T., Wessel, M. J., Schiavi, S., Daducci, A., Kober, T., Canales-Rodríguez, E. J., Hummel, F. C. & Thiran, J. P (2022). Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session, multi-run and multi-subject MRI acquisitions. Frontiers in Radiology, 24. (https://doi.org/10.3389/fradi.2022.930666)
Bogusz, F., Pieciak, T., Afzali, M., & Pizzolato, M. (2022). Diffusion-relaxation scattered MR signal representation in a multi-parametric sequence. Magnetic Resonance Imaging. (https://doi.org/10.1016/j.mri.2022.05.007)
Maffei, C., Girard, G., Schilling, K. G., Aydogan, D. B., Adluru, N., Zhylka, A., Wu, Y., Mancini, M., Hamamci, A., Sarica, A., Teillac, A., Baete, S.H., Karimi, D., Yeh, F.C., Yildiz, M.E., Gholipour, A., Bihan-Poudec, Y., Hiba, B., Quattrone, And., Quattrone, Aldo, Boshkovski, T., Stikov, N., Yap, P.T., De Luca, A., Pluim, J., Leemans, A., Prabhakaran, V., Bendlin, B.B., Alexander, A.L., Landman, B.A., Canales-Rodríguez, E.J., Barakovic, M., Rafael-Patino, J., Yu, T., Rensonnet, G., Schiavi, S., Daducci, A., Pizzolato, M., Fischi-Gomez, E., Thiran, J.P., Dai, G., Grisot, G., Lazovski, N., Puch, S., Ramos, M., Rodrigues, P., Prčkovska, V., Jones, R., Lehman, J., Haber, S.N., & Yendiki, A. (2022). Insights from the IronTract challenge: optimal methods for mapping brain pathways from multi-shell diffusion MRI. NeuroImage, 257, 119327. (https://doi.org/10.1016/j.neuroimage.2022.119327)
Koch, P. J., Girard, G., Brügger, J., Cadic-Melchior, A. G., Beanato, E., Park, C. H., Morishita, T., Wessel, M. J., Pizzolato, M., Canales-Rodríguez, E. J., Fischi-Gomez, E., Schiavi, S., Daducci, A., Piredda, G. F., Hilbert, T., Kober, T., Thiran, J-P. & Hummel, F. C. (2022). Evaluating reproducibility and subject-specificity of microstructure-informed connectivity. NeuroImage, 119356. (https://doi.org/10.1016/j.neuroimage.2022.119356)
Pizzolato, M., Andersson, M., Canales-Rodriguez, E. J., Thiran, J. P., & Dyrby, T. B. (2021). Axonal T2 estimation using the spherical variance of the strongly diffusion-weighted MRI signal. Magnetic resonance imaging. (https://doi.org/10.1016/j.mri.2021.11.012)
Andersson, M., Pizzolato, M., Kjer, H. M., Lundell, H., & Dyrby, T. B. (2021). Does powder averaging remove dispersion bias in diffusion MRI diameter estimates within real 3D axonal architectures?. NeuroImage. (https://doi.org/10.1016/j.neuroimage.2021.118718)
Piredda GF, Hilbert T, Ravano V, Canales‐Rodríguez EJ, Pizzolato M, Meuli R, Thiran JP, Richiardi J, Kober T (2021). Data‐driven myelin water imaging based on T1 and T2 relaxometry. NMR in Biomedicine. 2021 Dec 22:e4668. (https://doi.org/10.1002/nbm.4668)
Canales-Rodríguez, E.J., Pizzolato, M., Piredda, G.F., Hilbert, T., Kunz, N., Poth, C., Yu, T., Salvador, R., Pomarol-Clotet, E., Kober, T., Thiran, J.P., Daducci, A. (2021). Comparison of non-parametric T2 relaxometry methods for myelin water quantification. Medical Image Analysis 101959 (https://doi.org/10.1016/j.media.2021.101959).
Canales-Rodríguez, E. J., Pizzolato, M., Yu, T., Piredda, G. F., Hilbert, T., Radua, J., Kober, T., & Thiran, J. P. (2021). Revisiting the T2 spectrum imaging inverse problem: Bayesian regularized non-negative least squares. NeuroImage, 244, 118582 (https://doi.org/10.1016/j.neuroimage.2021.118582).
Rafael-Patino, J., Girard, G., Truffet, R., Pizzolato, M., Caruyer, E., & Thiran, J. P. (2021). The diffusion-simulated connectivity (DiSCo) dataset. Data in Brief, 38, 107429. (https://doi.org/10.1016/j.dib.2021.107429)
Pizzolato M, Gilbert G, Thiran JP, Descoteaux M, & Deriche R. (2020). Adaptive Phase Correction for Diffusion-Weighted Images. NeuroImage Vol. 206, 116274. (https://doi.org/10.1016/j.neuroimage.2019.116274)
Pizzolato M, Boutelier T, & Deriche R (2017). Perfusion deconvolution in DSC-MRI with dispersion-compliant bases. Medical Image Analysis, vol. 36, pp. 197-215, 2017. (https://doi.org/10.1016/j.media.2016.12.001)
Canales-Rodríguez EJ, Legarreta JH, Pizzolato M, Rensonnet G, ..., Daducci A (2019). Sparse wars: A survey and comparative study of spherical deconvolution algorithms for diffusion MRI. NeuroImage, vol. 184, pp. 140-160, 2019. (https://doi.org/10.1016/j.neuroimage.2018.08.071)
Schilling KG, Nath V, Hansen C, ..., Pizzolato M, ..., Landman B (2019). Limits to anatomical accuracy of diffusion tractography using modern approaches. NeuroImage, vol. 185, pp. 1-11, 2019. (https://doi.org/10.1016/j.neuroimage.2018.10.029)
Yu, T., Rodriguez, E. J. C., Pizzolato, M., Piredda, G. F., Hilbert, T., Fischi-Gomez, E., Weigelg, M., Barakovic, M., Bach Cuadra, M., Granziera, C., Kober, T., & Thiran, J.P. (2020). Model-Informed Machine Learning for Multi-component T2 Relaxometry. arXiv preprint arXiv:2007.10225. (https://arxiv.org/pdf/2007.10225.pdf, https://doi.org/10.1016/j.media.2020.101940)
Piredda GF, Hilbert T, Canales-Rodríguez EJ, Pizzolato, M., von Deuster, C., Meuli, R., Pfeuffer, J., Daducci, A., Thiran, J.P., & Kober, T. (2020). Fast and high-resolution myelin water imaging: Accelerating multi-echo GRASE with CAIPIRINHA. Magn Reson Med. 00:1–14. ( https://doi.org/10.1002/mrm.28427)
Schilling, K. G., Rheault, F., Petit, L., Hansen, C. B., Nath, V., Yeh, F. C., ... , Pizzolato, M., ..., & Descoteaux, M. (2020). Tractography dissection variability: what happens when 42 groups dissect 14 white matter bundles on the same dataset?. Neuroimage https://doi.org/10.1016/j.neuroimage.2021.118502 bioRxiv. https://www.biorxiv.org/content/10.1101/2020.10.07.321083v1.full
Conference (full-paper) articles and book chapters
Kebiri, H., Lajous, H., Alemán-Gómez, Y., Girard, G., Rodríguez, E. C., Tourbier, S., Pizzolato, M., Ledoux,J.B., Fornari, E., Jakab, A., & Cuadra, M. B. (2021, September). Quantitative evaluation of enhanced multi-plane clinical fetal diffusion MRI with a crossing-fiber phantom. In Computational Diffusion MRI: 12th International Workshop, CDMRI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings (pp. 12-22). Cham: Springer International Publishing. (https://doi.org/10.1007/978-3-030-87615-9_2)
Rafael-Patino, J., Girard, G., Truffet, R., Pizzolato, M., Thiran, J. P., & Caruyer, E. (2021, October). The Microstructural Features of the Diffusion-Simulated Connectivity (DiSCo) Dataset. In International Workshop on Computational Diffusion MRI (pp. 159-170). Springer, Cham. (https://doi.org/10.1007/978-3-030-87615-9_14)
Fischi-Gomez, E., Rafael-Patino, J., Pizzolato, M., Piredda, G. F., Hilbert, T., Kober, T., ... & Thiran, J. P. (2021). Multi-compartment diffusion MRI, T2 relaxometry and myelin water imaging as neuroimaging descriptors for anomalous tissue detection. ISBI 2021. (https://doi.org/10.1109/ISBI48211.2021.9433856)
Rensonnet, G., Patiño, J.R., Macq, B., Thiran, J.P., Girard, G., Pizzolato, M. (2020). A Signal Peak Separation Index for axisymmetric B-tensor encoding. Accepted to Computational Diffusion MRI 2020, In Press in CDMRI,, N. Gyori et al. (eds.), Mathematics and Visualization. Preprint: http://arxiv.org/abs/2010.08389 Postprint: https://infoscience.epfl.ch/record/281009 Official: https://doi.org/10.1007/978-3-030-73018-5_3
Pizzolato, M., Palombo, M., Bonet-Carne, E., Tax, C.M.W., Grussu, F., Ianus, A., Bogusz, F., Pieciak, T., Ning, L., Larochelle, H., Descoteaux, M., Chamberland, M., Blumberg, S.B., Mertzanidou, T., Alexander, D.C., Afzali, M., Aja-Fernández, S., Jones, D.K., Westin, C.F., Rathi, Y., Baete, S.H, Cordero-Grande, L., Ladner, T., Slator, P.J., Hajnal, J.V., Thiran, J.P, Price, A.N., Sepehrband, F., Zhang, F., & Hutter, J. (2020). Acquiring and Predicting Multidimensional Diffusion (MUDI) Data: An Open Challenge. In E. Bonet-Carne et al. (eds.), Computational Diffusion MRI, Mathematics and Visualization. (https://doi.org/10.1007/978-3-030-52893-5_17) (link to pdf)
Truffet, R., Rafael-Patino, J., Girard, G., Pizzolato, M., Barillot, C., Thiran, J.P & Caruyer, E. (2020). An evolutionary framework for microstructure-sensitive generalized diffusion gradient waveforms. In proceedings of MICCAI 2020, Lima, Peru. (https://www.hal.inserm.fr/inserm-02910086/?, https://doi.org/10.1007/978-3-030-59713-9_10)
Patino, j.R., Yu, Delvigne, V., Barakovic, M., Pizzolato, M., Girard, G., Jones, D.K., Canales-Rodríguez, E.J, & Thiran J.P, (2020). DWI Simulation-Assisted Machine Learning Models for Microstructure Estimation. In E. Bonet-Carne et al. (eds.), Computational Diffusion MRI, Mathematics and Visualization. (https://doi.org/10.1007/978-3-030-52893-5_11)
Pizzolato M, Deriche R, Canales-Rodriguez EJ, & Thiran JP (2019). Spatially varying Monte Carlo SURE for the regularization of biomedical images. IEEE International Symposium on Biomedical Imaging (ISBI), 2019. (https://doi.org/10.1109/ISBI.2019.8759338) (https://infoscience.epfl.ch/record/265384)
Pizzolato M, Yu T, Canales-Rodriguez EJ, Thiran JP (2019). Robust T2 relaxometry with Hamiltonian MCMC for myelin water fraction estimation. IEEE International Symposium on Biomedical Imaging (ISBI), 2019. (https://doi.org/10.1109/ISBI.2019.8759446) (https://infoscience.epfl.ch/record/264844)
Yu T, Pizzolato M, Canales-Rodriguez EJ, Girard G, & Thiran JP (2019). Robust biophysical parameter estimation with a neural network enhanced Hamiltonian Markov Chain Monte Carlo sampler. International Conference on Information Processing in Medical Imaging, 2019, pp. 818-829. Full-paper article. (Corresponding author) (https://doi.org/10.1007/978-3-030-20351-1_64) (https://infoscience.epfl.ch/record/264887)
Pizzolato M, Wassermann D, Deriche R, Thiran JP, Fick R (2019). Orientation-dispersed apparent axon diameter via multi-stage spherical mean optimization. Computational Diffusion MRI 2018. MICCAI 2018. Granada, Spain, pp. 91-101. Mathematics and Visualization. Springer, Cham. 2018 (https://doi.org/10.1007/978-3-030-05831-9_8) (https://infoscience.epfl.ch/record/257227)
Alimi A , Pizzolato M, Fick RHJ, & Deriche R (2017). Solving the inclination sign ambiguity in three dimensional Polarized Light Imaging with a PDE-based method. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI), Melbourne, pp. 737-740 (https://doi.org/10.1109/ISBI.2017.7950624)
Fick R, Sepasian N, Pizzolato M, Ianus A, Deriche R (2017). Assessing the feasibility of estimating axon diameter using diffusion models and machine learning. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI), Melbourne, pp. 766-769 (https://doi.org/10.1109/ISBI.2017.7950631)
Pizzolato M, Fick R, Boutelier T, & Deriche R (2017). Noise floor removal via phase correction of complex diffusion-weighted images: Influence on DTI and q-space metrics. 2017 Computational Diffusion MRI, pp. 21-34. MICCAI 2016. Mathematics and Visualization. Springer, Cham. (https://doi.org/10.1007/978-3-319-54130-3_2)
Fick RHJ, Daianu M, Pizzolato M, Wassermann D, Jacobs RE, Thompson PM, Town T, Deriche R (2017). Comparison of biomarkers in transgenic Alzheimer rats using multi-shell diffusion MRI. 2017 Computational Diffusion MRI, pp. 187-199, MICCAI 2016. Mathematics and Visualization. Springer, Cham (https://doi.org/10.1007/978-3-319-54130-3_16)
Pizzolato M, Boutelier T, Fick R, & Deriche R (2016). Elucidating dispersion effects in perfusion MRI by means of dispersion-compliant bases. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, pp. 440-443. (https://doi.org/10.1109/ISBI.2016.7493302)
Fick RHJ, Pizzolato M, Wassermann D, Zucchelli M, Menegaz G, & Deriche R (2016). A sensitivity analysis of q-space indices with respect to changes in axonal diameter, dispersion and tissue composition. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, 2016, pp. 1241-1244. (https://doi.org/10.1109/ISBI.2016.7493491)
Pizzolato M, Wassermann D, Duval T, Campbell JS, Boutelier T, Cohen-Adad J, Deriche R (2016). A temperature phantom to probe the ensemble average propagator asymmetry: an in-silico study. 2016 Computational Diffusion MRI, pp. 183-194, MICCAI 2015. Mathematics and Visualization. Springer, Cham (https://doi.org/10.1007/978-3-319-28588-7_16)
Pizzolato M, Wassermann D, Boutelier T, & Deriche R (2015). Exploiting the Phase in Diffusion MRI for Microstructure Recovery: Towards Axonal Tortuosity via Asymmetric Diffusion Processes . Medical Image Computing and Computer-Assisted Intervention. MICCAI 2015. Lecture Notes in Computer Science, vol 9349, pp. 109-116. Springer, Cham (https://doi.org/10.1007/978-3-319-24553-9_14)
Fick RHJ, Wassermann D, Pizzolato M, & Deriche R (2015). A Unifying Framework for Spatial and Temporal Diffusion in Diffusion MRI. Information Processing in Medical Imaging. IPMI 2015. Lecture Notes in Computer Science, vol 9123, pp. 167-178. Springer, Cham (https://doi.org/10.1007/978-3-319-19992-4_13)
Pizzolato M, Ghosh A, Boutelier T, & Deriche R (2015). Perfusion MRI deconvolution with delay estimation and non-negativity constraints. 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), New York, NY, 2015, pp. 1073-1076. (https://doi.org/10.1109/ISBI.2015.7164057)
Pizzolato M, Ghosh A, Boutelier T, & Deriche R (2014). Magnitude and Complex Based Diffusion Signal Reconstruction. 2014 Computational Diffusion MRI, pp. 127-140. MICCAI 2014. Mathematics and Visualization. Springer, Cham (https://doi.org/10.1007/978-3-319-11182-7_12)
Fick RHJ, Pizzolato M, Wassermann D, Deriche R (2017) Diffusion MRI Anisotropy: Modeling, Analysis and Interpretation. In: Schultz T., Özarslan E., Hotz I. (eds) Modeling, Analysis, and Visualization of Anisotropy. Mathematics and Visualization. Springer, Cham, pp. 203-228. (https://doi.org/10.1007/978-3-319-61358-1_9)
Conference Abstracts
Pieciak, T., Afzali, M., Bogusz, F., Ciupek, D., Jones D.K., Pizzolato, M. (2021). Is the inversion time important? A study of the reciprocal influence of inversion time and b-value on diffusion and longitudinal relaxation in MRI. ISMRM 2021.
Girard, G., Rafael-Patino, J., Truffet, R., Pizzolato, M., Caruyer, E., Thiran, J.P. (2021). A novel in silico phantom for microstructure, tractography and quantitative connectivity estimation. ISMRM 2021.
Andersson, M., Pizzolato, M., Kjer, H.M., Lundell, H., Dyrby, T.B. (2021). Feasibility of axon diameter estimation in complex fiber architectures by powder averaging of the diffusion MRI signal. ISMRM 2021.
Afzali, M., Pieciak, T., Doring, A., Ma, D., Pizzolato, M., Jones, D.K. (2021). Estimating the pore size in a biomimetic phantom using free gradient waveforms. ISMRM 2021.
Nath, V., Pizzolato, M, ..., Hutter, J (2021). Resolving to super resolution multi-dimensional diffusion imaging (Super-MUDI) (2021). ISMRM 2021.
Maffei, C., Girard, G., ..., Pizzolato., M., ..., Yendiki, A. (2021). New insights from the IronTract challenge: Simple post-processing enhances the accuracy of diffusion tractography. ISMRM 2021.
Ciupek, D., Afzali, M.,, Bogusz, F., Pizzolato, M., Jones, D.K., Pieciak, T. (2021). The effect of inversion time on a two-compartment SMT and NODDI: an in vivo study. ISMRM 2021.
Pizzolato, M., Palombo, M., ... , & Hutter, J. (2020). Acquiring and predicting multidimensional (MUDI) data: an open challenge. ISMRM 28th Annual Meeting (Magna Cum Laude, abstract 960 https://www.ismrm.org/20/program_files/O69.htm https://www.ismrm.org/about/ampc-faq/#Q17
Maffei, C., Girard, G., Schilling, K.G., Adluru, N., Aydogan, D.B., Hamamci, A., Yeh, F-C., Mancini, M., Wu, Y., Sarica, A., Teillac, A., Baete, S.H., Karimi, D., Lin, Boada, F., Richard, N., Hiba, B., Quattrone, A., Hong, Y., Shen, D., Yap, P-T., Boshkovski, T., Campbell, J.S.W., Stikov, N., Pike, G.B., Bendlin, B.B., Alexander, A.L., Prabhakaran, V., Anderson, A., Landman, B.A. Canales-Rodrígues, E.J., Barakovic, M., Patino, J.R, Yu, T., Rensonnet, G., Schiavi, S., Daducci, A., Pizzolato, M., Fischi-Gomez, E., Thiran, J.P., Dai, G., Grisot, G., Lazovski, N., Puente, A., Rowe, M., Sanchez, I., Prchkovska, V., Jones, R., Lehman, J., Haber, S., Yendiki A (2020). The IronTract challenge: Validation and optimal tractography methods for the HCP diffusion acquisition scheme. ISMRM 28th Annual Meeting. Abstract 849 (https://www.ismrm.org/20/program_files/O68.htm), YouTube: https://www.youtube.com/watch?v=vHg9m3p1XLM.
Bogusz F., Pieciak T., Afzali M., Pizzolato, M., Aja-Fernandez S., & Jones D. (2020). Gamma kurtosis model in diffusion-relaxometry signal prediction, IEEE International Symposium on Biomedical Imaging (ISBI), 2020, Iowa City.
Canales-Rodríguez, E. J., Pizzolato, M., Piredda, G. F., Hilbert, T., Kunz, N., Kober, T., Thiran, J. P. Pot, C., & Daducci, A. (2019). Robust myelin water imaging from multi-echo T2 data using second order Tikhonov regularization with control points. In proceedings of ISMRM 27th Annual Meeting.
Schiavi, S., Pizzolato, M., Ocampo-Pineda, M., Canales-Rodríguez, E. J., Thiran, J. P., & Daducci, A. (2019). Is it feasible to directly access the bundle's specific myelin content, instead of averaging? A study with Microstructure Informed Tractography. In proc. of ISMRM 27th Annual Meeting.
Piredda, G. F., Hilbert, T., Canales-Rodríguez, E. J., Pizzolato, M., Meuli, R., Pfeuffer, J., Thiran, J. P., & Kober, T. (2019). Accelerating multi-echo GRASE with CAIPIRINHA for Fast and High resolution Myelin Water Imaging. In proceedings of ISMRM 27th Annual Meeting.
Piredda, G. F., Hilbert, T., Richiardi, T., Canales-Rodríguez, E. J., Pizzolato, M., Meuli, R., Thiran,J. P., & Kober, T. (2019). Deriving brain myelin water fraction maps from relaxometry - a data driven approach. In proceedings of ISMRM 27th Annual Meeting.
Pizzolato, M., Canales Rodríguez, E.J., Daducci, A., Thiran J.P. (2018). Multimodal microstructure imaging: combining relaxometry and diffusometry to estimate myelin, intracellular, extracellular, and cerebrospinal fluid properties. In proceedings of ISMRM 26th Annual Meeting. (https://infoscience.epfl.ch/record/264843)
Pizzolato, M. & Deriche, R. (2018). Automatic and Spatially Varying Phase Correction for Diffusion-Weighted Images. In proceedings of ISMRM 26th Annual Meeting.
Tourbier, S., Pizzolato, M., Carboni, M., Pascucci, D., Rubega, M., ... & Hagmann, P. (2018). Adopting the Brain Imaging Data Structure in the Connectome Mapper. In Alpine Brain Imaging Meeting, Champéry, Switzerland, January 7-11, 2018, Champéry, 2018.
Casagranda, S., Pizzolato, M., Torrealdea, F., Golay, X., & Boutelier, T. (2018). . Principal process analysis of dynamic GlucoCEST MRI data. In proceedings of ISMRM 26th Annual Meeting. (https://arxiv.org/pdf/1804.04585.pdf)
Canales-Rodríguez, E.J., Pizzolato, M., Aleman-Gomez, Y., Kunz, N., Pot, C., Thiran, J.P., Daducci, A. (2018). Unified multi-modal characterization of microstructural parameters of brain tissue using diffusion MRI and multi-echo T2 data In proceedings of ISMRM 26th Annual Meeting. (https://infoscience.epfl.ch/record/234472)
Pizzolato M, Fick R, Boutelier T, & Deriche R (2016). Unveiling the Dispersion Kernel in DSC-MRI by Means of Dispersion-Compliant Bases and Control Point Interpolation Techniques. In proc. of ISMRM 24th Annual Meeting (https://hal.inria.fr/hal-01408170/document)
Pizzolato M, Fick R, Boutelier T, & Deriche R (2016). Improved Vascular Transport Function Characterization in DSC-MRI via Deconvolution with Dispersion-Compliant Bases. In proceedings of ISMRM 24th Annual Meeting (https://hal.inria.fr/hal-01358775)
Dosen, S., Kristensen, G.K., Bakhshaie, B., Pizzolato, M., Smondrk, M., Krohova, J., & Popovic, M. (2010). Computer vision for selection of electrical stimulation protocol to assist prehension and grasp. Artificial Organs, vol. 34, no. 8, pp. A46, No. 112. (https://doi.org/10.1111/j.1525-1594.2010.01075.x)