Marco Pizzolato

Conference proceedings (reverse chronological order):

Pizzolato M, Deriche R, Canales-Rodriguez EJ, Thiran JP. Spatially varying Monte Carlo SURE for the regularization of biomedical images. Accepted to IEEE International Symposium on Biomedical Imaging (ISBI), 2019a. Waiting for D.O.I. (IEEE). Full-paper article. (https://infoscience.epfl.ch/record/265384).

Pizzolato M, Yu  T,  Canales-Rodriguez EJ, Thiran JP. Robust T2 relaxometry with Hamiltonian MCMC for myelin water fraction estimation. Accepted to IEEE International Symposium on Biomedical Imaging (ISBI), 2019b. Waiting for D.O.I. (IEEE). Full-paper article. (https://infoscience.epfl.ch/record/264844).

Yu T, Pizzolato M, Canales-Rodriguez EJ, Girard G, Thiran JP. Robust biophysical parameter estimation with a neural network enhanced Hamiltonian Markov Chain Monte Carlo sampler. Accepted to International Conference on Information Processing in Medical Imaging, 2019, pp. 818-829. Full-paper article. (Corresponding author) (D.O.I.: 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. 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. Full-paper article. 2018a (D.O.I.:  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. 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 (D.O.I.: https://doi.org/10.1109/ISBI.2017.7950624).

Fick R, Sepasian N, Pizzolato M, Ianus A, Deriche R. 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 (D.O.I.: https://doi.org/10.1109/ISBI.2017.7950631).

Pizzolato M, Fick R, Boutelier T, Deriche R. 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 (Oral presentation). (D.O.I.: 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. 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. (D.O.I.: https://doi.org/10.1007/978-3-319-54130-3_16).

Pizzolato M, Boutelier T, Fick R, Deriche R. 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. (D.O.I.: https://doi.org/10.1109/ISBI.2016.7493302).

Fick RHJ, Pizzolato M, Wassermann D, Zucchelli M, Menegaz G, Deriche R. 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. (D.O.I.: https://doi.org/10.1109/ISBI.2016.7493491).

Pizzolato M, Wassermann D, Duval T, Campbell JS, Boutelier T, Cohen-Adad J, Deriche R. 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 (D.O.I.: https://doi.org/10.1007/978-3-319-28588-7_16).

Pizzolato M, Wassermann D, Boutelier T, Deriche R. 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 (D.O.I: https://doi.org/10.1007/978-3-319-24553-9_14).

Fick RHJ, Wassermann D, Pizzolato M, Deriche R. 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 (D.O.I.: https://doi.org/10.1007/978-3-319-19992-4_13).

Pizzolato M, Ghosh A, Boutelier T, Deriche R. 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. (D.O.I.:  https://doi.org/10.1109/ISBI.2015.7164057).

Pizzolato M, Ghosh A, Boutelier T, Deriche R. Magnitude and Complex Based Diffusion Signal Reconstruction. 2014 Computational Diffusion MRI, pp. 127-140. MICCAI 2014. Mathematics and Visualization. Springer, Cham  (Oral presentation) (D.O.I.: https://doi.org/10.1007/978-3-319-11182-7_12).

Journal articles:

Pizzolato M, Gilbert G, Thiran  JP, Descoteaux  M, Deriche R. Adaptive Phase Correction for Diffusion Weighted Images. NeuroImage  (D.O.I.: https://doi.org/10.1016/j.neuroimage.2019.116274).

Pizzolato M, Boutelier T, Deriche R. Perfusion deconvolution in DSC-MRI with dispersion-compliant bases. Medical Image Analysis, vol. 36, pp. 197-215, 2017. (D.O.I.: https://doi.org/10.1016/j.media.2016.12.001).

Canales-Rodríguez EJ, Legarreta JH, Pizzolato M, Rensonnet G, …, Daducci A. Sparse wars: A survey and comparative study of spherical deconvolution algorithms for diffusion MRI. NeuroImage, vol. 184, pp. 140-160, 2019. (D.O.I.: https://doi.org/10.1016/j.neuroimage.2018.08.071).

Schilling KG, Nath  V, Hansen C, ..., Pizzolato M, …, Landman B. Limits to anatomical accuracy of diffusion tractography using modern approaches. NeuroImage, vol. 185, pp. 1-11, 2019. (D.O.I.: https://doi.org/10.1016/j.neuroimage.2018.10.029).

Book chapter:

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. (D.O.I.: https://doi.org/10.1007/978-3-319-61358-1_9)

Conference abstracts (peer-reviewed):

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 proceedings of ISMRM 27 th 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 Highresolution. 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 datadriven approach. In proceedings of ISMRM 27th Annual Meeting.

Pizzolato, M., Canales Rodríguez, E.J., Daducci, A., Thiran J.P. (2018b). 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). In Alpine Brain Imaging Meeting, Champéry, Switzerland, January 7-11, 2018, Champéry, 2018. Adopting the Brain Imaging Data Structure in the Connectome Mapper.

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. Unveiling the Dispersion Kernel in DSC-MRI by Means of Dispersion-Compliant Bases and Control Point Interpolation Techniques. In proceedings of ISMRM 24th Annual Meeting (oral presentation). (https://hal.inria.fr/hal-01408170/document).

Pizzolato M, Fick R, Boutelier T, Deriche R. 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/document).

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)