{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:01:34Z","timestamp":1785402094805,"version":"3.56.0"},"reference-count":78,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000054","name":"NIH","doi-asserted-by":"publisher","award":["R37CA288396"],"award-info":[{"award-number":["R37CA288396"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"NIH","doi-asserted-by":"publisher","award":["U01CA220378"],"award-info":[{"award-number":["U01CA220378"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"NIH","doi-asserted-by":"publisher","award":["U01CA250481"],"award-info":[{"award-number":["U01CA250481"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"NIH","doi-asserted-by":"publisher","award":["R01CA264992"],"award-info":[{"award-number":["R01CA264992"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"NIH","doi-asserted-by":"publisher","award":["R01CA221938"],"award-info":[{"award-number":["R01CA221938"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"NIH","doi-asserted-by":"publisher","award":["R21NS082609"],"award-info":[{"award-number":["R21NS082609"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"NIH","doi-asserted-by":"publisher","award":["K01EB032898"],"award-info":[{"award-number":["K01EB032898"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"award":["R37CA288396"],"award-info":[{"award-number":["R37CA288396"]}],"id":[{"id":"https:\/\/ror.org\/05h1kgg64","id-type":"ROR","asserted-by":"publisher"}]},{"award":["U01CA220378"],"award-info":[{"award-number":["U01CA220378"]}],"id":[{"id":"https:\/\/ror.org\/05h1kgg64","id-type":"ROR","asserted-by":"publisher"}]},{"award":["U01CA250481"],"award-info":[{"award-number":["U01CA250481"]}],"id":[{"id":"https:\/\/ror.org\/05h1kgg64","id-type":"ROR","asserted-by":"publisher"}]},{"award":["R01CA264992"],"award-info":[{"award-number":["R01CA264992"]}],"id":[{"id":"https:\/\/ror.org\/05h1kgg64","id-type":"ROR","asserted-by":"publisher"}]},{"award":["R01CA221938"],"award-info":[{"award-number":["R01CA221938"]}],"id":[{"id":"https:\/\/ror.org\/05h1kgg64","id-type":"ROR","asserted-by":"publisher"}]},{"award":["R21NS082609"],"award-info":[{"award-number":["R21NS082609"]}],"id":[{"id":"https:\/\/ror.org\/05h1kgg64","id-type":"ROR","asserted-by":"publisher"}]},{"award":["K01EB032898"],"award-info":[{"award-number":["K01EB032898"]}],"id":[{"id":"https:\/\/ror.org\/05h1kgg64","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Mayo Clinic & ASU Alliance for Health Care"},{"name":"ASU"},{"id":[{"id":"https:\/\/ror.org\/00cb9w016","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Arizona Biomedical Research Center"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Tomography"],"abstract":"<jats:p>Background\/Objectives: White matter (WM) tract detection is critical in the presurgical planning of tumor resection. However, standard-of-care imaging techniques including T1-weighted, T2-weighted, and Diffusion Tensor Imaging (DTI) often fail to identify WM tracts within edematous regions. In T1\/T2-weighted imaging, edema increases extracellular water and reduces tissue contrast, and in diffusion-weighted imaging, edema elevates isotropic diffusion, reducing sensitivity to anisotropic diffusion along WM tracts. Advanced biophysical diffusion modeling techniques such as Neurite Orientation Dispersion and Density Imaging (NODDI) and the Standard Model (SM) address this limitation by compartmentalizing the diffusion signal into free-water, intra-neurite, and extra-neurite contributions. Here, we test if biophysical multi-compartment models can robustly identify WM tracts and recover tractography streamlines within edematous regions. Methods: In this study, we use multi-shell diffusion-weighted MRI data obtained from patients with meningiomas\u2014a pathology allowing for isolation of the effects of edema without the confounding effects of tumor cell invasion. We compared FA from standard and free-water-corrected DTI, the orientation dispersion index (ODI) from NODDI, and P2 (a scalar descriptor of fiber orientation coherence) from the SM fODF in edematous and unaffected contralateral WM regions. As a proof of concept, we visually evaluated the tractography performance across models. Results: Our results show that (1 \u2212 ODI) and P2 values in edema remained close to within-subject contralateral measurements, contrasting with substantial reductions in FA and FW-FA. (1 \u2212 ODI) showed a small but statistically significant increase in edema (~8%, p = 0.02), while P2 was unchanged. Conclusions: These results highlight the potential of biophysical diffusion models for preoperative mapping in edema.<\/jats:p>","DOI":"10.3390\/tomography12060078","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T15:05:08Z","timestamp":1779721508000},"page":"78","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Biophysical Diffusion MRI Models Better Identify White Matter Tracts in Edema"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1359-1627","authenticated-orcid":false,"given":"Isaac E.","family":"Prentiss","sequence":"first","affiliation":[{"name":"School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0019-6284","authenticated-orcid":false,"given":"Sasha","family":"Hakhu","sequence":"additional","affiliation":[{"name":"School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennapher","family":"Lingo VanGilder","sequence":"additional","affiliation":[{"name":"School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9194-1297","authenticated-orcid":false,"given":"Parvathy","family":"Hareesh","sequence":"additional","affiliation":[{"name":"School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8705-3759","authenticated-orcid":false,"given":"Andrew","family":"Hooyman","sequence":"additional","affiliation":[{"name":"School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason","family":"Yalim","sequence":"additional","affiliation":[{"name":"Research Computing, Arizona State University, Tempe, AZ 85287, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin","family":"Hines","sequence":"additional","affiliation":[{"name":"Department of Radiology, Mayo Clinic, Phoenix, AZ 85054, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabe","family":"LaFond","sequence":"additional","affiliation":[{"name":"Department of Radiology, Mayo Clinic, Phoenix, AZ 85054, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5440-1649","authenticated-orcid":false,"given":"Edward","family":"Ofori","sequence":"additional","affiliation":[{"name":"College of Health Solutions, Arizona State University, Tempe, AZ 85287, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3971-863X","authenticated-orcid":false,"given":"Leslie C.","family":"Baxter","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Psychology, Mayo Clinic, Phoenix, AZ 85054, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxiang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Radiology, Mayo Clinic, Phoenix, AZ 85054, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leland S.","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Mayo Clinic, Phoenix, AZ 85054, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kurt G.","family":"Schilling","sequence":"additional","affiliation":[{"name":"Department of Radiology, Vanderbilt University Medical Center, Nashville, TN 37232, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6721-1185","authenticated-orcid":false,"given":"Scott C.","family":"Beeman","sequence":"additional","affiliation":[{"name":"School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287, USA"},{"name":"John Shufeldt School of Medicine and Medical Engineering, Arizona State University, Tempe, AZ 85287, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gondar, R., Patet, G., Schaller, K., and Meling, T.R. (2021). Meningiomas and Cognitive Impairment after Treatment: A Systematic and Narrative Review. Cancers, 13.","DOI":"10.3390\/cancers13081846"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"311","DOI":"10.3171\/2023.6.JNS231048","article-title":"Damaging a Few Millimeters of the Deep White Matter Tracts during Glioma Surgery May Result in a Large-Scale Brain Disconnection","volume":"140","author":"Duffau","year":"2023","journal-title":"J. Neurosurg."},{"key":"ref_3","first-page":"937","article-title":"Peritumoral Diffusion Tensor Imaging of High-Grade Gliomas and Metastatic Brain Tumors","volume":"24","author":"Lu","year":"2003","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1002\/nbm.1544","article-title":"Diffusion Imaging of Brain Tumors","volume":"23","author":"Maier","year":"2010","journal-title":"NMR Biomed."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1136\/jnnp.69.2.269","article-title":"Diffusion Tensor Imaging Can Detect and Quantify Corticospinal Tract Degeneration after Stroke","volume":"69","author":"Werring","year":"2000","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1002\/1522-2594(200010)44:4<583::AID-MRM12>3.0.CO;2-O","article-title":"Magnetic Resonance Diffusion Tensor Imaging for Characterizing Diffuse and Focal White Matter Abnormalities in Multiple Sclerosis","volume":"44","author":"Bammer","year":"2000","journal-title":"Magn. Reson. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/S0730-725X(02)00509-X","article-title":"Diffusion Tensor MRI in Temporal Lobe Epilepsy","volume":"20","author":"Arfanakis","year":"2002","journal-title":"Magn. Reson. Imaging"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1212\/01.wnl.0000250326.77323.01","article-title":"Diffusion Tensor Imaging of Cingulum Fibers in Mild Cognitive Impairment and Alzheimer Disease","volume":"68","author":"Zhang","year":"2007","journal-title":"Neurology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"203","DOI":"10.3174\/ajnr.A1303","article-title":"Diffusion Tensor Imaging in Glioblastoma Multiforme and Brain Metastases: The Role of p, q, L, and Fractional Anisotropy","volume":"30","author":"Wang","year":"2009","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s11682-012-9156-5","article-title":"A Review of Magnetic Resonance Imaging and Diffusion Tensor Imaging Findings in Mild Traumatic Brain Injury","volume":"6","author":"Shenton","year":"2012","journal-title":"Brain Imaging Behav."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.neuroimage.2011.11.006","article-title":"Diffusion MRI at 25: Exploring Brain Tissue Structure and Function","volume":"61","year":"2012","journal-title":"NeuroImage"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2283","DOI":"10.1007\/s00330-008-0971-4","article-title":"Diffusion Tensor Imaging and Tractography of the Median Nerve in Carpal Tunnel Syndrome: Preliminary Results","volume":"18","author":"Khalil","year":"2008","journal-title":"Eur. Radiol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Heckel, A., Weiler, M., Xia, A., Ruetters, M., Pham, M., Bendszus, M., Heiland, S., and Baeumer, P. (2015). Peripheral Nerve Diffusion Tensor Imaging: Assessment of Axon and Myelin Sheath Integrity. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0130833"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1002\/jmri.25876","article-title":"Peripheral Nerve Diffusion Tensor Imaging: Overview, Pitfalls, and Future Directions","volume":"47","author":"Jeon","year":"2018","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4852","DOI":"10.1038\/s41598-023-31307-2","article-title":"Meta-Analysis of the Normal Diffusion Tensor Imaging Values of the Peripheral Nerves in the Upper Limb","volume":"13","author":"Wade","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/S0006-3495(94)80775-1","article-title":"MR Diffusion Tensor Spectroscopy and Imaging","volume":"66","author":"Basser","year":"1994","journal-title":"Biophys. J."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Costabile, J.D., Alaswad, E., D\u2019Souza, S., Thompson, J.A., and Ormond, D.R. (2019). Current Applications of Diffusion Tensor Imaging and Tractography in Intracranial Tumor Resection. Front. Oncol., 9.","DOI":"10.3389\/fonc.2019.00426"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1007\/s11548-015-1344-5","article-title":"Corticospinal Tract Modeling for Neurosurgical Planning by Tracking through Regions of Peritumoral Edema and Crossing Fibers Using Two-Tensor Unscented Kalman Filter Tractography","volume":"11","author":"Chen","year":"2016","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gong, S., Zhang, F., Norton, I., Essayed, W.I., Unadkat, P., Rigolo, L., Pasternak, O., Rathi, Y., Hou, L., and Golby, A.J. (2018). Free Water Modeling of Peritumoral Edema Using Multi-Fiber Tractography: Application to Tracking the Arcuate Fasciculus for Neurosurgical Planning. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0197056"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1097\/01.cco.0000142076.52721.b3","article-title":"The Management of Brain Edema in Brain Tumors","volume":"16","author":"Kaal","year":"2004","journal-title":"Curr. Opin. Oncol."},{"key":"ref_21","unstructured":"Lecoeur, J., Caruyer, E., Elliott, M., Brem, S., Macyszyn, L., and Verma, R. (2014). Addressing the Challenge of Edema in Fiber Tracking. MICCAI 2014 DTI Tractography Challenge, HAL."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1016\/j.nicl.2017.06.027","article-title":"Performance of Unscented Kalman Filter Tractography in Edema: Analysis of the Two-Tensor Model","volume":"15","author":"Liao","year":"2017","journal-title":"Neuroimage Clin."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1227\/NEU.0000000000000146","article-title":"Differences Between Generalized Q-Sampling Imaging and Diffusion Tensor Imaging in the Preoperative Visualization of the Nerve Fiber Tracts Within Peritumoral Edema in Brain","volume":"73","author":"Zhang","year":"2013","journal-title":"Neurosurgery"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1002\/mrm.22055","article-title":"Free Water Elimination and Mapping from Diffusion MRI","volume":"62","author":"Pasternak","year":"2009","journal-title":"Magn. Reson. Med."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Figley, C.R., Uddin, M.N., Wong, K., Kornelsen, J., Puig, J., and Figley, T.D. (2022). Potential Pitfalls of Using Fractional Anisotropy, Axial Diffusivity, and Radial Diffusivity as Biomarkers of Cerebral White Matter Microstructure. Front. Neurosci., 15.","DOI":"10.3389\/fnins.2021.799576"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.neuroimage.2014.09.053","article-title":"Optimization of a Free Water Elimination Two-Compartment Model for Diffusion Tensor Imaging","volume":"103","author":"Hoy","year":"2014","journal-title":"NeuroImage"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1016\/j.neuroimage.2012.03.072","article-title":"NODDI: Practical in Vivo Neurite Orientation Dispersion and Density Imaging of the Human Brain","volume":"61","author":"Zhang","year":"2012","journal-title":"NeuroImage"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"119290","DOI":"10.1016\/j.neuroimage.2022.119290","article-title":"Reproducibility of the Standard Model of Diffusion in White Matter on Clinical MRI Systems","volume":"257","author":"Coelho","year":"2022","journal-title":"NeuroImage"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.2214\/ajr.135.5.1031","article-title":"Kricheff White Matter Buckling: CT Sign of Extraaxial Intracranial Mass","volume":"135","author":"George","year":"1980","journal-title":"Am. J. Roentgenol."},{"key":"ref_30","first-page":"29","article-title":"Intracranial Meningioma","volume":"13","author":"Sheporaitis","year":"1992","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e383","DOI":"10.1016\/S1470-2045(16)30321-7","article-title":"EANO Guidelines for the Diagnosis and Treatment of Meningiomas","volume":"17","author":"Goldbrunner","year":"2016","journal-title":"Lancet Oncol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"100","DOI":"10.14791\/btrt.2016.4.2.100","article-title":"White Matter Change Revealed by Diffusion Tensor Imaging in Gliomas","volume":"4","author":"Won","year":"2016","journal-title":"Brain Tumor Res. Treat."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1007\/s13311-018-00697-x","article-title":"Tractography for Surgical Neuro-Oncology Planning: Towards a Gold Standard","volume":"16","author":"Panesar","year":"2019","journal-title":"Neurotherapeutics"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Manan, A.A., Yahya, N.A., Taib, N.H.M., Idris, Z., and Manan, H.A. (2023). The Assessment of White Matter Integrity Alteration Pattern in Patients with Brain Tumor Utilizing Diffusion Tensor Imaging: A Systematic Review. Cancers, 15.","DOI":"10.3390\/cancers15133326"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"116137","DOI":"10.1016\/j.neuroimage.2019.116137","article-title":"MRtrix3: A Fast, Flexible and Open Software Framework for Medical Image Processing and Visualisation","volume":"202","author":"Tournier","year":"2019","journal-title":"NeuroImage"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.neuroimage.2016.08.016","article-title":"Denoising of Diffusion MRI Using Random Matrix Theory","volume":"142","author":"Veraart","year":"2016","journal-title":"NeuroImage"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.mri.2019.05.008","article-title":"Synthesized B0 for Diffusion Distortion Correction (Synb0-DisCo)","volume":"64","author":"Schilling","year":"2019","journal-title":"Magn. Reson. Imaging"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1016\/j.neuroimage.2011.09.015","article-title":"FSL","volume":"62","author":"Jenkinson","year":"2012","journal-title":"NeuroImage"},{"key":"ref_39","unstructured":"(2025, November 07). FSL Diffusion Toolbox Practical. Available online: https:\/\/fsl.fmrib.ox.ac.uk\/fslcourse\/2019_Beijing\/lectures\/FDT\/fdt1.html."},{"key":"ref_40","unstructured":"D\u2019Costa, S. (2025, November 07). Sameerd\/DiffusionTensorImaging 2025. Available online: https:\/\/github.com\/sameerd\/DiffusionTensorImaging."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Henriques, R.N., Rokem, A., Garyfallidis, E., St-Jean, S., Peterson, E.T., and Correia, M.M. (2017). [Re] Optimization of a Free Water Elimination Two-Compartment Model for Diffusion Tensor Imaging. bioRxiv.","DOI":"10.1101\/108795"},{"key":"ref_42","unstructured":"(2025, November 07). NYU-DiffusionMRI\/SMI 2025. Available online: https:\/\/github.com\/NYU-DiffusionMRI\/SMI."},{"key":"ref_43","unstructured":"(2025, November 07). Microstructure Imaging Group|Download NODDI Matlab Toolbox. Available online: http:\/\/mig.cs.ucl.ac.uk\/index.php?n=Download.NODDI."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chong, S.T., Liu, X., Kao, H.-W., Lin, C.-Y.E., Hsu, C.-C.H., Kung, Y.-C., Kuo, K.-T., Huang, C.-C., Lo, C.-Y.Z., and Li, Y. (2021). Exploring Peritumoral Neural Tracts by Using Neurite Orientation Dispersion and Density Imaging. Front. Neurosci., 15.","DOI":"10.3389\/fnins.2021.800009"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1002\/1531-8249(199902)45:2<265::AID-ANA21>3.0.CO;2-3","article-title":"Three-Dimensional Tracking of Axonal Projections in the Brain by Magnetic Resonance Imaging","volume":"45","author":"Mori","year":"1999","journal-title":"Ann. Neurol."},{"key":"ref_46","unstructured":"(2025, November 07). Tckgen\u2014MRtrix3 3.0 Documentation. Available online: https:\/\/mrtrix.readthedocs.io\/en\/dev\/reference\/commands\/tckgen.html."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"297","DOI":"10.2307\/1932409","article-title":"Measures of the Amount of Ecologic Association Between Species","volume":"26","author":"Dice","year":"1945","journal-title":"Ecology"},{"key":"ref_48","unstructured":"(2025, November 07). FAST. Available online: https:\/\/web.mit.edu\/fsl_v5.0.10\/fsl\/doc\/wiki\/FAST.html."},{"key":"ref_49","unstructured":"Dhollander, T., Mito, R., Raffelt, D., and Connelly, A. (2019, January 11\u201316). Improved White Matter Response Function Estimation for 3-Tissue Constrained Spherical Deconvolution. Proceedings of the ISMRM 27th Annual Meeting & Exhibition, Montr\u00e9al, QC, Canada."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.neuroimage.2014.07.061","article-title":"Multi-Tissue Constrained Spherical Deconvolution for Improved Analysis of Multi-Shell Diffusion MRI Data","volume":"103","author":"Jeurissen","year":"2014","journal-title":"NeuroImage"},{"key":"ref_51","first-page":"1670","article-title":"Improved Probabilistic Streamlines Tractography by 2nd Order Integration over Fibre Orientation Distributions","volume":"Volume 18","author":"Tournier","year":"2010","journal-title":"Proceedings of the International Society for Magnetic Resonance in Medicine"},{"key":"ref_52","unstructured":"(2025, November 07). R: The R Project for Statistical Computing. Available online: https:\/\/www.r-project.org\/."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Edwards, L.J., Pine, K.J., Ellerbrock, I., Weiskopf, N., and Mohammadi, S. (2017). NODDI-DTI: Estimating Neurite Orientation and Dispersion Parameters from a Diffusion Tensor in Healthy White Matter. Front. Neurosci., 11.","DOI":"10.3389\/fnins.2017.00720"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.nicl.2015.08.017","article-title":"Clinically Feasible NODDI Characterization of Glioma Using Multiband EPI at 7 T","volume":"9","author":"Wen","year":"2015","journal-title":"Neuroimage Clin."},{"key":"ref_55","first-page":"24","article-title":"Differentiation of Glioma Malignancy Grade Using Diffusion MRI","volume":"40","author":"Maximov","year":"2017","journal-title":"Phys. Medica Eur. J. Med. Phys."},{"key":"ref_56","first-page":"251","article-title":"Differentiation of Edematous, Tumoral and Normal Areas of Brain Using Diffusion Tensor and Neurite Orientation Dispersion and Density Imaging","volume":"8","author":"Masjoodi","year":"2018","journal-title":"J. Biomed. Phys. Eng."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"e247","DOI":"10.1016\/j.wneu.2019.07.121","article-title":"Application of Neurite Orientation Dispersion and Density Imaging in Assessing Glioma Grades and Cellular Proliferation","volume":"131","author":"Li","year":"2019","journal-title":"World Neurosurg."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.neurad.2018.10.005","article-title":"Differentiation between Glioblastoma and Solitary Brain Metastasis Using Neurite Orientation Dispersion and Density Imaging","volume":"47","author":"Kadota","year":"2020","journal-title":"J. Neuroradiol."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Mao, J., Zeng, W., Zhang, Q., Yang, Z., Yan, X., Zhang, H., Wang, M., Yang, G., Zhou, M., and Shen, J. (2020). Differentiation between High-Grade Gliomas and Solitary Brain Metastases: A Comparison of Five Diffusion-Weighted MRI Models. BMC Med. Imaging, 20.","DOI":"10.1186\/s12880-020-00524-w"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1268","DOI":"10.1002\/hbm.25291","article-title":"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","volume":"42","author":"Pieri","year":"2020","journal-title":"Hum. Brain Mapp."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"W\u00fcrtemberger, U., Rau, A., Reisert, M., Kellner, E., Diebold, M., Erny, D., Reinacher, P.C., Hosp, J.A., Hohenhaus, M., and Urbach, H. (2022). Differentiation of Perilesional Edema in Glioblastomas and Brain Metastases: Comparison of Diffusion Tensor Imaging, Neurite Orientation Dispersion and Density Imaging and Diffusion Microstructure Imaging. Cancers, 15.","DOI":"10.3390\/cancers15010129"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.mri.2023.03.001","article-title":"Neurite Orientation Dispersion and Density Imaging and Diffusion Tensor Imaging to Facilitate Distinction between Infiltrating Tumors and Edemas in Glioblastoma","volume":"100","author":"Okita","year":"2023","journal-title":"Magn. Reson. Imaging"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"26","DOI":"10.3340\/jkns.2011.49.1.26","article-title":"Peritumoral Brain Edema in Meningiomas: Correlation of Radiologic and Pathologic Features","volume":"49","author":"Kim","year":"2011","journal-title":"J. Korean Neurosurg. Soc."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1016\/j.neuroimage.2006.02.024","article-title":"Tract-Based Spatial Statistics: Voxelwise Analysis of Multi-Subject Diffusion Data","volume":"31","author":"Smith","year":"2006","journal-title":"NeuroImage"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1007\/s00234-010-0716-3","article-title":"Voxel-Based Analysis of the Diffusion Tensor","volume":"52","author":"Abe","year":"2010","journal-title":"Neuroradiology"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"410","DOI":"10.3348\/kjr.2015.16.2.410","article-title":"Effect of Increasing Diffusion Gradient Direction Number on Diffusion Tensor Imaging Fiber Tracking in the Human Brain","volume":"16","author":"Yao","year":"2015","journal-title":"Korean J. Radiol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"4843","DOI":"10.1111\/ejn.15785","article-title":"Effect of Number of Diffusion-Encoding Directions in Diffusion Metrics of 5-Year-Olds Using Tract-Based Spatial Statistical Analysis","volume":"56","author":"Kumpulainen","year":"2022","journal-title":"Eur. J. Neurosci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3827","DOI":"10.1111\/ejn.16135","article-title":"Effect of Number of Diffusion Encoding Directions in Neonatal Diffusion Tensor Imaging Using Tract-Based Spatial Statistical Analysis","volume":"58","author":"Merisaari","year":"2023","journal-title":"Eur. J. Neurosci."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.neurobiolaging.2018.07.018","article-title":"Re-Examining Age-Related Differences in White Matter Microstructure with Free-Water Corrected Diffusion Tensor Imaging","volume":"71","author":"Chad","year":"2018","journal-title":"Neurobiol. Aging"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Hoy, A.R., Ly, M., Carlsson, C.M., Okonkwo, O.C., Zetterberg, H., Blennow, K., Sager, M.A., Asthana, S., Johnson, S.C., and Alexander, A.L. (2017). Microstructural White Matter Alterations in Preclinical Alzheimer\u2019s Disease Detected Using Free Water Elimination Diffusion Tensor Imaging. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0173982"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1093\/brain\/awv361","article-title":"Free-Water Imaging in Parkinson\u2019s Disease and Atypical Parkinsonism","volume":"139","author":"Planetta","year":"2016","journal-title":"Brain"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1093\/neuros\/nyab129","article-title":"Enhanced Fiber Tractography Using Edema Correction: Application and Evaluation in High-Grade Gliomas","volume":"89","author":"Henderson","year":"2021","journal-title":"Neurosurgery"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"imag-2-00252","DOI":"10.1162\/imag_a_00252","article-title":"The Trouble with Free-Water Elimination Using Single-Shell Diffusion MRI Data: A Case Study in Ageing","volume":"2","author":"Correia","year":"2024","journal-title":"Imaging Neurosci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1002\/mrm.24685","article-title":"Multiband Phase-Constrained Parallel MRI","volume":"69","author":"Blaimer","year":"2013","journal-title":"Magn. Reson. Med."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1002\/jmri.23639","article-title":"Parallel MR Imaging","volume":"36","author":"Deshmane","year":"2012","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Lee, D., Yoo, J., and Ye, J.C. (2017). Deep Artifact Learning for Compressed Sensing and Parallel MRI 2017. arXiv.","DOI":"10.1109\/ISBI.2017.7950457"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"116835","DOI":"10.1016\/j.neuroimage.2020.116835","article-title":"SANDI: A Compartment-Based Model for Non-Invasive Apparent Soma and Neurite Imaging by Diffusion MRI","volume":"215","author":"Palombo","year":"2020","journal-title":"NeuroImage"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"119277","DOI":"10.1016\/j.neuroimage.2022.119277","article-title":"Neurite Exchange Imaging (NEXI): A Minimal Model of Diffusion in Gray Matter with Inter-Compartment Water Exchange","volume":"256","author":"Jelescu","year":"2022","journal-title":"NeuroImage"}],"container-title":["Tomography"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2379-139X\/12\/6\/78\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T10:07:07Z","timestamp":1781690827000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2379-139X\/12\/6\/78"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,25]]},"references-count":78,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["tomography12060078"],"URL":"https:\/\/doi.org\/10.3390\/tomography12060078","relation":{},"ISSN":["2379-139X"],"issn-type":[{"value":"2379-139X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,25]]}}}