Dr Marco Palombo

(e/fe)

2024

• Molendowska, M. et al. 2024. Diffusion MRI in prostate cancer with ultra-strong whole body gradients. NMR in Biomedicine (10.1002/nbm.5229)
• Langkammer, C., Vaclavu, L., Kuestner, T., Bauer, M., Salameh, N., Palombo, M. and Lopez, R. P. 2024. ESMRMB 2024 focus topic: MR beyond trends—fact-checking MR. Magnetic Resonance Materials in Physics, Biology and Medicine 37, pp. 321-322. (10.1007/s10334-024-01177-4)
• Ioakeimidis, V. et al. 2024. Protocol for a randomised controlled unblinded feasibility trial of HD-DRUM, a rhythmic movement training application for cognitive and motor symptoms in people with Huntington’s disease. BMJ Open 14(7), article number: e082161. (10.1136/bmjopen-2023-082161)
• Cipiccia, S. et al. 2024. Fast X-ray ptychography: towards nanoscale imaging of large volume of brain. European Physical Journal Plus 139(5), article number: 434. (10.1140/epjp/s13360-024-05224-w)
• Barakovic, M. et al. 2024. A novel imaging marker of cortical “cellularity” in multiple sclerosis patients. Scientific Reports 14(1), article number: 9848. (10.1038/s41598-024-60497-6)
• Preziosa, P. et al. 2024. In-vivo assessment of Cellular Soma and Neurite Density Abnormalities in Multiple Sclerosis Paramagnetic Rim and Core-sign Lesions (P11-6.006). Neurology 102(17S1), article number: P11-6.006. (10.1212/WNL.0000000000205130)
• Mougel, E., Valette, J. and Palombo, M. 2024. Investigating exchange, structural disorder and restriction in Gray Matter via water and metabolites diffusivity and kurtosis time-dependence. Imaging Neuroscience 2, pp. 1-14. (10.1162/imag_a_00123)
• Ligneul, C. et al. 2024. Diffusion‐weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling. Magnetic Resonance in Medicine 91(3), pp. 860-885. (10.1002/mrm.29877)
• Uhl, Q., Pavan, T., Molendowska, M., Jones, D. K., Palombo, M. and Jelescu, I. 2024. Quantifying human gray matter microstructure using neurite exchange imaging (NEXI) and 300 mT/m gradients. Imaging Neuroscience 2, pp. 1-19. (10.1162/imag_a_00104)
• Lou, J. et al. 2024. Predicting radiologists' gaze with computational saliency models in mammogram reading. IEEE Transactions on Multimedia 26, pp. 256-269. (10.1109/TMM.2023.3263553)

2023

• Endt, S. et al. 2023. In vivo myelin water quantification using diffusion–relaxation correlation MRI: A comparison of 1D and 2D methods. Applied Magnetic Resonance 54, pp. 1571-1588. (10.1007/s00723-023-01584-1)
• Ioakeimidis, V. et al. 2023. Protocol for a randomised controlled feasibility trial of HD-DRUM, a rhythmic movement training application for cognitive and motor symptoms in people with Huntington?s disease.. [Online]. medRxiv: medRxiv. (10.1101/2023.11.15.23298581) Available at: https://doi.org/10.1101/2023.11.15.23298581
• 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)
• 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)
• 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)
• 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.
• 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.

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. 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)
• 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.
• 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.
• 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)
• 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)
• 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)
• 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., 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)
• 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. 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.
• 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).
• 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.
• 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)