![Marco Palombo](https://profiles-cardiff.imgix.net/attachments/1604?w=200&h=200&auto=format&crop=faces&fit=crop&q=90")
Dr Marco Palombo
Senior Lecturer
- PalomboM@caerdydd.ac.uk
- +44 29208 70358
- Canolfan Ymchwil Delweddu'r Ymennydd Prifysgol Caerdydd, Ystafell 1.003, Heol Maendy, Caerdydd, CF24 4HQ
- Ar gael fel goruchwyliwr ôl-raddedig
Trosolwyg
Biography
I am currently UKRI Future Leaders Fellow and Senior Lecturer (Associate Professor) in microstructure imaging at Cardiff University, with a joint appointment between the Cardiff University Brain Research Imaging Centre (CUBRIC) in the School of Psychology and the School of Computer Science and Informatics.
I am also member of the Centre for Artificial Intelligence, Robotics and Human-Machine Systems (IROHMS) at Cardiff University and co-chair of the "Human-centric AI for Medical Imaging" Working Group.
I am a physicist with expertise in biophysical modelling, machine learning, computational modelling, medical imaging, and data analysis.
Research
My research interest is in combining Physics, Computer Science and Neuroscience to develop non-invasive imaging technologies for early diagnosis and prognosis of neurological and psychiatric conditions.
In particular, my research programme combines computational modelling, MRI and modern machine learning to pioneer next generation healthcare technology for in vivo imaging of brain tissue. Towards this goal, I have made a few key innovations in microstructure imaging and non-invasive histology of the brain (with particular focus on grey matter): the first demonstration of non-invasive quantification of complex brain cell morphology, using diffusion-weighted MR spectroscopy and computational modelling (Palombo et al., PNAS 2016); mapping of cell body size and density, using SANDI (Palombo et al. Neuroimage 2020); mapping of water exchange, using NEXI (Jelescu et al. Neuroimage 2022); and first-of-its-kind generative models of complex brain cell morphologies (Palombo et al., Neuroimage 2019) and axonal bundles (Callaghan et al. Neuroimage 2020) for the controlled and flexible generation of ultra-realistic computational models of the brain microstructure, essential for more realistic numerical simulations (e.g. Monte Carlo).
Recent work focuses on combining such computational models, Monte Carlo simulations and machine learning for next generation microstructure imaging, e.g. maps of axonal permeability as new imaging marker of demyelination (Hill et al. Neuroimage 2021); in vivo quantification of glial reactivity (Ligneul et al., Neuroimage 2019, Genovese et al., NMR Biomed 2021); cancer-specific microstructure imaging to assess brain tumor’s response to radio/proton therapy (Buizza et al., Medical Physics 2020).
Cyhoeddiad
2023
- Schiavi, S. et al. 2023. Mapping tissue microstructure across the human brain on a clinical scanner with soma and neurite density image metrics. Human Brain Mapping 44(13), pp. 4792-4811. (10.1002/hbm.26416)
- Reddaway, J., Richardson, P. E., Bevan, R. J., Stoneman, J. and Palombo, M. 2023. Microglial morphometric analysis: so many options, so little consistency. Frontiers in Neuroinformatics 17, article number: 1211188. (10.3389/fninf.2023.1211188)
- Örzsik, B., Palombo, M., Asllani, I., Dijk, D., Harrison, N. A. and Cercignani, M. 2023. Higher order diffusion imaging as a putative index of human sleep-related microstructural changes and glymphatic clearance. NeuroImage 274, article number: 120124. (10.1016/j.neuroimage.2023.120124)
- Morelli, L. et al. 2023. Microstructural parameters from DW-MRI for tumour characterization and local recurrence prediction in particle therapy of skull-base chordoma. Medical Physics 50(5), pp. 2900-2913. (10.1002/mp.16202)
- Figini, M. et al. 2023. Comprehensive brain tumour characterization with VERDICT-MRI: evaluation of cellular and vascular measures validated by histology. Cancers 15(9), article number: 2490. (10.3390/cancers15092490)
- Spindler, M., Palombo, M., Zhang, H. and Thiel, C. M. 2023. Dysfunction of the hypothalamic-pituitary adrenal axis and its influence on aging: the role of the hypothalamus. Scientific Reports 13(1), article number: 6866. (10.1038/s41598-023-33922-5)
- Figini, M. et al. 2023. Comprehensive brain tumour characterisation with VERDICT-MRI: Evaluation of cellular and vascular measures validated by histology. Cancers 15(9), article number: 2490. (10.3390/cancers15092490)
- Warner, W. et al. 2023. Temporal Diffusion Ratio (TDR) for imaging restricted diffusion: optimisation and pre-clinical demonstration. NeuroImage 269, article number: 119930. (10.1016/j.neuroimage.2023.119930)
- Lou, J. et al. 2023. Predicting radiologists' gaze with computational saliency models in mammogram reading. IEEE Transactions on Multimedia (10.1109/TMM.2023.3263553)
- Palombo, M. et al. 2023. Joint estimation of relaxation and diffusion tissue parameters for prostate cancer with relaxation-VERDICT MRI. Scientific Reports 13(1), article number: 2999. (10.1038/s41598-023-30182-1)
- Margoni, M. et al. 2023. In vivo quantification of brain soma and neurite density abnormalities in multiple sclerosis. Journal of Neurology 270(1), pp. 433–445. (10.1007/s00415-022-11386-3)
2022
- Schilling, K. G., Palombo, M., O'Grady, K. P., Combes, A. J., Anderson, A. W., Landman, B. A. and Smith, S. A. 2022. Minimal number of sampling directions for robust measures of the spherical mean diffusion weighted signal: Effects of sampling directions, b-value, signal-to-noise ratio, hardware, and fitting strategy. Magnetic Resonance Imaging 94, pp. 25-35. (10.1016/j.mri.2022.07.015)
- Lim, J. P., Blumberg, S. B., Narayan, N., Epstein, S. C., Alexander, D. C., Palombo, M. and Slator, P. J. 2022. Fitting a directional microstructure model to diffusion-relaxation mri data with self-supervised machine learning. Lecture Notes in Computer Science 13722, pp. 77-88. (10.1007/978-3-031-21206-2_7)
- Jelescu, I. O., Skowronski, A. d., Geffroy, F., Palombo, M. and Novikov, D. S. 2022. Neurite Exchange Imaging (NEXI): A minimal model of diffusion in gray matter with inter-compartment water exchange. NeuroImage 256, article number: 119277.
- Ianus, A., Carvalho, J., Fernandes, F. F., Cruz, R., Chavarrias, C., Palombo, M. and Shemesh, N. 2022. Soma and Neurite Density MRI (SANDI) of the in-vivo mouse brain and comparison with the Allen Brain Atlas. NeuroImage 254, article number: 119135. (10.1016/j.neuroimage.2022.119135)
- Palombo, M., Barbetta, A., Cametti, C., Favero, G. and Capuani, S. 2022. Transient anomalous diffusion MRI measurement discriminates porous polymeric matrices characterized by different sub-microstructures and fractal dimension. Gels 8(2), article number: 95. (10.3390/gels8020095)
- Gyori, N., Palombo, M., Clark, C., Zhang, H. and Alexander, D. 2022. Training data distribution significantly impacts the estimation of tissue microstructure with machine learning. Magnetic Resonance in Medicine 87(2), pp. 932-947. (10.1002/mrm.29014)
2021
- Slator, P. et al. 2021. Combined diffusion-relaxometry microstructure imaging: Current status and future prospects. Magnetic Resonance in Medicine 86(6), pp. 2987-3011. (10.1002/mrm.28963)
- Kerkelaa, L. et al. 2021. Comparative analysis of signal models for microscopic fractional anisotropy estimation using q-space trajectory encoding. NeuroImage 242, article number: 118445. (10.1016/j.neuroimage.2021.118445)
- Ianus, A., Alexander, D., Zhang, H. and Palombo, M. 2021. Mapping complex cell morphology in the grey matter with double diffusion encoding MR: A simulation study. NeuroImage 241, article number: 118424. (10.1016/j.neuroimage.2021.118424)
- De Luca, A. et al. 2021. On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge. NeuroImage 240, article number: 118367. (10.1016/j.neuroimage.2021.118367)
- Grussu, F., Battiston, M., Palombo, M., Schneider, T., Wheeler-Kingshott, C. A. and Alexander, D. C. 2021. Deep learning model fitting for diffusion-relaxometry: a comparative study. In: Gyori, N. et al. eds. Computational Diffusion MRI. Mathematics and Visualization. Mathematics and Visualization Cham: Springer, pp. 159-172., (10.1007/978-3-030-73018-5_13)
- Afzali, M., Nilsson, M., Palombo, M. and Jones, D. K. 2021. SPHERIOUSLY? The challenges of estimating sphere radius non-invasively in the human brain from diffusion MRI. NeuroImage 237, article number: 118183. (10.1016/j.neuroimage.2021.118183)
- Slator, P. et al. 2021. Data-driven multi-contrast spectral microstructure imaging with InSpect: INtegrated SPECTral component estimation and mapping. Medical Image Analysis 71, article number: 102045. (10.1016/j.media.2021.102045)
- Palombo, M. et al. 2021. Joint estimation of relaxation and diffusion tissue parameters for prostate cancer grading with relaxation-VERDICT MRI. [Online]. medRxiv: Cold Spring Harbor Laboratory. (10.1101/2021.06.24.21259440) Available at: https://doi.org/10.1101/2021.06.24.21259440
- Perot, J., Celestine, M., Palombo, M., Dhenain, M., Humbert, S., Brouillet, E. and Flament, J. 2021. Identification of the key role of white matter alteration in the pathogenesis of Huntington’s Disease. [Online]. bioRxiv: Cold Spring Harbor Laboratory. (10.1101/2021.06.21.449242) Available at: https://doi.org/10.1101/2021.06.21.449242
- Valindria, V., Palombo, M., Chiou, E., Singh, S., Punwani, S. and Panagiotaki, E. 2021. Synthetic Q-Space learning with deep regression networks for prostate cancer characterisation with VERDICT. Presented at: 2021 IEEE 18th International Symposium on Biomedical Imaging, 13-16 April 20212021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, (10.1109/ISBI48211.2021.9434096)
- Genovese, G. et al. 2021. Inflammation-driven glial alterations in the cuprizone mouse model probed with diffusion-weighted magnetic resonance spectroscopy at 11.7 T. NMR in Biomedicine 34(4), article number: e4480. (10.1002/nbm.4480)
- Buizza, G. et al. 2021. Improving the characterization of meningioma microstructure in proton therapy from conventional apparent diffusion coefficient measurements using Monte Carlo simulations of diffusion MRI. Medical Physics 48(3), pp. 1250-1261. (10.1002/mp.14689)
- Callaghan, R., Alexander, D., Palombo, M. and Zhang, H. 2021. Impact of within-voxel heterogeneity in fibre geometry on spherical deconvolution. [Online]. arXiv: Cornell University. (10.48550/arXiv.2103.08237) Available at: https://doi.org/10.48550/arXiv.2103.08237
- Martins, J. P. d. A., Nilsson, M., Lampinen, B., Palombo, M., While, P. T., Westin, C. and Szczepankiewicz, F. 2021. Neural networks for parameter estimation in microstructural MRI: a study with a high-dimensional diffusion-relaxation model of white matter microstructure. [Online]. bioRxiv: Cold Spring Harbor Laboratory. (10.1101/2021.03.12.435163) Available at: https://doi.org/10.1101/2021.03.12.435163
- Palombo, M., Ianus, A., Guerreri, M., Nunes, D., Alexander, D., Shemesh, N. and Zhang, H. 2021. Corrigendum to “SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI” [Neuroimage 215 (2020), 116835]. NeuroImage 226, article number: 117612. (10.1016/j.neuroimage.2020.117612)
- Henriques, R. N., Palombo, M., Jespersen, S. N., Shemesh, N., Lundell, H. and Ianuş, A. 2021. Double diffusion encoding and applications for biomedical imaging. Journal of Neuroscience Methods 348, article number: 108989. (10.1016/j.jneumeth.2020.108989)
- Hill, I. et al. 2021. Machine learning based white matter models with permeability: An experimental study in cuprizone treated in-vivo mouse model of axonal demyelination.. NeuroImage 224, article number: 117425. (10.1016/j.neuroimage.2020.117425)
2020
- Ning, L. et al. 2020. Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: algorithms and result. NeuroImage 221, article number: 117128. (10.1016/j.neuroimage.2020.117128)
- Pizzolato, M. et al. 2020. Acquiring and predicting multidimensional diffusion (MUDI) data: an open challenge. Presented at: MICCAI Workshop, Shenzhen, China, Oct 2019 Presented at Bonet-Carne, E. et al. eds.Computational Diffusion MRI. Mathematics and Visualization Springer pp. 195-208., (10.1007/978-3-030-52893-5_17)
- Callaghan, R., Alexander, D. C., Palombo, M. and Zhang, H. 2020. ConFiG: Contextual Fibre Growth to generate realistic axonal packing for diffusion MRI simulation. NeuroImage 220, article number: 117107. (10.1016/j.neuroimage.2020.117107)
- Jelescu, I. O., Palombo, M., Bagnato, F. and Schilling, K. G. 2020. Challenges for biophysical modeling of microstructure. Journal of Neuroscience Methods 344, article number: 108861. (10.1016/j.jneumeth.2020.108861)
- Slator, P. J. et al. 2020. Data-driven multi-contrast spectral microstructure imaging with InSpect. Presented at: MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, Peru, 4–8 October, 2020 Presented at Martel, A. et al. eds.Medical Image Computing and Computer Assisted Intervention – MICCAI 2020., Vol. 12266. Cham: Springer pp. 375-385., (10.1007/978-3-030-59725-2_36)
- Warner, R. W., Palombo, M., Dell'Acqua, F. and Drobnjak, I. 2020. Optimisation of Temporal Diffusion Ratio (TDR) to maximise its potential to map large axons: Insight from simulations. Presented at: ISMRM and SMRT Virtual Conference and Exhibition, 8-14 August 2020.
- Palombo, M., Ianus, A., Guerreri, M., Nunes, D., Alexander, D. C., Shemesh, N. and Zhang, H. 2020. SANDI: a compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI.. NeuroImage 215, article number: 116835. (10.1016/j.neuroimage.2020.116835)
- Vincent, M., Palombo, M. and Valette, J. 2020. Revisiting double diffusion encoding MRS in the mouse brain at 11.7T: Which microstructural features are we sensitive to?. NeuroImage 207, article number: 116399. (10.1016/j.neuroimage.2019.116399)
- Capuani, S. and Palombo, M. 2020. Mini review on anomalous diffusion by MRI: Potential advantages, pitfalls, limitations, nomenclature, and correct interpretation of literature. Frontiers in Physics 7, article number: 248. (10.3389/fphy.2019.00248)
- Guerreri, M., Szczepankiewicz, F., Lampinen, B., Palombo, M., Nilsson, M. and Zhang, H. 2020. Tortuosity assumption not the cause of NODDI’s incompatibility with tensor-valued diffusion encoding. Presented at: ISMRM and SMRT Virtual Conference and Exhibition, 8-14 August 2020.
- Palombo, M. and Singh, S. 2020. Relaxed-VERDICT: decoupling relaxation and diffusion for comprehensive microstructure characterization of prostate cancer.. Presented at: ISMRM & SMRT Virtual Conference & Exhibition 2020, Online, 8-14 August 2020.
2019
- Slator, P. et al. 2019. Combined diffusion-relaxometry MRI to identify dysfunction in the human placenta. Magnetic Resonance in Medicine 82(1), pp. 95-106. (10.1002/mrm.27733)
- Callaghan, R., Alexander, D. C., Zhang, H. and Palombo, M. 2019. Contextual fibre growth to generate realistic axonal packing for diffusion MRI simulation. Presented at: IPMI: 26th International Conference on Information Processing in Medical Imaging, Hong Kong, China, 2-7-June 2019 Presented at Chung, A. et al. eds.Information Processing in Medical Imaging Proceedings, Vol. 11492. Lecture Notes in Computer Science Springer pp. 429-440., (10.1007/978-3-030-20351-1_33)
- Slator, P. J. et al. 2019. InSpect: INtegrated SPECTral component estimation and mapping for multi-contrast microstructural MRI. Presented at: IPMI 2019: International Conference on Information Processing in Medical Imaging, 2-7 June 2019 Presented at Chung, A. C. S. et al. eds.Information Processing in Medical Imaging: 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings. Springer pp. 755-766., (10.1007/978-3-030-20351-1_59)
- Callaghan, R., Shemesh, N., Alexander, D., Zhang, H. and Palombo, M. 2019. Towards a more realistic and flexible white matter numerical phantom generator for diffusion MRI simulation. Presented at: ISMRM 27th Annual Meeting and Exhibition, 11-16 May 2019.
- Ianus, A., Callaghan, R., Alexander, D. and Palombo, M. 2019. Effect of cell complexity and size on diffusion MRI signal: a simulation study. Presented at: ISMRM 27th Annual Meeting and Exhibition, 11-16 May 2019.
- Ning, L. et al. 2019. Muti-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC): Progress and results. Presented at: MICCAI 2018, Granada, Spain, 16-20 September 2018 Presented at Bonet-Carne, E. et al. eds.Computational Diffusion MRI, Vol. 1. Mathematics and Visualization Cham: Springer pp. 217-224., (10.1007/978-3-030-05831-9_18)
- Ligneu, C. et al. 2019. Diffusion-weighted magnetic resonance spectroscopy enables cell-specific monitoring of astrocyte reactivity in vivo. NeuroImage 191, pp. 457-469. (10.1016/j.neuroimage.2019.02.046)
- Slator, P. et al. 2019. Placenta Imaging Workshop 2018 report: Multiscale and multimodal approaches. Placenta 79, pp. 78-82. (10.1016/j.placenta.2018.10.010)
- Guerreri, M., Palombo, M., Caporale, A., Fasano, F., Macaluso, E., Bozzali, M. and Capuani, S. 2019. Age-related microstructural and physiological changes in normal brain measured by MRI γ-metrics derived from anomalous diffusion signal representation. NeuroImage 188, pp. 654-667. (10.1016/j.neuroimage.2018.12.044)
- Palombo, M., Alexander, D. C. and Zhang, H. 2019. A generative model of realistic brain cells with application to numerical simulation of the diffusion-weighted MR signal. NeuroImage 188, pp. 391-402. (10.1016/j.neuroimage.2018.12.025)
- Blumberg, S. B., Palombo, M., Khoo, C. S., Tax, C. M. W., Tanno, R. and Alexander, D. C. 2019. Multi-stage prediction networks for data harmonization. Presented at: Medical Image Computing and Computer Assisted Intervention – MICCAI, Shenzhen, China, 13-17 Oct 2019Medical Image Computing and Computer Assisted Intervention – MICCAI Proceedings, Vol. 11767. Lecture Notes in Computer Science Springer pp. 411-419., (10.1007/978-3-030-32251-9_45)
- Palombo, M. et al. 2019. Improving strain diagnosis of prion disease by diffusion MRI and biophysical modelling. Presented at: 27th ISMRM Annual Meeting and Exhibition, 11-16 May 2019.
- Palombo, M., Nunes, D., Alexander, D. C., Zhang, H. and Shemesh, N. 2019. Histological validation of the brain cell body imaging with diffusion MRI at ultrahigh field. Presented at: ISMRM 27th Annual Meeting and Exhibition, 11-16 May 2019 Presented at Port, J. D. and Noll, D. C. eds.Proceedings of the 27th ISMRM Annual Meeting and Exhibition. ISMRM (International Society for Magnetic Resonance in Medicine).
2018
- Jones, D. K. et al. 2018. Microstructural imaging of the human brain with a ‘super-scanner’: 10 key advantages of ultra-strong gradients for diffusion MRI. NeuroImage 182, pp. 8-38. (10.1016/j.neuroimage.2018.05.047)
- Palombo, M., Ligneul, C., Hernandez-Garzon, E. and Valette, J. 2018. Can we detect the effect of spines and leaflets on the diffusion of brain intracellular metabolites?. NeuroImage 182, pp. 283-293. (10.1016/j.neuroimage.2017.05.003)
- Palombo, M., Shemesh, N., Ronen, I. and Valette, J. 2018. Insights into brain microstructure from in vivo DW-MRS. NeuroImage 182, pp. 97-116. (10.1016/j.neuroimage.2017.11.028)
- Guerreri, M., Szczepankiewicz, F., Lampinen, B., Nilsson, M., Palombo, M., Capuani, S. and Zhang, G. H. 2018. Revised NODDI model for diffusion MRI data with multiple b-tensor encodings. Presented at: Joint Annual Meeting ISMRM-ESMRMB 2018, 16-21 June 2018.
- Hill, I. et al. 2018. Deep neural network based framework for in-vivo axonal permeability estimation. Presented at: Joint Annual Meeting ISMRM-ESMRMB 2018, 16-21 June 2018Proceedings of the Joint Annual Meeting ISMRM-ESMRMB 2018. ISMRM (International Society for Magnetic Resonance in Medicine).
- Palombo, M. et al. 2018. Machine learning based estimation of axonal permeability: validation on cuprizone treated in-vivo mouse model of axonal demyelination. Presented at: Joint Annual Meeting ISMRM-ESMRMB 2018, 16-21 June 2018 Presented at Miller, K. L. and Port, J. D. eds.
- Palombo, M., Shemesh, N., Ianus, A., Alexander, D. and Zhang, H. 2018. Abundance of cell bodies can explain the stick model’s failure in grey matter at high bvalue. Presented at: Joint Annual Meeting ISMRM-ESMRMB 2018, 16-21 June 2018.
- Sinibaldi, R. et al. 2018. Multimodal-3D imaging based on MRI and CT techniques bridges the gap with histology in visualization of the bone regeneration process. Journal of Tissue Engineering and Regenerative Medicine 12(3), pp. 750-761. (10.1002/term.2494)
- Valette, J., Ligneul, C., Marchadour, C., Najac, C. and Palombo, M. 2018. Brain metabolite diffusion from ultra-short to ultra-long time scales: What do we learn, where should we go?. Frontiers in Neuroscience 12, article number: 2. (10.3389/fnins.2018.00002)
2017
- Conti, A., Palombo, M., Parmentier, A., Poggi, G., Baglioni, P. and De Luca, F. 2017. Two-phase water model in the cellulose network of paper. Cellulose 24(8), pp. 3479-3487. (10.1007/s10570-017-1338-2)
- Ligneul, C., Palombo, M. and Valette, J. 2017. Metabolite diffusion up to very high b in the mouse brain In Vivo: Revisiting the potential correlation between relaxation and diffusion properties. Magnetic Resonance in Medicine 77(4), pp. 1390-1398. (10.1002/mrm.26217)
- Caporale, A., Palombo, M., Macaluso, E., Guerreri, M., Bozzali, M. and Capuani, S. 2017. The gamma-parameter of anomalous diffusion quantified in human brain by MRI depends on local magnetic susceptibility differences. NeuroImage 147, pp. 619-631. (10.1016/j.neuroimage.2016.12.051)
- Zhang, D., Zhu, X., Bifone, A., Gozzi, A., Capuani, S. and Palombo, M. 2017. Efficient parametric imaging with GPU computing. Biophysical Journal 112(3), pp. 583A-584A. (10.1016/j.bpj.2016.11.3141)
- Palombo, M., Ligneul, C. and Valette, J. 2017. Modeling diffusion of intracellular metabolites in the mouse brain up to very high diffusion-weighting: Diffusion in long fibers (almost) accounts for non-monoexponential attenuation. Magnetic Resonance in Medicine 77(1), pp. 343-350. (10.1002/mrm.26548)
2016
- Santi, G. D., La Greca, C., Bruno, A., Palombo, M., Bronco, I. and Palombo, P. 2016. The use of dermal regeneration template (Matriderm (R) 1 mm) for reconstruction of a large full-thickness scalp and calvaria exposure. Journal of Burn Care and Research 37(5), pp. E497-E498. (10.1097/BCR.0000000000000395)
- Palombo, M. et al. 2016. New paradigm to assess brain cell morphology by diffusion-weighted MR spectroscopy in vivo. Proceedings of the National Academy of Sciences 113(24), pp. 6671-6676. (10.1073/pnas.1504327113)
2015
- Palombo, M., Gentili, S., Bozzali, M., Macaluso, E. and Capuani, S. 2015. New insight into the contrast in diffusional kurtosis images: does it depend on magnetic susceptibility?. Magnetic Resonance in Medicine 73(5), pp. 2015-2024. (10.1002/mrm.25308)
2014
- Di Pietro, G., Palombo, M. and Capuani, S. 2014. Internal magnetic field gradients in heterogeneous porous systems: comparison between spin-echo and diffusion decay internal field (DDIF) method. Applied Magnetic Resonance 45(8), pp. 771-784. (10.1007/s00723-014-0556-0)
2013
- Palombo, M., Gabrielli, A., Servedio, V. D. P., Ruocco, G. and Capuani, S. 2013. Structural disorder and anomalous diffusion in random packing of spheres. Scientific Reports 3, article number: 2631. (10.1038/srep02631)
- GadElkarim, J. J., Magin, R. L., Meerschaert, M. M., Capuani, S., Palombo, M., Kumar, A. and Leow, A. D. 2013. Fractional order generalization of anomalous diffusion as a multidimensional extension of the transmission line equation. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 3(3), pp. 432-441. (10.1109/JETCAS.2013.2265795)
- Di Pietro, G. et al. 2013. Assessment of muscle microstructures in osteoporotic and osteoarthritic subjects by using magnetic resonance diffusion tensor imaging. Presented at: European Congress on Osteoporosis and Osteoarthritis (ESCEO13-IOF), 2013, Vol. 24. Vol. Supp 1. Springer pp. S293-S293., (10.1007/s00198-013-2312-y)
- Capuani, S., Palombo, M., Gabrielli, A., Orlandi, A., Maraviglia, B. and Pastore, F. S. 2013. Spatio-temporal anomalous diffusion imaging: results in controlled phantoms and in excised human meningiomas. Magnetic Resonance Imaging 31(3), pp. 359-365. (10.1016/j.mri.2012.08.012)
2012
- Palombo, M., Gabrielli, A., De Santis, S. and Capuani, S. 2012. The γ parameter of the stretched-exponential model is influenced by internal gradients: Validation in phantoms. Journal of Magnetic Resonance 216, pp. 28-36. (10.1016/j.jmr.2011.12.023)
2011
- De Santis, S., Gabrielli, A., Palombo, M., Maraviglia, B. and Capuani, S. 2011. Non-Gaussian diffusion imaging: a brief practical review. Magnetic Resonance Imaging 29(10), pp. 1410-1416. (10.1016/j.mri.2011.04.006)
Articles
- Schiavi, S. et al. 2023. Mapping tissue microstructure across the human brain on a clinical scanner with soma and neurite density image metrics. Human Brain Mapping 44(13), pp. 4792-4811. (10.1002/hbm.26416)
- Reddaway, J., Richardson, P. E., Bevan, R. J., Stoneman, J. and Palombo, M. 2023. Microglial morphometric analysis: so many options, so little consistency. Frontiers in Neuroinformatics 17, article number: 1211188. (10.3389/fninf.2023.1211188)
- Örzsik, B., Palombo, M., Asllani, I., Dijk, D., Harrison, N. A. and Cercignani, M. 2023. Higher order diffusion imaging as a putative index of human sleep-related microstructural changes and glymphatic clearance. NeuroImage 274, article number: 120124. (10.1016/j.neuroimage.2023.120124)
- Morelli, L. et al. 2023. Microstructural parameters from DW-MRI for tumour characterization and local recurrence prediction in particle therapy of skull-base chordoma. Medical Physics 50(5), pp. 2900-2913. (10.1002/mp.16202)
- Figini, M. et al. 2023. Comprehensive brain tumour characterization with VERDICT-MRI: evaluation of cellular and vascular measures validated by histology. Cancers 15(9), article number: 2490. (10.3390/cancers15092490)
- Spindler, M., Palombo, M., Zhang, H. and Thiel, C. M. 2023. Dysfunction of the hypothalamic-pituitary adrenal axis and its influence on aging: the role of the hypothalamus. Scientific Reports 13(1), article number: 6866. (10.1038/s41598-023-33922-5)
- Figini, M. et al. 2023. Comprehensive brain tumour characterisation with VERDICT-MRI: Evaluation of cellular and vascular measures validated by histology. Cancers 15(9), article number: 2490. (10.3390/cancers15092490)
- Warner, W. et al. 2023. Temporal Diffusion Ratio (TDR) for imaging restricted diffusion: optimisation and pre-clinical demonstration. NeuroImage 269, article number: 119930. (10.1016/j.neuroimage.2023.119930)
- Lou, J. et al. 2023. Predicting radiologists' gaze with computational saliency models in mammogram reading. IEEE Transactions on Multimedia (10.1109/TMM.2023.3263553)
- Palombo, M. et al. 2023. Joint estimation of relaxation and diffusion tissue parameters for prostate cancer with relaxation-VERDICT MRI. Scientific Reports 13(1), article number: 2999. (10.1038/s41598-023-30182-1)
- Margoni, M. et al. 2023. In vivo quantification of brain soma and neurite density abnormalities in multiple sclerosis. Journal of Neurology 270(1), pp. 433–445. (10.1007/s00415-022-11386-3)
- Schilling, K. G., Palombo, M., O'Grady, K. P., Combes, A. J., Anderson, A. W., Landman, B. A. and Smith, S. A. 2022. Minimal number of sampling directions for robust measures of the spherical mean diffusion weighted signal: Effects of sampling directions, b-value, signal-to-noise ratio, hardware, and fitting strategy. Magnetic Resonance Imaging 94, pp. 25-35. (10.1016/j.mri.2022.07.015)
- Lim, J. P., Blumberg, S. B., Narayan, N., Epstein, S. C., Alexander, D. C., Palombo, M. and Slator, P. J. 2022. Fitting a directional microstructure model to diffusion-relaxation mri data with self-supervised machine learning. Lecture Notes in Computer Science 13722, pp. 77-88. (10.1007/978-3-031-21206-2_7)
- Jelescu, I. O., Skowronski, A. d., Geffroy, F., Palombo, M. and Novikov, D. S. 2022. Neurite Exchange Imaging (NEXI): A minimal model of diffusion in gray matter with inter-compartment water exchange. NeuroImage 256, article number: 119277.
- Ianus, A., Carvalho, J., Fernandes, F. F., Cruz, R., Chavarrias, C., Palombo, M. and Shemesh, N. 2022. Soma and Neurite Density MRI (SANDI) of the in-vivo mouse brain and comparison with the Allen Brain Atlas. NeuroImage 254, article number: 119135. (10.1016/j.neuroimage.2022.119135)
- Palombo, M., Barbetta, A., Cametti, C., Favero, G. and Capuani, S. 2022. Transient anomalous diffusion MRI measurement discriminates porous polymeric matrices characterized by different sub-microstructures and fractal dimension. Gels 8(2), article number: 95. (10.3390/gels8020095)
- Gyori, N., Palombo, M., Clark, C., Zhang, H. and Alexander, D. 2022. Training data distribution significantly impacts the estimation of tissue microstructure with machine learning. Magnetic Resonance in Medicine 87(2), pp. 932-947. (10.1002/mrm.29014)
- Slator, P. et al. 2021. Combined diffusion-relaxometry microstructure imaging: Current status and future prospects. Magnetic Resonance in Medicine 86(6), pp. 2987-3011. (10.1002/mrm.28963)
- Kerkelaa, L. et al. 2021. Comparative analysis of signal models for microscopic fractional anisotropy estimation using q-space trajectory encoding. NeuroImage 242, article number: 118445. (10.1016/j.neuroimage.2021.118445)
- Ianus, A., Alexander, D., Zhang, H. and Palombo, M. 2021. Mapping complex cell morphology in the grey matter with double diffusion encoding MR: A simulation study. NeuroImage 241, article number: 118424. (10.1016/j.neuroimage.2021.118424)
- De Luca, A. et al. 2021. On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge. NeuroImage 240, article number: 118367. (10.1016/j.neuroimage.2021.118367)
- Afzali, M., Nilsson, M., Palombo, M. and Jones, D. K. 2021. SPHERIOUSLY? The challenges of estimating sphere radius non-invasively in the human brain from diffusion MRI. NeuroImage 237, article number: 118183. (10.1016/j.neuroimage.2021.118183)
- Slator, P. et al. 2021. Data-driven multi-contrast spectral microstructure imaging with InSpect: INtegrated SPECTral component estimation and mapping. Medical Image Analysis 71, article number: 102045. (10.1016/j.media.2021.102045)
- Genovese, G. et al. 2021. Inflammation-driven glial alterations in the cuprizone mouse model probed with diffusion-weighted magnetic resonance spectroscopy at 11.7 T. NMR in Biomedicine 34(4), article number: e4480. (10.1002/nbm.4480)
- Buizza, G. et al. 2021. Improving the characterization of meningioma microstructure in proton therapy from conventional apparent diffusion coefficient measurements using Monte Carlo simulations of diffusion MRI. Medical Physics 48(3), pp. 1250-1261. (10.1002/mp.14689)
- Palombo, M., Ianus, A., Guerreri, M., Nunes, D., Alexander, D., Shemesh, N. and Zhang, H. 2021. Corrigendum to “SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI” [Neuroimage 215 (2020), 116835]. NeuroImage 226, article number: 117612. (10.1016/j.neuroimage.2020.117612)
- Henriques, R. N., Palombo, M., Jespersen, S. N., Shemesh, N., Lundell, H. and Ianuş, A. 2021. Double diffusion encoding and applications for biomedical imaging. Journal of Neuroscience Methods 348, article number: 108989. (10.1016/j.jneumeth.2020.108989)
- Hill, I. et al. 2021. Machine learning based white matter models with permeability: An experimental study in cuprizone treated in-vivo mouse model of axonal demyelination.. NeuroImage 224, article number: 117425. (10.1016/j.neuroimage.2020.117425)
- Ning, L. et al. 2020. Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: algorithms and result. NeuroImage 221, article number: 117128. (10.1016/j.neuroimage.2020.117128)
- Callaghan, R., Alexander, D. C., Palombo, M. and Zhang, H. 2020. ConFiG: Contextual Fibre Growth to generate realistic axonal packing for diffusion MRI simulation. NeuroImage 220, article number: 117107. (10.1016/j.neuroimage.2020.117107)
- Jelescu, I. O., Palombo, M., Bagnato, F. and Schilling, K. G. 2020. Challenges for biophysical modeling of microstructure. Journal of Neuroscience Methods 344, article number: 108861. (10.1016/j.jneumeth.2020.108861)
- Palombo, M., Ianus, A., Guerreri, M., Nunes, D., Alexander, D. C., Shemesh, N. and Zhang, H. 2020. SANDI: a compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI.. NeuroImage 215, article number: 116835. (10.1016/j.neuroimage.2020.116835)
- Vincent, M., Palombo, M. and Valette, J. 2020. Revisiting double diffusion encoding MRS in the mouse brain at 11.7T: Which microstructural features are we sensitive to?. NeuroImage 207, article number: 116399. (10.1016/j.neuroimage.2019.116399)
- Capuani, S. and Palombo, M. 2020. Mini review on anomalous diffusion by MRI: Potential advantages, pitfalls, limitations, nomenclature, and correct interpretation of literature. Frontiers in Physics 7, article number: 248. (10.3389/fphy.2019.00248)
- Slator, P. et al. 2019. Combined diffusion-relaxometry MRI to identify dysfunction in the human placenta. Magnetic Resonance in Medicine 82(1), pp. 95-106. (10.1002/mrm.27733)
- Ligneu, C. et al. 2019. Diffusion-weighted magnetic resonance spectroscopy enables cell-specific monitoring of astrocyte reactivity in vivo. NeuroImage 191, pp. 457-469. (10.1016/j.neuroimage.2019.02.046)
- Slator, P. et al. 2019. Placenta Imaging Workshop 2018 report: Multiscale and multimodal approaches. Placenta 79, pp. 78-82. (10.1016/j.placenta.2018.10.010)
- Guerreri, M., Palombo, M., Caporale, A., Fasano, F., Macaluso, E., Bozzali, M. and Capuani, S. 2019. Age-related microstructural and physiological changes in normal brain measured by MRI γ-metrics derived from anomalous diffusion signal representation. NeuroImage 188, pp. 654-667. (10.1016/j.neuroimage.2018.12.044)
- Palombo, M., Alexander, D. C. and Zhang, H. 2019. A generative model of realistic brain cells with application to numerical simulation of the diffusion-weighted MR signal. NeuroImage 188, pp. 391-402. (10.1016/j.neuroimage.2018.12.025)
- Jones, D. K. et al. 2018. Microstructural imaging of the human brain with a ‘super-scanner’: 10 key advantages of ultra-strong gradients for diffusion MRI. NeuroImage 182, pp. 8-38. (10.1016/j.neuroimage.2018.05.047)
- Palombo, M., Ligneul, C., Hernandez-Garzon, E. and Valette, J. 2018. Can we detect the effect of spines and leaflets on the diffusion of brain intracellular metabolites?. NeuroImage 182, pp. 283-293. (10.1016/j.neuroimage.2017.05.003)
- Palombo, M., Shemesh, N., Ronen, I. and Valette, J. 2018. Insights into brain microstructure from in vivo DW-MRS. NeuroImage 182, pp. 97-116. (10.1016/j.neuroimage.2017.11.028)
- Sinibaldi, R. et al. 2018. Multimodal-3D imaging based on MRI and CT techniques bridges the gap with histology in visualization of the bone regeneration process. Journal of Tissue Engineering and Regenerative Medicine 12(3), pp. 750-761. (10.1002/term.2494)
- Valette, J., Ligneul, C., Marchadour, C., Najac, C. and Palombo, M. 2018. Brain metabolite diffusion from ultra-short to ultra-long time scales: What do we learn, where should we go?. Frontiers in Neuroscience 12, article number: 2. (10.3389/fnins.2018.00002)
- Conti, A., Palombo, M., Parmentier, A., Poggi, G., Baglioni, P. and De Luca, F. 2017. Two-phase water model in the cellulose network of paper. Cellulose 24(8), pp. 3479-3487. (10.1007/s10570-017-1338-2)
- Ligneul, C., Palombo, M. and Valette, J. 2017. Metabolite diffusion up to very high b in the mouse brain In Vivo: Revisiting the potential correlation between relaxation and diffusion properties. Magnetic Resonance in Medicine 77(4), pp. 1390-1398. (10.1002/mrm.26217)
- Caporale, A., Palombo, M., Macaluso, E., Guerreri, M., Bozzali, M. and Capuani, S. 2017. The gamma-parameter of anomalous diffusion quantified in human brain by MRI depends on local magnetic susceptibility differences. NeuroImage 147, pp. 619-631. (10.1016/j.neuroimage.2016.12.051)
- Zhang, D., Zhu, X., Bifone, A., Gozzi, A., Capuani, S. and Palombo, M. 2017. Efficient parametric imaging with GPU computing. Biophysical Journal 112(3), pp. 583A-584A. (10.1016/j.bpj.2016.11.3141)
- Palombo, M., Ligneul, C. and Valette, J. 2017. Modeling diffusion of intracellular metabolites in the mouse brain up to very high diffusion-weighting: Diffusion in long fibers (almost) accounts for non-monoexponential attenuation. Magnetic Resonance in Medicine 77(1), pp. 343-350. (10.1002/mrm.26548)
- Santi, G. D., La Greca, C., Bruno, A., Palombo, M., Bronco, I. and Palombo, P. 2016. The use of dermal regeneration template (Matriderm (R) 1 mm) for reconstruction of a large full-thickness scalp and calvaria exposure. Journal of Burn Care and Research 37(5), pp. E497-E498. (10.1097/BCR.0000000000000395)
- Palombo, M. et al. 2016. New paradigm to assess brain cell morphology by diffusion-weighted MR spectroscopy in vivo. Proceedings of the National Academy of Sciences 113(24), pp. 6671-6676. (10.1073/pnas.1504327113)
- Palombo, M., Gentili, S., Bozzali, M., Macaluso, E. and Capuani, S. 2015. New insight into the contrast in diffusional kurtosis images: does it depend on magnetic susceptibility?. Magnetic Resonance in Medicine 73(5), pp. 2015-2024. (10.1002/mrm.25308)
- Di Pietro, G., Palombo, M. and Capuani, S. 2014. Internal magnetic field gradients in heterogeneous porous systems: comparison between spin-echo and diffusion decay internal field (DDIF) method. Applied Magnetic Resonance 45(8), pp. 771-784. (10.1007/s00723-014-0556-0)
- Palombo, M., Gabrielli, A., Servedio, V. D. P., Ruocco, G. and Capuani, S. 2013. Structural disorder and anomalous diffusion in random packing of spheres. Scientific Reports 3, article number: 2631. (10.1038/srep02631)
- GadElkarim, J. J., Magin, R. L., Meerschaert, M. M., Capuani, S., Palombo, M., Kumar, A. and Leow, A. D. 2013. Fractional order generalization of anomalous diffusion as a multidimensional extension of the transmission line equation. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 3(3), pp. 432-441. (10.1109/JETCAS.2013.2265795)
- Capuani, S., Palombo, M., Gabrielli, A., Orlandi, A., Maraviglia, B. and Pastore, F. S. 2013. Spatio-temporal anomalous diffusion imaging: results in controlled phantoms and in excised human meningiomas. Magnetic Resonance Imaging 31(3), pp. 359-365. (10.1016/j.mri.2012.08.012)
- Palombo, M., Gabrielli, A., De Santis, S. and Capuani, S. 2012. The γ parameter of the stretched-exponential model is influenced by internal gradients: Validation in phantoms. Journal of Magnetic Resonance 216, pp. 28-36. (10.1016/j.jmr.2011.12.023)
- De Santis, S., Gabrielli, A., Palombo, M., Maraviglia, B. and Capuani, S. 2011. Non-Gaussian diffusion imaging: a brief practical review. Magnetic Resonance Imaging 29(10), pp. 1410-1416. (10.1016/j.mri.2011.04.006)
Book sections
- Grussu, F., Battiston, M., Palombo, M., Schneider, T., Wheeler-Kingshott, C. A. and Alexander, D. C. 2021. Deep learning model fitting for diffusion-relaxometry: a comparative study. In: Gyori, N. et al. eds. Computational Diffusion MRI. Mathematics and Visualization. Mathematics and Visualization Cham: Springer, pp. 159-172., (10.1007/978-3-030-73018-5_13)
Conferences
- Valindria, V., Palombo, M., Chiou, E., Singh, S., Punwani, S. and Panagiotaki, E. 2021. Synthetic Q-Space learning with deep regression networks for prostate cancer characterisation with VERDICT. Presented at: 2021 IEEE 18th International Symposium on Biomedical Imaging, 13-16 April 20212021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, (10.1109/ISBI48211.2021.9434096)
- Pizzolato, M. et al. 2020. Acquiring and predicting multidimensional diffusion (MUDI) data: an open challenge. Presented at: MICCAI Workshop, Shenzhen, China, Oct 2019 Presented at Bonet-Carne, E. et al. eds.Computational Diffusion MRI. Mathematics and Visualization Springer pp. 195-208., (10.1007/978-3-030-52893-5_17)
- Slator, P. J. et al. 2020. Data-driven multi-contrast spectral microstructure imaging with InSpect. Presented at: MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, Peru, 4–8 October, 2020 Presented at Martel, A. et al. eds.Medical Image Computing and Computer Assisted Intervention – MICCAI 2020., Vol. 12266. Cham: Springer pp. 375-385., (10.1007/978-3-030-59725-2_36)
- Warner, R. W., Palombo, M., Dell'Acqua, F. and Drobnjak, I. 2020. Optimisation of Temporal Diffusion Ratio (TDR) to maximise its potential to map large axons: Insight from simulations. Presented at: ISMRM and SMRT Virtual Conference and Exhibition, 8-14 August 2020.
- Guerreri, M., Szczepankiewicz, F., Lampinen, B., Palombo, M., Nilsson, M. and Zhang, H. 2020. Tortuosity assumption not the cause of NODDI’s incompatibility with tensor-valued diffusion encoding. Presented at: ISMRM and SMRT Virtual Conference and Exhibition, 8-14 August 2020.
- Palombo, M. and Singh, S. 2020. Relaxed-VERDICT: decoupling relaxation and diffusion for comprehensive microstructure characterization of prostate cancer.. Presented at: ISMRM & SMRT Virtual Conference & Exhibition 2020, Online, 8-14 August 2020.
- Callaghan, R., Alexander, D. C., Zhang, H. and Palombo, M. 2019. Contextual fibre growth to generate realistic axonal packing for diffusion MRI simulation. Presented at: IPMI: 26th International Conference on Information Processing in Medical Imaging, Hong Kong, China, 2-7-June 2019 Presented at Chung, A. et al. eds.Information Processing in Medical Imaging Proceedings, Vol. 11492. Lecture Notes in Computer Science Springer pp. 429-440., (10.1007/978-3-030-20351-1_33)
- Slator, P. J. et al. 2019. InSpect: INtegrated SPECTral component estimation and mapping for multi-contrast microstructural MRI. Presented at: IPMI 2019: International Conference on Information Processing in Medical Imaging, 2-7 June 2019 Presented at Chung, A. C. S. et al. eds.Information Processing in Medical Imaging: 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings. Springer pp. 755-766., (10.1007/978-3-030-20351-1_59)
- Callaghan, R., Shemesh, N., Alexander, D., Zhang, H. and Palombo, M. 2019. Towards a more realistic and flexible white matter numerical phantom generator for diffusion MRI simulation. Presented at: ISMRM 27th Annual Meeting and Exhibition, 11-16 May 2019.
- Ianus, A., Callaghan, R., Alexander, D. and Palombo, M. 2019. Effect of cell complexity and size on diffusion MRI signal: a simulation study. Presented at: ISMRM 27th Annual Meeting and Exhibition, 11-16 May 2019.
- Ning, L. et al. 2019. Muti-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC): Progress and results. Presented at: MICCAI 2018, Granada, Spain, 16-20 September 2018 Presented at Bonet-Carne, E. et al. eds.Computational Diffusion MRI, Vol. 1. Mathematics and Visualization Cham: Springer pp. 217-224., (10.1007/978-3-030-05831-9_18)
- Blumberg, S. B., Palombo, M., Khoo, C. S., Tax, C. M. W., Tanno, R. and Alexander, D. C. 2019. Multi-stage prediction networks for data harmonization. Presented at: Medical Image Computing and Computer Assisted Intervention – MICCAI, Shenzhen, China, 13-17 Oct 2019Medical Image Computing and Computer Assisted Intervention – MICCAI Proceedings, Vol. 11767. Lecture Notes in Computer Science Springer pp. 411-419., (10.1007/978-3-030-32251-9_45)
- Palombo, M. et al. 2019. Improving strain diagnosis of prion disease by diffusion MRI and biophysical modelling. Presented at: 27th ISMRM Annual Meeting and Exhibition, 11-16 May 2019.
- Palombo, M., Nunes, D., Alexander, D. C., Zhang, H. and Shemesh, N. 2019. Histological validation of the brain cell body imaging with diffusion MRI at ultrahigh field. Presented at: ISMRM 27th Annual Meeting and Exhibition, 11-16 May 2019 Presented at Port, J. D. and Noll, D. C. eds.Proceedings of the 27th ISMRM Annual Meeting and Exhibition. ISMRM (International Society for Magnetic Resonance in Medicine).
- Guerreri, M., Szczepankiewicz, F., Lampinen, B., Nilsson, M., Palombo, M., Capuani, S. and Zhang, G. H. 2018. Revised NODDI model for diffusion MRI data with multiple b-tensor encodings. Presented at: Joint Annual Meeting ISMRM-ESMRMB 2018, 16-21 June 2018.
- Hill, I. et al. 2018. Deep neural network based framework for in-vivo axonal permeability estimation. Presented at: Joint Annual Meeting ISMRM-ESMRMB 2018, 16-21 June 2018Proceedings of the Joint Annual Meeting ISMRM-ESMRMB 2018. ISMRM (International Society for Magnetic Resonance in Medicine).
- Palombo, M. et al. 2018. Machine learning based estimation of axonal permeability: validation on cuprizone treated in-vivo mouse model of axonal demyelination. Presented at: Joint Annual Meeting ISMRM-ESMRMB 2018, 16-21 June 2018 Presented at Miller, K. L. and Port, J. D. eds.
- Palombo, M., Shemesh, N., Ianus, A., Alexander, D. and Zhang, H. 2018. Abundance of cell bodies can explain the stick model’s failure in grey matter at high bvalue. Presented at: Joint Annual Meeting ISMRM-ESMRMB 2018, 16-21 June 2018.
- Di Pietro, G. et al. 2013. Assessment of muscle microstructures in osteoporotic and osteoarthritic subjects by using magnetic resonance diffusion tensor imaging. Presented at: European Congress on Osteoporosis and Osteoarthritis (ESCEO13-IOF), 2013, Vol. 24. Vol. Supp 1. Springer pp. S293-S293., (10.1007/s00198-013-2312-y)
Websites
- Palombo, M. et al. 2021. Joint estimation of relaxation and diffusion tissue parameters for prostate cancer grading with relaxation-VERDICT MRI. [Online]. medRxiv: Cold Spring Harbor Laboratory. (10.1101/2021.06.24.21259440) Available at: https://doi.org/10.1101/2021.06.24.21259440
- Perot, J., Celestine, M., Palombo, M., Dhenain, M., Humbert, S., Brouillet, E. and Flament, J. 2021. Identification of the key role of white matter alteration in the pathogenesis of Huntington’s Disease. [Online]. bioRxiv: Cold Spring Harbor Laboratory. (10.1101/2021.06.21.449242) Available at: https://doi.org/10.1101/2021.06.21.449242
- Callaghan, R., Alexander, D., Palombo, M. and Zhang, H. 2021. Impact of within-voxel heterogeneity in fibre geometry on spherical deconvolution. [Online]. arXiv: Cornell University. (10.48550/arXiv.2103.08237) Available at: https://doi.org/10.48550/arXiv.2103.08237
- Martins, J. P. d. A., Nilsson, M., Lampinen, B., Palombo, M., While, P. T., Westin, C. and Szczepankiewicz, F. 2021. Neural networks for parameter estimation in microstructural MRI: a study with a high-dimensional diffusion-relaxation model of white matter microstructure. [Online]. bioRxiv: Cold Spring Harbor Laboratory. (10.1101/2021.03.12.435163) Available at: https://doi.org/10.1101/2021.03.12.435163
Bywgraffiad
Education
- 2014: PhD in Biophysics. Sapienza University of Rome, Rome, Italy. Anomalous diffusion to probe brain microstructure by means of new NMR parameters: from theoretical modelling to NMR in vivo experiments.
- 2010: MSc in Physics. Sapienza University of Rome, Rome, Italy
- 2007: BSc in Physics. Sapienza University of Rome, Rome, Italy
Employment
- 2021 – present: Senior Lecturer joint School of Psychology and School of Computer Science and Informatics. Cardiff University, Cardiff, UK.
- 2018 – 2021: Senior Research Associate. University College London, London, UK.
- 2016 – 2018: Research Associate. University College London, London, UK.
- 2014 – 2016: Research Associate. Atomic Energy and Alternative Energies Commission (CEA), Fontenay-aux-Roses, France.
Anrhydeddau a dyfarniadau
11/2019 | 2019 ISMRM Outstanding Teacher Award |
05/2019 | 3rd place at the EPSRC’s Science Photography Competition 2019, in the Weird & Wonderful category. |
05/2019 | Magna Cum Laude Merit Award at International Society for Magnetic Resonance in Medicine (ISMRM) annual meeting. |
11/2018 | Best research image at the UCL Institute of Healthcare Engineering Autumn Research Symposium |
06/2018 | Finalist at the public engagement competition during the the International Society for Magnetic Resonance in Medicine (ISMRM) annual meeting |
04/2018 | Certificates of Outstanding Contribution in Reviewing by Neuroimage, Elsevier. |
01/2018 | UCL representative at the Global Young Scientists Summit (GYSS), Singapore |
06/2017 | Magna Cum Laude Merit Award at International Society for Magnetic Resonance in Medicine (ISMRM) annual meeting. |
05/2016 | Best work at the Diffusion Study Group at the International Society for Magnetic Resonance in Medicine (ISMRM) annual meeting |
04/2016 | Certificates of Outstanding Contribution in Reviewing by Journal of Magnetic Resonance Imaging, Wiley. |
2011 – 2014 | Educational Stipend awarded by the International Society for Magnetic Resonance in Medicine (ISMRM) |
Pwyllgorau ac adolygu
Peer reviewer for grant schemes both nationally and internationally:
- UKRI Future Leaders Fellowship scheme
- Personalized Health and Related Technologies (PHRT) strategic focus area of the ETH Domain
- Swiss Cancer Research foundation & Swiss Cancer League
Regular reviewer for research-focused journals:
- NeuroImage
- Magnetic Resonance in Medicine
- Journal of Magnetic Resonance
- Journal of Magnetic Resonance Imaging
- Magnetic Resonance Imaging
- Neurobiology of Aging
- Frontiers in Physics
