Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge

. 2023 Aug 15 ; 277 () : 120231. [epub] 20230616

Jazyk angličtina Země Spojené státy americké Médium print-electronic

Typ dokumentu časopisecké články, Research Support, N.I.H., Extramural, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/pmid37330025

Grantová podpora
U54 AI117924 NIAID NIH HHS - United States
R01 AG037639 NIA NIH HHS - United States
R01 AI138647 NIAID NIH HHS - United States
R01 MH125479 NIMH NIH HHS - United States
R01 EB028774 NIBIB NIH HHS - United States
P50 HD105353 NICHD NIH HHS - United States
R01 NS102665 NINDS NIH HHS - United States
UF1 AG051216 NIA NIH HHS - United States
U54 HD090256 NICHD NIH HHS - United States
P50 AG033514 NIA NIH HHS - United States
P41 EB017183 NIBIB NIH HHS - United States
R01 NS092870 NINDS NIH HHS - United States
R34 DA050258 NIDA NIH HHS - United States
R01 EB017230 NIBIB NIH HHS - United States
R01 NS117568 NINDS NIH HHS - United States
R01 NS124920 NINDS NIH HHS - United States
R01 NS111022 NINDS NIH HHS - United States
P30 AG066512 NIA NIH HHS - United States
K01 EB032898 NIBIB NIH HHS - United States
R01 EB022883 NIBIB NIH HHS - United States
R01 AG027161 NIA NIH HHS - United States
P01 AI132132 NIAID NIH HHS - United States
RF1 AG059312 NIA NIH HHS - United States
R01 NS123378 NINDS NIH HHS - United States
R01 NS105646 NINDS NIH HHS - United States
R21 NS126806 NINDS NIH HHS - United States
R01 NS082436 NINDS NIH HHS - United States

Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.

A 1 Virtanen Institute for Molecular Sciences University of Eastern Finland Kuopio Finland; Department of Neuroscience and Biomedical Engineering Aalto University Espoo Finland; Department of Psychiatry Helsinki University Hospital Helsinki Finland

AGH University of Science and Technology Kraków Poland

AGH University of Science and Technology Kraków Poland; Laboratorio de Procesado de Imagen ETSI Telecomunicación Universidad de Valladolid Valladolid Spain

Athena Project Team Centre Inria d'Université Côte d'Azur France

Athena Project Team Centre Inria d'Université Côte d'Azur France; Institut de Biologie de Valrose Université Côte d'Azur Nice France

Brain Mapping Lab Department of Biomedical Dental Sciences and Morphological and Functional Images University of Messina Messina Italy

Brain Mapping Lab Department of Biomedical Dental Sciences and Morphological and Functional Images University of Messina Messina Italy; Center for Complex Network Intelligence Tsinghua University Beijing China; Department of Biomedical Engineering Tsinghua University Beijing China

Center for Advanced Imaging Innovation and Research Department of Radiology NYU Langone Health New York NY United States

CIBM Center for Biomedical Imaging Switzerland; Radiology Department Centre Hospitalier Universitaire Vaudois and University of Lausanne Lausanne Switzerland; Signal Processing Laboratory Lausanne Switzerland

Computer Science Department Centro de Investigación en Matemáticas A C Guanajuato México

Department of Biomedical Engineering The University of Melbourne Parkville Victoria Australia; Melbourne Neuropsychiatry Centre Department of Psychiatry The University of Melbourne and Melbourne Health Parkville Victoria Australia

Department of Electrical and Computer Engineering Vanderbilt University Nashville TN United States

Department of Medicine University of Wisconsin Madison Madison WI United States

Department of Neuroscience Rehabilitation Ophthalmology Genetics Maternal and Child Health University of Genoa Genoa Italy

Department of Neurosurgery Perlmutter Cancer Center Neuroscience Institute Kimmel Center for Stem Cell Biology NYU Langone Health New York NY United States

Department of Radiology and Biomedical Research Imaging Center The University of North Carolina at Chapel Hill Chapel Hill NC United States

Department of Radiology and Biomedical Research Imaging Center The University of North Carolina at Chapel Hill Chapel Hill NC United States; School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Department of Radiology and Radiological Sciences Vanderbilt University Medical Center Nashville TN United States

Department of Radiology and Radiological Sciences Vanderbilt University Medical Center Nashville TN United States; Department of Electrical and Computer Engineering Vanderbilt University Nashville TN United States

Department of Radiology Stanford University Stanford CA United States

Department of Radiology University of Wisconsin Madison Madison WI United States

Diffusion Imaging and Connectivity Estimation Department of Biomedical Engineering University Hospital Basel and University of Basel Basel Switzerland

Diffusion Imaging and Connectivity Estimation Department of Computer Science University of Sherbrooke Sherbrooke QC Canada

Diffusion Imaging and Connectivity Estimation Lab Department of Computer Science University of Verona Verona Italy

Diffusion Imaging and Connectivity Estimation Lab Department of Computer Science University of Verona Verona Italy; Brno Faculty of Electrical Engineering and Communication Department of mathematics University of Technology Brno Czech Republic

Diffusion Imaging and Connectivity Estimation Lab Department of Computer Science University of Verona Verona Italy; Department of Advanced Biomedical Sciences University of Naples Federico 2 Naples Italy

Instituto de Neurobiología Universidad Nacional Autónoma de México Juriquilla Querétaro México

Melbourne Neuropsychiatry Centre Department of Psychiatry The University of Melbourne and Melbourne Health Parkville Victoria Australia; School of Biomedical Engineering The University of Sydney Sydney Australia; Department of Psychological and Brain Sciences Indiana University Bloomington IN United States

Radiology Department Centre Hospitalier Universitaire Vaudois and University of Lausanne Lausanne Switzerland; Signal Processing Laboratory Lausanne Switzerland

Sano Centre for Computational Personalised Medicine Kraków Poland

Sherbrooke Connectivity Imaging Laboratory Department of Computer Science University of Sherbrooke Sherbrooke QC Canada

Signal Processing Laboratory Lausanne Switzerland

Signal Processing Laboratory Lausanne Switzerland; Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs Lyngby Denmark

Signal Processing Laboratory Lausanne Switzerland; McGill University Montréal QC Canada

Univ Rennes Inria CNRS Inserm IRISA UMR 6074 Empenn ERL U 1228 Rennes France

Waisman Center University of Wisconsin Madison Madison WI United States; Department of Medical Physics University of Wisconsin Madison Madison WI United States; Department of Psychiatry University of Wisconsin Madison Madison WI United States

Waisman Center University of Wisconsin Madison Madison WI United States; Department of Radiology University of Wisconsin Madison Madison WI United States

Zobrazit více v PubMed

Alexander DC, Hubbard PL, Hall MG, Moore E.a., Ptito M, Parker GJM, Dyrby TB, 2010. Orientationally invariant indices of axon diameter and density from diffusion mri. Neuroimage 52, 1374–1389. doi:10.1016/j.neuroimage.2010.05.043. PubMed DOI

Ambrosen KS, Eskildsen SF, Hinne M, Krug K, Lundell H, Schmidt MN, van Gerven MA, Mørup M, Dyrby TB, 2020. Validation of structural brain connectivity networks: the impact of scanning parameters. Neuroimage 204, 116–207. doi:10.1016/J.NEUROIMAGE.2019.116207. PubMed DOI

Ambrosen KS, Eskildsen SF, Hinne M, Krug K, Lundell H, Schmidt MN, van Gerven MA, Mørup M, Dyrby TB, 2020. Validation of structural brain connectivity networks: the impact of scanning parameters. Neuroimage 204, 116–207. doi:10.1016/J.NEUROIMAGE.2019.116207. PubMed DOI

Andersson M, Kjer HM, Rafael-Patino J, Pacureanu A, Pakkenberg B, Thiran JP, Ptito M, Bech M, Dahl AB, Dahl VA, Dyrby TB, 2021. Axon morphology is modulated by the local environment and impacts the noninvasive investigation of its structurefunction relationship. Proc. Natl. Acad. Sci. U.S.A 117, 33649–33659. doi:10.1073/PNAS.2012533117. PubMed DOI PMC

Assaf Y, Blumenfeld-Katzir T, Yovel Y, Basser PJ, 2008. Axcaliber: a method for measuring axon diameter distribution from diffusion mri. Magn. Reson. Med 59, 1347–1354. doi:10.1002/mrm.21577. PubMed DOI PMC

Aydogan DB, Jacobs R, Dulawa S, Thompson SL, Francois MC, Toga AW, Dong H, Knowles JA, Shi Y, 2018. When tractography meets tracer injections: a systematic study of trends and variation sources of diffusion-based connectivity. Brain Struct. Funct 223, 2841–2858. doi:10.1007/s00429-018-1663-8. PubMed DOI PMC

Aydogan DB, Shi Y, 2021. Parallel transport tractography. IEEE Trans. Med. Imaging 40, 635–647. doi:10.1109/TMI.2020.3034038. PubMed DOI PMC

Azadbakht H, Parkes LM, Haroon HA, Augath M, Logothetis NK, de Crespigny A, D’Arceuil HE, Parker GJM, 2015. Validation of high-resolution tractography against in vivo tracing in the macaque visual cortex. Cerebral Cortex 25, 4299–4309. doi:10.1093/cercor/bhu326. PubMed DOI PMC

Baete SH, Yutzy S, Boada FE, 2016. Radial q-space sampling for dsi. Magn. Reson. Med 76, 769–780. doi:10.1002/MRM.25917/ASSET/SUPINFO/MRM25917-SUP-0001-SUPPINFO01.PDF. PubMed DOI PMC

Caminiti R, Girard G, Battaglia-Mayer A, Borra E, Schito A, Innocenti GM, Luppino G, 2021. The complex hodological architecture of the macaque dorsal intraparietal areas as emerging from neural tracers and dw-mri tractography. eNeuro 8. doi:10.1523/ENEURO.0102-21.2021. PubMed DOI PMC

Canales-Rodríguez EJ, Daducci A, Sotiropoulos SN, Caruyer E, Aja-Fernández S, Radua J, Mendizabal JMY, Iturria-Medina Y, Melie-García L, Alemán-Gómez Y, Thiran J-P, Sarró S, Pomarol-Clotet E, Salvador R, 2015. Spherical deconvolution of multichannel diffusion mri data with non-gaussian noise models and spatial regularization. PLoS ONE 10, e0138910. doi:10.1371/journal.pone.0138910. PubMed DOI PMC

Caruyer E, Daducci A, Descoteaux M, christophe Houde J, philippe Thiran J, Verma R, 2014. Phantomas: a flexible software library to simulate diffusion mr phantoms.

Côté M-A, Girard G, Boré A, Garyfallidis E, Houde J-C, Descoteaux M, 2013. Tractometer: towards validation of tractography pipelines. Med. Image Anal 17, 844–857. PubMed

Cercignani M, Giulietti G, Dowell NG, Gabel M, Broad R, Leigh PN, Harrison NA, Bozzali M, 2017. Characterizing axonal myelination within the healthy population: a tract-by-tract mapping of effects of age and gender on the fiber g-ratio. Neurobiol. Aging 49, 109. doi:10.1016/J.NEUROBIOLAGING.2016.09.016. PubMed DOI PMC

Chomiak T, Hu B, 2009. What is the optimal value of the g-ratio for myelinated fibers in the rat cns? A theoretical approach. PLoS ONE 4. doi:10.1371/JOURNAL.PONE.0007754. PubMed DOI PMC

Close TG, Tournier J-D, Calamante F, Johnston LA, Mareels I, Connelly A, 2009. A software tool to generate simulated white matter structures for the assessment of fibre-tracking algorithms. Neuroimage 47, 1288–1300. doi:10.1016/J.NEUROIMAGE.2009.03.077. PubMed DOI

Coronado-Leija R, Ramirez-Manzanares A, Marroquin JL, 2017. Estimation of individual axon bundle properties by a multi-resolution discrete-search method. Med. Image Anal 42, 26–43. doi:10.1016/J.MEDIA.2017.06.008 . PubMed DOI

Daducci A, Canales-Rodríguez EJ, Zhang H, Dyrby TB, Alexander DC, Thiran JP, 2015. Accelerated microstructure imaging via convex optimization (amico) from diffusion mri data. Neuroimage 105, 32–44. doi:10.1016/j.neuroimage.2014.10.026. PubMed DOI

Daducci A, Palu AD, Lemkaddem A, Thiran J-P, 2014. Commit: convex optimization modeling for micro-structure informed tractography. IEEE Trans. Med. Imaging 34. PubMed

Delettre C, Messe A, Dell L-A, Foubet O, Heuer K, Larrat B, Meriaux S, Mangin J-F, Reillo I, de Juan Romero C, Borrell V, Toro R, Hilgetag CC, 2019. Comparison between diffusion mri tractography and histological tract-tracing of cortico-cortical structural connectivity in the ferret brain. Network Neurosci. (Cambridge, Mass.) 3, 1038–1050. doi:10.1101/517136. PubMed DOI PMC

Dimitriadis SI, Drakesmith M, Bells S, Parker GD, Linden DE, Jones DK, 2017. Improving the reliability of network metrics in structural brain networks by integrating different network weighting strategies into a single graph. Front. Neurosci 11, 694. doi:10.3389/FNINS.2017.00694/BIBTEX. PubMed DOI PMC

Donahue CJ, Sotiropoulos SN, Jbabdi S, Hernandez-Fernandez M, Behrens TE, Dyrby TB, Coalson T, Kennedy H, Knoblauch K, Essen DCV, Glasser MF, 2016. Using diffusion tractography to predict cortical connection strength and distance: a quantitative comparison with tracers in the monkey. J. Neurosci 36, 6758–6770. doi:10.1523/JNEUROSCI.0493-16.2016. PubMed DOI PMC

Essen DCV, Jbabdi S, Sotiropoulos SN, Chen C, Dikranian K, Coalson T, Harwell J, Glasser MF, 2014. Mapping connections in humans and non-human primates: aspirations and challenges for diffusion imaging. Diffusion MRI 337–358. doi:10.1016/B978-0-12-396460-1.00016-0. DOI

Fick RH, Wassermann D, Deriche R, 2019. The dmipy toolbox: diffusion mri multi-compartment modeling and microstructure recovery made easy. Front. Neuroinform 13, 64. doi:10.3389/FNINF.2019.00064/BIBTEX. PubMed DOI PMC

Fillard P, Descoteaux M, Goh A, Gouttard S, Jeurissen B, Malcolm J, Ramirez-Manzanares A, Reisert M, Sakaie K, Tensaouti F, Yo T, Mangin J-F, Poupon C, 2011. Quantitative evaluation of 10 tractography algorithms on a realistic diffusion mr phantom. Neuroimage 56, 220–234. doi:10.1016/j.neuroimage.2011.01.032. PubMed DOI

Frigo M, Zucchelli M, Deriche R, Deslauriers-Gauthier S, 2021. Talon: tractograms as linear operators in neuroimaging.

Garyfallidis E, Brett M, Amirbekian B, Rokem A, Walt SVD, Descoteaux M, Nimmo-smith I, Contributors D, 2014. Dipy, a library for the analysis of diffusion mri data. Front. Neuroinform 8, 1–5. http://www.frontiersin.org/Journal/10.3389/fninf.2014.00008/abstract. PubMed DOI PMC

Girard G, Caminiti R, Battaglia-Mayer A, St-Onge E, Ambrosen KS, Eskildsen SF, Krug K, Dyrby TB, Descoteaux M, Thiran JP, Innocenti GM, 2020. On the cortical connectivity in the macaque brain: acomparison of diffusion tractography and histological tracing data. Neuroimage 221, 117201. doi:10.1016/j.neuroimage.2020.117201. PubMed DOI

Girard G, Caruyer E, Rafael-Patino J, Pizzolato M, Truffet R, Thiran J-P, 2021. Diffusion-simulated connectivity challenge. Zenodo. 10.5281/zenodo.4733450. PubMed DOI PMC

Girard G, Whittingstall K, Deriche R, Descoteaux M, 2014. Towards quantitative connectivity analysis: reducing tractography biases. Neuroimage 98, 266–278. doi:10.1016/j.neuroimage.2014.04.074. PubMed DOI

Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O, 2008. Mapping the structural core of human cerebral cortex. PLoS Biol. 6, e159. doi:10.1371/journal.pbio.0060159. PubMed DOI PMC

Hagmann P, Kurant M, Gigandet X, Thiran P, Wedeen VJ, Meuli R, Thiran J-P, 2007. Mapping human whole-brain structural networks with diffusion mri. PLoS ONE 2, e597. doi:10.1371/journal.pone.0000597. PubMed DOI PMC

Hall MG, Alexander DC, 2009. Convergence and parameter choice for monte-carlo simulations of diffusion mri. IEEE Trans. Med. Imaging 28, 1354–1364. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4797853. PubMed

van den Heuvel MP, de Reus MA, Barrett LF, Scholtens LH, Coopmans FM, Schmidt R, Preuss TM, Rilling JK, Li L, 2015. Comparison of diffusion tractography and tract-tracing measures of connectivity strength in rhesus macaque connectome. Hum. Brain Mapp 36, 3064–3075. doi:10.1002/hbm.22828. PubMed DOI PMC

Jbabdi S, Lehman JF, Haber SN, Behrens TE, 2013. Human and monkey ventral prefrontal fibers use the same organizational principles to reach their targets: tracing versus tractography. J. Neurosci 33, 3190–3201. doi:10.1523/JNEUROSCI.2457-12.2013. PubMed DOI PMC

Jbabdi S, Sotiropoulos SN, Haber SN, Essen DCV, Behrens TE, 2015. Measuring macroscopic brain connections in vivo. Nat. Neurosci 18, 1546–1555. doi:10.1038/nn.4134 . PubMed DOI

Jeurissen B, Descoteaux M, Mori S, Leemans A, 2017. Diffusion mri fiber tractography of the brain. NMR Biomed. e3785. doi:10.1002/nbm.3785. PubMed DOI

Jeurissen B, Tournier J-D, Dhollander T, Connelly A, Sijbers J, 2014. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion mri data. Neuroimage 103, 411–426. doi:10.1016/j.neuroimage.2014.07.061. PubMed DOI

Jones DK, 2010. Challenges and limitations of quantifying brain connectivity in vivo with diffusion mri. Imaging Med. 2, 341–355. http://www.futuremedicine.com/doi/abs/10.2217/iim.10.21. DOI

Le Bihan D, Poupon C, Amadon A, Lethimonnier F, 2006. Artifacts and pitfalls in diffusion mri. J. Magn. Reson. Imaging 24, 478–488. http://www.ncbi.nlm.nih.gov/pubmed/16897692. PubMed

Lee HH, Fieremans E, Novikov DS, 2021. Realistic microstructure simulator (rms): monte carlo simulations of diffusion in three-dimensional cell segmentations of microscopy images. J. Neurosci. Methods 350, 109018. doi:10.1016/j.jneumeth.2020.109018. PubMed DOI PMC

Maffei C, Girard G, Schilling KG, Aydogan DB, Adluru N, Zhylka A, Wu Y, Mancini M, Hamamci A, Sarica A, Teillac A, Baete SH, Karimi D, Yeh F-C, Yildiz ME, Gholipour A, Bihan-Poudec Y, Hiba B, Quattrone A, Quattrone A, Boshkovski T, Stikov N, Yap P-T, de Luca A, Pluim J, Leemans A, Prabhakaran V, Bendlin BB, Alexander AL, Landman BA, Canales-Rodríguez EJ, Barakovic M, Rafael-Patino J, Yu T, Rensonnet G, Schiavi S, Daducci A, Pizzolato M, Fischi-Gomez E, Thiran J-P, Dai G, Grisot G, Lazovski N, Puch S, Ramos M, Rodrigues P, Prkovska V, Jones R, Lehman J, Haber SN, Yendiki A, 2022. Insights from the irontract challenge: optimal methods for mapping brain pathways from multi-shell diffusion mri. Neuroimage 257, 119327. doi:10.1016/J.NEUROIMAGE.2022.119327. PubMed DOI PMC

Maier-Hein KH, Neher PF, Houde J-C, Côté M-A, Garyfallidis E, Zhong J, Chamberland M, Yeh F-C, Lin Y-C, Ji Q, Reddick WE, Glass JO, Chen DQ, Feng Y, Gao C, Wu Y, Ma J, Renjie H, Li Q, Westin C-F, Deslauriers-Gauthier S, González JOO, Paquette M, St-Jean S, Girard G, Rheault F, Sidhu J, Tax CMW, Guo F, Mesri HY, Dávid S, Froeling M, Heemskerk AM, Leemans A, Boré A, Pinsard B, Bedetti C, Desrosiers M, Brambati S, Doyon J, Sarica A, Vasta R, Cerasa A, Quattrone A, Yeatman J, Khan AR, Hodges W, Alexander S, Romascano D, Barakovic M, Auría A, Esteban O, Lemkaddem A, Thiran J-P, Cetingul HE, Odry BL, Mailhe B, Nadar MS, Pizzagalli F, Prasad G, Villalon-Reina JE, Galvis J, Thompson PM, Requejo FDS, Laguna PL, Lacerda LM, Barrett R, Dell’Acqua F, Catani M, Petit L, Caruyer E, Daducci A, Dyrby TB, Holland-Letz T, Hilgetag CC, Stieltjes B, Descoteaux M, 2017. The challenge of mapping the human connectome based on diffusion tractography. Nat. Commun 8, 1349. doi:10.1038/s41467-017-01285-x. PubMed DOI PMC

Messaritaki E, Dimitriadis SI, Jones DK, 2019. Optimization of graph construction can significantly increase the power of structural brain network studies. Neuroimage 199, 495–511. doi:10.1016/J.NEUROIMAGE.2019.05.052. PubMed DOI PMC

Nath V, Schilling KG, Parvathaneni P, Huo Y, Blaber JA, Hainline AE, Barakovic M, Romascano D, RafaelPatino J, Frigo M, Girard G, Thiran J, Daducci A, Rowe M, Rodrigues P, Prkovska V, Aydogan DB, Sun W, Shi Y, Parker WA, Ismail AAO, Verma R, Cabeen RP, Toga AW, Newton AT, Wasserthal J, Neher P, MaierHein K, Savini G, Palesi F, Kaden E, Wu Y, He J, Feng Y, Paquette M, Rheault F, Sidhu J, Lebel C, Leemans A, Descoteaux M, Dyrby TB, Kang H, Landman BA, 2020. Tractography reproducibility challenge with empirical data (traced): the 2017 ismrm diffusion study group challenge. J. Magn. Reson. Imaging 51, 234–249. doi:10.1002/jmri.26794. PubMed DOI PMC

Neher PF, Stieltjes B, Laun FB, Meinzer H-P, Fritzsche K, 2013. Fiberfox : a novel tool to generate software phantoms of complex fiber geometries, Vol. 21.

Ocampo-Pineda M, Schiavi S, Rheault F, Girard G, Petit L, Descoteaux M, Daducci A, 2021. Hierarchical microstructure informed tractography. Brain Connect. doi:10.1089/brain.2020.0907. PubMed DOI

Rafael-Patino J, Girard G, Truffet R, Pizzolato M, Caruyer E, Thiran J-P, 2021. The diffusion-simulated connectivity (disco) dataset. Data Brief 38, 107429. doi:10.1016/J.DIB.2021.107429. PubMed DOI PMC

Rafael-Patino J, Girard G, Truffet R, Pizzolato M, Caruyer E, Thiran J-P, 2022. The diffusion-simulated connectivity dataset 2. doi:10.17632/FGF86JDFG6.2. PubMed DOI PMC

Rafael-Patino J, Girard G, Truffet R, Pizzolato M, Thiran J-P, Caruyer E, 2021. The microstructural features of the diffusion-simulated connectivity (disco) dataset. Lect. Note. Comput. Sci. (Includ. subser. Lect. Note. Artif. Intell. Lect. Note. Bioinform.) 13006 LNCS, 159–170. doi:10.1007/978-3-030-87615-9_14. DOI

Rafael-Patino J, Romascano D, Ramirez-Manzanares A, Canales-Rodríguez EJ, Girard G, Thiran J-P, 2020. Robust monte-carlo simulations in diffusion-mri: effect of the substrate complexity and parameter choice on the reproducibility of results. Front. Neuroinform 14, 8. doi:10.3389/fninf.2020.00008. PubMed DOI PMC

Romascano D, Barakovic M, Rafael-Patino J, Dyrby TB, Thiran JP, Daducci A, 2019. Activeaxadd: toward non-parametric and orientationally invariant axon diameter distribution mapping using pgse. Magn. Reson. Med 83, 2322–2330. doi:10.1002/mrm.28053. PubMed DOI

Schilling KG, Gao Y, Stepniewska I, Janve V, Landman BA, Anderson AW, 2019. Anatomical accuracy of standard-practice tractography algorithms in the motor system - a histological validation in the squirrel monkey brain. Magn. Reson. Imaging 55, 7–25. doi:10.1016/j.mri.2018.09.004. PubMed DOI PMC

Schilling KG, Nath V, Hansen C, Parvathaneni P, Blaber J, Gao Y, Neher P, Aydogan DB, Shi Y, Ocampo-Pineda M, Schiavi S, Daducci A, Girard G, Barakovic M, Rafael-Patino J, Romascano D, Rensonnet G, Pizzolato M, Bates A, Fischi E, Thiran J-P, Canales-Rodríguez EJ, Huang C, Zhu H, Zhong L, Cabeen R, Toga AW, Rheault F, Theaud G, Houde J-C, Sidhu J, Chamberland M, Westin C-F, Dyrby TB, Verma R, Rathi Y, Irfanoglu MO, Thomas C, Pierpaoli C, Descoteaux M, Anderson AW, Landman BA, 2019. Limits to anatomical accuracy of diffusion tractography using modern approaches. Neuroimage 185, 1–11. doi:10.1016/J.NEUROIMAGE.2018.10.029. PubMed DOI PMC

Sedlar S, Papadopoulo T, Deriche R, Deslauriers-Gauthier S, 2021. Diffusion mri fiber orientation distribution function estimation using voxel-wise spherical u-net. Springer, Cham, pp. 95–106. doi:10.1007/978-3-030-73018-5_8. DOI

Smith RE, Tournier J-D, Calamante F, Connelly A, 2013. Sift: spherical-deconvolution informed filtering of tractograms. Neuroimage 67, 298–312. doi:10.1016/j.neuroimage.2012.11.049. PubMed DOI

Smith RE, Tournier J-D, Calamante F, Connelly A, 2015. Sift2: enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. Neuroimage 119, 338–351. doi:10.1016/j.neuroimage.2015.06.092. PubMed DOI

Sotiropoulos SN, Zalesky A, 2019. Building connectomes using diffusion mri: why, how and but. NMR Biomed. 32. doi:10.1002/NBM.3752. PubMed DOI PMC

Sporns O, 2011. The human connectome: a complex network. Ann. N. Y. Acad. Sci 1224, 109–125. http://www.ncbi.nlm.nih.gov/pubmed/21251014. PubMed

Thomas C, Ye FQ, Irfanoglu MO, Modi P, Saleem KS, Leopold DA, Pierpaoli C, 2014. Anatomical accuracy of brain connections derived from diffusion mri tractography is inherently limited. Proc. Natl. Acad. Sci. U.S.A 111, 16574–16579. doi:10.1073/pnas.1405672111. PubMed DOI PMC

Tournier J-D, Calamante F, Connelly A, 2010. Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions.

Tournier J-D, Calamante F, Gadian DG, Connelly A, 2004. Direct estimation of the fiber orientation density function from diffusion-weighted mri data using spherical deconvolution. Neuroimage 23, 1176–1185. doi:10.1016/j.neuroimage.2004.07.037. PubMed DOI

Tournier JD, Smith R, Raffelt D, Tabbara R, Dhollander T, Pietsch M, Christiaens D, Jeurissen B, Yeh CH, Connelly A, 2019. Mrtrix3: a fast, flexible and open software framework for medical image processing and visualisation. Neuroimage 202, 116137. doi:10.1016/j.neuroimage.2019.116137. PubMed DOI

Tran G, Shi Y, 2015. Fiber orientation and compartment parameter estimation from multi-shell diffusion imaging. IEEE Trans. Med. Imaging 34, 2320–2332. doi:10.1109/TMI.2015.2430850 . PubMed DOI PMC

Veraart J, Novikov DS, Christiaens D, Ades-aron B, Sijbers J, Fieremans E, 2016. Denoising of diffusion mri using random matrix theory. Neuroimage 142, 394–406. doi:10.1016/j.neuroimage.2016.08.016. PubMed DOI PMC

Wu Y, Hong Y, Ahmad S, Chang WT, Lin W, Shen D, Yap PT, 2020. Globally optimized super-resolution of diffusion mri data via fiber continuity. Medical image computing and computer-assisted intervention : MICCAI … International Conference on Medical Image Computing and Computer-Assisted Intervention 12267, 260. doi:10.1007/978-3-030-59728-3_26. PubMed DOI PMC

Wu Y, Lin W, Shen D, Yap P-T, 2019. Asymmetry spectrum imaging for baby diffusion tractography. doi:10.1007/978-3-030-20351-1_24. PubMed DOI PMC

Yeh CH, Jones DK, Liang X, Descoteaux M, Connelly A, 2021. Mapping structural connectivity using diffusion mri: challenges and opportunities. J. Magn. Reson. Imaging 53, 1666–1682. doi:10.1002/JMRI.27188. PubMed DOI PMC

Yeh F, 2017. Diffusion mri reconstruction in dsi studio. Advanced Biomedical MRI Lab, National Taiwan University Hospital. Available online at: http://dsi-studio.labsolver.org/Manual/Reconstruction.

Najít záznam

Citační ukazatele

Nahrávání dat ...

Možnosti archivace

Nahrávání dat ...