Differentiation of glioma malignancy grade using diffusion MRI |
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| Institution: | 1. College of Medicine and Veterinary Medicine, University of Edinburgh, UK;2. Brain Research Imaging Centre, University of Edinburgh, UK;3. Centre for Clinical Brain Sciences, University of Edinburgh, UK;4. Computer Science Department, Brown University, Providence, RI, United States;1. Brigham and Women’s Hospital, Harvard Medical School, Boston, United States;2. German Cancer Research Institute, Germany;3. IBM Almaden Research Center, San Jose, United States;4. Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology, University of Utah, United States;5. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore;6. ParIMed Team, LRPE, USTHB, Algiers, Algeria;7. Athena Project-Team, Inria Sophia Antipolis-Méditerranée, France;8. Department of Computer Science, University of Verona, Italy;9. Université de Sherbrooke, Canada;10. Centro de Investigation en Matematicas, Department of Computer Science, Mexico;1. Ecole Polythechnique Fédéral de Lausanne, Signal Processing Laboratories (LTS5), Lausanne, Switzerland;2. Centre d''Imagerie BioMédicale (CIBM-AIT), Ecole Polythechnique Fédéral de Lausanne, Lausanne, Switzerland;3. Dpt of Radiology, University Hospital of Geneva, Switzerland;4. Epilepsy Unit, Neurology Clinic, University Hospitals and Faculty of Medicine of Geneva, Switzerland;5. Dpt of Radiology, University Hospital and University of Lausanne, Switzerland;1. Department of Radiology, The University of Tokyo, Bunkyo, Tokyo, Japan;2. Department of Radiology, Juntendo University School of Medicine, Bunkyo, Tokyo, Japan;3. Department of Neurosurgery, Juntendo University School of Medicine, Bunkyo, Tokyo, Japan;4. Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Arakawa, Tokyo, Japan;5. Department of Radiology, The University of Tokyo Hospital, Bunkyo, Tokyo, Japan;1. Electrical Engineering, Vanderbilt University, Nashville, TN, USA;2. Computer Science, Vanderbilt University, Nashville, TN, USA;3. Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA;4. Biostatistics, Vanderbilt University, Nashville, TN, USA;5. MR Clinical Science, Philips Healthcare, Gainsville, FL, USA |
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| Abstract: | Modern diffusion MR protocols allow one to acquire the multi-shell diffusion data with high diffusion weightings in a clinically feasible time. In the present work we assessed three diffusion approaches based on diffusion and kurtosis tensor imaging (DTI, DKI), and neurite orientation dispersion and density imaging (NODDI) as possible biomarkers for human brain glioma grade differentiation based on the one diffusion protocol. We used three diffusion weightings (so called b-values) equal to 0, 1000, and 2500 s/mm2 with 60 non-coplanar diffusion directions in the case of non-zero b-values. The patient groups of the glioma grades II, III, and IV consist of 8 subjects per group. We found that DKI, and NODDI scalar metrics can be effectively used as glioma grade biomarkers with a significant difference (p < 0.05) for grading between low- and high-grade gliomas, in particular, for glioma II versus glioma III grades, and glioma III versus glioma IV grades. The use of mean/axial kurtosis and intra-axonal fraction/orientation dispersion index metrics allowed us to obtain the most feasible and reliable differentiation criteria. For example, in the case of glioma grades II, III, and IV the mean kurtosis is equal to 0.31, 0.51, and 0.90, and the orientation dispersion index is equal to 0.14, 0.30, and 0.59, respectively. The limitations and perspectives of the biophysical diffusion models based on intra-/extra-axonal compartmentalisation for glioma differentiation are discussed. |
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| Keywords: | Tumour malignancy differentiation NODDI DKI DTI |
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