| Variability in the analysis of a single neuroimaging dataset by many teams R Botvinik-Nezer, F Holzmeister, CF Camerer, A Dreber, J Huber, ... Nature 582 (7810), 84-88, 2020 | 910 | 2020 |
| Neuroimaging standards for research into small vessel disease—advances since 2013 M Duering, GJ Biessels, A Brodtmann, C Chen, C Cordonnier, ... The Lancet Neurology 22 (7), 602-618, 2023 | 170 | 2023 |
| Neurogenetics of developmental dyslexia: from genes to behavior through brain neuroimaging and cognitive and sensorial mechanisms S Mascheretti*, A De Luca*, V Trezzi, D Peruzzo, A Nordio, C Marino, ... Translational psychiatry 7 (1), e987-e987, 2017 | 161 | 2017 |
| Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset? KG Schilling, F Rheault, L Petit, CB Hansen, V Nath, FC Yeh, G Girard, ... Neuroimage 243, 118502, 2021 | 134 | 2021 |
| Multi‐center evaluation of stability and reproducibility of quantitative MRI measures in healthy calf muscles L Schlaffke, R Rehmann, M Rohm, LAM Otto, A de Luca, J Burakiewicz, ... NMR in Biomedicine 32 (9), e4119, 2019 | 60 | 2019 |
| Epilepsy in multiple sclerosis: The role of temporal lobe damage M Calabrese, M Castellaro, A Bertoldo, A De Luca, FB Pizzini, ... Multiple Sclerosis Journal 23 (3), 473-482, 2017 | 48 | 2017 |
| Effects of perfusion on DTI and DKI estimates in the skeletal muscle A De Luca, A Bertoldo, M Froeling Magnetic Resonance in Medicine 78 (1), 233-246, 2017 | 47 | 2017 |
| Multiparametric quantitative MRI assessment of thigh muscles in limb‐girdle muscular dystrophy 2A and 2B F Arrigoni, A De Luca, D Velardo, F Magri, S Gandossini, A Russo, ... Muscle & nerve 58 (4), 550-558, 2018 | 45 | 2018 |
| European guideline for imaging in paediatric and adolescent rhabdomyosarcoma—joint statement by the European Paediatric Soft Tissue Sarcoma Study Group, the Cooperative … R van Ewijk, RA Schoot, M Sparber-Sauer, SAJ Ter Horst, N Jehanno, ... Pediatric Radiology 51 (10), 1940-1951, 2021 | 37 | 2021 |
| Fractal analysis of MRI data for the characterization of patients with schizophrenia and bipolar disorder L Squarcina, A De Luca, M Bellani, P Brambilla, FE Turkheimer, ... Physics in Medicine & Biology 60 (4), 1697, 2015 | 37 | 2015 |
| Functional and structural brain damage in Friedreich's ataxia M Vavla, F Arrigoni, A Nordio, A De Luca, S Pizzighello, E Petacchi, ... Frontiers in Neurology 9, 747, 2018 | 26 | 2018 |
| Consenso argentino sobre parálisis cerebral. Rol del cuidado perinatal C Solana, A Uranga, R Votta, N Bruno, A Kozak, A Larguía, J Urman, ... Revista del Hospital Materno Infantil Ramón Sardá 19 (3), 120-124, 2000 | 26 | 2000 |
| Automated characterization of noise distributions in diffusion MRI data S St-Jean, A De Luca, CMW Tax, MA Viergever, A Leemans Medical Image Analysis 65, 101758, 2020 | 25 | 2020 |
| A robust deconvolution method to disentangle multiple water pools in diffusion MRI A De Luca, A Leemans, A Bertoldo, F Arrigoni, M Froeling NMR in Biomedicine 31 (11), e3965, 2018 | 25 | 2018 |
| Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI C Maffei, G Girard, KG Schilling, DB Aydogan, N Adluru, A Zhylka, Y Wu, ... NeuroImage 257, 119327, 2022 | 23 | 2022 |
| Quantitative muscle MRI protocol as possible biomarker in Becker muscular dystrophy L Maggi, M Moscatelli, R Frangiamore, F Mazzi, M Verri, A De Luca, ... Clinical neuroradiology 31, 257-266, 2021 | 22 | 2021 |
| Generalized Richardson-Lucy (GRL) for analyzing multi-shell diffusion MRI data F Guo, A Leemans, MA Viergever, F Dell’Acqua, A De Luca Neuroimage 218, 116948, 2020 | 22 | 2020 |
| Zooming in on cerebral small vessel function in small vessel diseases with 7T MRI: rationale and design of the “ZOOM@ SVDs” study H Van Den Brink, A Kopczak, T Arts, L Onkenhout, JCW Siero, ... Cerebral Circulation-Cognition and Behavior 2, 100013, 2021 | 21 | 2021 |
| Spherical deconvolution with tissue-specific response functions and multi-shell diffusion MRI to estimate multiple fiber orientation distributions (mFODs) A De Luca, F Guo, M Froeling, A Leemans Neuroimage 222, 117206, 2020 | 19 | 2020 |
| On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge A De Luca, A Ianus, A Leemans, M Palombo, N Shemesh, H Zhang, ... NeuroImage 240, 118367, 2021 | 17 | 2021 |
Alexander LeemansAssociate Professor, UMC Utrecht, The Netherlands在 umcutrecht.nl 的电子邮件经过验证
