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Pieri V, Sanvito F, Riva M, Petrini A, Rancoita PMV, Cirillo S, Iadanza A, Bello L, Castellano A, Falini A. Along-tract statistics of neurite orientation dispersion and density imaging diffusion metrics to enhance MR tractography quantitative analysis in healthy controls and in patients with brain tumors. Hum Brain Mapp 2020; 42:1268-1286. [PMID: 33274823 PMCID: PMC7927309 DOI: 10.1002/hbm.25291] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 10/29/2020] [Accepted: 11/09/2020] [Indexed: 12/12/2022] Open
Abstract
Along‐tract statistics analysis enables the extraction of quantitative diffusion metrics along specific white matter fiber tracts. Besides quantitative metrics derived from classical diffusion tensor imaging (DTI), such as fractional anisotropy and diffusivities, new parameters reflecting the relative contribution of different diffusion compartments in the tissue can be estimated through advanced diffusion MRI methods as neurite orientation dispersion and density imaging (NODDI), leading to a more specific microstructural characterization. In this study, we extracted both DTI‐ and NODDI‐derived quantitative microstructural diffusion metrics along the most eloquent fiber tracts in 15 healthy subjects and in 22 patients with brain tumors. We obtained a robust intraprotocol reference database of normative along‐tract microstructural metrics, and their corresponding plots, from healthy fiber tracts. Each diffusion metric of individual patient's fiber tract was then plotted and statistically compared to the normative profile of the corresponding metric from the healthy fiber tracts. NODDI‐derived metrics appeared to account for the pathological microstructural changes of the peritumoral tissue more accurately than DTI‐derived ones. This approach may be useful for future studies that may compare healthy subjects to patients diagnosed with other pathological conditions.
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Affiliation(s)
- Valentina Pieri
- Vita-Salute San Raffaele University, Milan, Italy.,Neuroradiology Unit and CERMAC, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Francesco Sanvito
- Vita-Salute San Raffaele University, Milan, Italy.,Neuroradiology Unit and CERMAC, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Marco Riva
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milan, Italy.,Neurosurgical Oncology Unit, Humanitas Clinical and Research Center - IRCCS, Milan, Italy
| | - Alessandro Petrini
- Department of Computer Science, Università degli Studi di Milano, Milan, Italy
| | - Paola M V Rancoita
- University Centre for Statistics in the Biomedical Sciences, Vita-Salute San Raffaele University, Milan, Italy
| | - Sara Cirillo
- Vita-Salute San Raffaele University, Milan, Italy.,Neuroradiology Unit and CERMAC, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Antonella Iadanza
- Vita-Salute San Raffaele University, Milan, Italy.,Neuroradiology Unit and CERMAC, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Lorenzo Bello
- Neurosurgical Oncology Unit, Humanitas Clinical and Research Center - IRCCS, Milan, Italy.,Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Antonella Castellano
- Vita-Salute San Raffaele University, Milan, Italy.,Neuroradiology Unit and CERMAC, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Andrea Falini
- Vita-Salute San Raffaele University, Milan, Italy.,Neuroradiology Unit and CERMAC, IRCCS San Raffaele Scientific Institute, Milan, Italy
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