{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T07:04:36Z","timestamp":1774076676130,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T00:00:00Z","timestamp":1650844800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T00:00:00Z","timestamp":1650844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/S021930\/1"],"award-info":[{"award-number":["EP\/S021930\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose:<\/jats:title>\n                <jats:p>Intraoperative diffusion MRI could provide a means of visualising brain fibre tracts near a neurosurgical target after preoperative images have been invalidated by brain shift. We propose an atlas-based intraoperative tract segmentation method, as the standard preoperative method, streamline tractography, is unsuitable for intraoperative implementation.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods:<\/jats:title>\n                <jats:p>A tract-specific voxel-wise fibre orientation atlas is constructed from healthy training data. After registration with a target image, a radial tumour deformation model is applied to the orientation atlas to account for displacement caused by lesions. The final tract map is obtained from the inner product of the atlas and target image fibre orientation data derived from intraoperative diffusion MRI.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results:<\/jats:title>\n                <jats:p>The simple tumour model takes only seconds to effectively deform the atlas into alignment with the target image. With minimal processing time and operator effort, maps of surgically relevant tracts can be achieved that are visually and qualitatively comparable with results obtained from streamline tractography.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion:<\/jats:title>\n                <jats:p>Preliminary results demonstrate feasibility of intraoperative streamline-free tract segmentation in challenging neurosurgical cases. Demonstrated results in a small number of representative sample subjects are realistic despite the simplicity of the tumour deformation model employed. Following this proof of concept, future studies will focus on achieving robustness in a wide range of tumour types and clinical scenarios, as well as quantitative validation of segmentations.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-022-02617-z","type":"journal-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T09:16:36Z","timestamp":1650878196000},"page":"1559-1567","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Fibre tract segmentation for intraoperative diffusion MRI in neurosurgical patients using tract-specific orientation atlas and tumour deformation modelling"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6803-0690","authenticated-orcid":false,"given":"Fiona","family":"Young","sequence":"first","affiliation":[]},{"given":"Kristian","family":"Aquilina","sequence":"additional","affiliation":[]},{"given":"Chris","family":"A. Clark","sequence":"additional","affiliation":[]},{"given":"Jonathan","family":"D. Clayden","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,25]]},"reference":[{"issue":"4","key":"2617_CR1","doi-asserted-by":"publisher","first-page":"e3785","DOI":"10.1002\/nbm.3785","volume":"32","author":"B Jeurissen","year":"2019","unstructured":"Jeurissen B, Descoteaux M, Mori S, Leemans A (2019) Diffusion MRI fiber tractography of the brain. NMR Biomed 32(4):e3785. https:\/\/doi.org\/10.1002\/nbm.3785","journal-title":"NMR Biomed"},{"issue":"1","key":"2617_CR2","doi-asserted-by":"publisher","first-page":"011011","DOI":"10.1088\/1741-2552\/ab6aad","volume":"17","author":"F Rheault","year":"2020","unstructured":"Rheault F, Poulin P, Valcourt Caron A, St-Onge E, Descoteaux M (2020) Common misconceptions, hidden biases and modern challenges of dMRI tractography. J Neural Eng 17(1):011011. https:\/\/doi.org\/10.1088\/1741-2552\/ab6aad","journal-title":"J Neural Eng"},{"issue":"15","key":"2617_CR3","doi-asserted-by":"publisher","first-page":"15TR011","DOI":"10.1088\/1361-6560\/ac0d90","volume":"66","author":"JY-M Yang","year":"2021","unstructured":"Yang JY-M, Yeh C-H, Poupon C, Calamante F (2021) Diffusion MRI tractography for neurosurgery: the basics, current state, technical reliability and challenges. Phys Med Biol 66(15):15TR011. https:\/\/doi.org\/10.1088\/1361-6560\/ac0d90","journal-title":"Phys Med Biol"},{"key":"2617_CR4","doi-asserted-by":"publisher","first-page":"118651","DOI":"10.1016\/j.neuroimage.2021.118651","volume":"245","author":"F-C Yeh","year":"2021","unstructured":"Yeh F-C, Irimia A, Bastos DCdA, Golby AJ (2021) Tractography methods and findings in brain tumors and traumatic brain injury. NeuroImage 245:118651. https:\/\/doi.org\/10.1016\/j.neuroimage.2021.118651","journal-title":"NeuroImage"},{"key":"2617_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118502","author":"KG Schilling","year":"2021","unstructured":"Schilling KG, Rheault F, Petit L, Hansen CB, Nath V, Yeh FC, Girard G, Barakovic M, Rafael-Patino J, Yu T, Fischi-Gomez E, Pizzolato M, Ocampo-Pineda M, Schiavi S, Canales-Rodr\u00edguez EJ, Daducci A, Granziera C, Innocenti G, Thiran JP, Mancini L, Wastling S, Cocozza S, Petracca M, Pontillo G, Mancini M, Vos SB, Vakharia VN, Duncan JS, Melero H, Manzanedo L, Sanz-Morales E, Pe\u00f1a-Meli\u00e1n \u00c1, Calamante F, Atty\u00e9 A, Cabeen RP, Korobova L, Toga AW, Vijayakumari AA, Parker D, Verma R, Radwan A, Sunaert S, Emsell L, De Luca A, Leemans A, Bajada CJ, Haroon H, Azadbakht H, Chamberland M, Genc S, Tax CM, Yeh PH, Srikanchana R, Mcknight CD, Yang JYM, Chen J, Kelly CE, Yeh CH, Cochereau J, Maller JJ, Welton T, Almairac F, Seunarine KK, Clark CA, Zhang F, Makris N, Golby A, Rathi Y, O\u2019Donnell LJ, Xia Y, Aydogan DB, Shi Y, Fernandes FG, Raemaekers M, Warrington S, Michielse S, Ram\u00edrez-Manzanares A, Concha L, Aranda R, Meraz MR, Lerma-Usabiaga G, Roitman L, Fekonja LS, Calarco N, Joseph M, Nakua H, Voineskos AN, Karan P, Grenier G, Legarreta JH, Adluru N, Nair VA, Prabhakaran V, Alexander AL, Kamagata K, Saito Y, Uchida W, Andica C, Abe M, Bayrak RG, Wheeler-Kingshott CA, D\u2019Angelo E, Palesi F, Savini G, Rolandi N, Guevara P, Houenou J, L\u00f3pez-L\u00f3pez N, Mangin JF, Poupon C, Rom\u00e1n C, V\u00e1zquez A, Maffei C, Arantes M, Andrade JP, Silva SM, Calhoun VD, Caverzasi E, Sacco S, Lauricella M, Pestilli F, Bullock D, Zhan Y, Brignoni-Perez E, Lebel C, Reynolds JE, Nestrasil I, Labounek R, Lenglet C, Paulson A, Aulicka S, Heilbronner SR, Heuer K, Chandio BQ, Guaje J, Tang W, Garyfallidis E, Raja R, Anderson AW, Landman BA, Descoteaux M (2021) Tractography dissection variability: what happens when 42 groups dissect 14 white matter bundles on the same dataset? NeuroImage. https:\/\/doi.org\/10.1016\/j.neuroimage.2021.118502","journal-title":"NeuroImage"},{"issue":"1","key":"2617_CR6","doi-asserted-by":"publisher","first-page":"E7","DOI":"10.1093\/neuros\/nyx082","volume":"81","author":"C Wu","year":"2017","unstructured":"Wu C, Mohamed FB (2017) Letter: evaluation of diffusion tensor imaging-based tractography of the corticospinal tract: a correlative study with intraoperative magnetic resonance imaging and direct electrical subcortical stimulation. Neurosurgery 81(1):E7\u2013E8. https:\/\/doi.org\/10.1093\/neuros\/nyx082","journal-title":"Neurosurgery"},{"issue":"11","key":"2617_CR7","doi-asserted-by":"publisher","first-page":"1062","DOI":"10.1016\/S1470-2045(11)70130-9","volume":"12","author":"PL Kubben","year":"2011","unstructured":"Kubben PL, ter Meulen KJ, Schijns OE, ter Laak-Poort MP, van Overbeeke JJ, van Santbrink H (2011) Intraoperative MRI-guided resection of glioblastoma multiforme: a systematic review. Lancet Oncol 12(11):1062\u20131070. https:\/\/doi.org\/10.1016\/S1470-2045(11)70130-9","journal-title":"Lancet Oncol"},{"issue":"6","key":"2617_CR8","doi-asserted-by":"publisher","first-page":"2977","DOI":"10.1007\/s10143-021-01488-3","volume":"44","author":"C Tuleasca","year":"2021","unstructured":"Tuleasca C, Leroy H-A, Peciu-Florianu I, Strachowski O, Derre B, Levivier M, Schulder M, Reyns N (2021) Impact of combined use of intraoperative MRI and awake microsurgical resection on patients with gliomas: a systematic review and meta-analysis. Neurosurg Rev 44(6):2977\u20132990. https:\/\/doi.org\/10.1007\/s10143-021-01488-3","journal-title":"Neurosurg Rev"},{"issue":"April","key":"2617_CR9","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1016\/j.nicl.2017.06.011","volume":"15","author":"WI Essayed","year":"2017","unstructured":"Essayed WI, Zhang F, Unadkat P, Cosgrove GR, Golby AJ, O\u2019Donnell LJ (2017) White matter tractography for neurosurgical planning: a topography-based review of the current state of the art. NeuroImage Clin 15(April):659\u2013672. https:\/\/doi.org\/10.1016\/j.nicl.2017.06.011","journal-title":"NeuroImage Clin"},{"key":"2617_CR10","doi-asserted-by":"publisher","DOI":"10.1080\/02688697.2020.1849542","author":"SM Toescu","year":"2020","unstructured":"Toescu SM, Hales PW, Tisdall MM, Aquilina K, Clark CA (2020) Neurosurgical applications of tractography in the UK. Br J Neurosurg. https:\/\/doi.org\/10.1080\/02688697.2020.1849542","journal-title":"Br J Neurosurg"},{"issue":"4","key":"2617_CR11","doi-asserted-by":"publisher","first-page":"S710","DOI":"10.1016\/S1053-8119(18)31543-X","volume":"7","author":"S Mori","year":"1998","unstructured":"Mori S, Crain BJ, van Zijl PC (1998) 3D brain fiber reconstruction from diffusion MRI. NeuroImage 7(4):S710. https:\/\/doi.org\/10.1016\/S1053-8119(18)31543-X","journal-title":"NeuroImage"},{"issue":"7\u20138","key":"2617_CR12","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1002\/nbm.781","volume":"15","author":"S Mori","year":"2002","unstructured":"Mori S, van Zijl PCM (2002) Fiber tracking: principles and strategies\u2014a technical review. NMR Biomed 15(7\u20138):468\u2013480. https:\/\/doi.org\/10.1002\/nbm.781","journal-title":"NMR Biomed"},{"key":"2617_CR13","doi-asserted-by":"publisher","first-page":"107001","DOI":"10.1016\/j.clineuro.2021.107001","volume":"210","author":"TJ Richards","year":"2021","unstructured":"Richards TJ, Anderson KL, Anderson JS (2021) Fully automated segmentation of the corticospinal tract using the TractSeg algorithm in patients with brain tumors. Clin Neurol Neurosurg 210:107001. https:\/\/doi.org\/10.1016\/j.clineuro.2021.107001","journal-title":"Clin Neurol Neurosurg"},{"key":"2617_CR14","doi-asserted-by":"publisher","first-page":"30663","DOI":"10.1109\/ACCESS.2018.2839681","volume":"6","author":"A Elazab","year":"2018","unstructured":"Elazab A, Abdulazeem YM, Anter AM, Hu Q, Wang T, Lei B (2018) Macroscopic cerebral tumor growth modeling from medical images: a review. IEEE Access 6:30663\u201330679. https:\/\/doi.org\/10.1109\/ACCESS.2018.2839681","journal-title":"IEEE Access"},{"issue":"3","key":"2617_CR15","doi-asserted-by":"publisher","first-page":"e158","DOI":"10.1016\/j.cmpb.2011.07.015","volume":"104","author":"M Cabezas","year":"2011","unstructured":"Cabezas M, Oliver A, Llad\u00f3 X, Freixenet J, Bach Cuadra M (2011) A review of atlas-based segmentation for magnetic resonance brain images. Comput Methods Prog Biomed 104(3):e158\u2013e177. https:\/\/doi.org\/10.1016\/j.cmpb.2011.07.015","journal-title":"Comput Methods Prog Biomed"},{"key":"2617_CR16","doi-asserted-by":"crossref","unstructured":"Mang A, Bakas S, Subramanian S, Davatzikos C, Biros G (2020) Integrated biophysical modeling and image analysis: application to neuro-oncology. Ann Rev Biomed Eng 22:309\u2013341. https:\/\/www.annualreviews.org\/doi\/abs\/10.1146\/annurev-bioeng-062117-121105. arXiv:2002.09628","DOI":"10.1146\/annurev-bioeng-062117-121105"},{"issue":"5","key":"2617_CR17","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1016\/j.media.2006.06.005","volume":"10","author":"A Mohamed","year":"2006","unstructured":"Mohamed A, Zacharaki E, Shen D, Davatzikos C (2006) Deformable registration of brain tumor images via a statistical model of tumor-induced deformation. Med Image Anal 10(5):752\u2013763. https:\/\/doi.org\/10.1016\/j.media.2006.06.005","journal-title":"Med Image Anal"},{"issue":"3","key":"2617_CR18","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1016\/j.neuroimage.2009.01.051","volume":"46","author":"EI Zacharaki","year":"2009","unstructured":"Zacharaki EI, Hogea CS, Shen D, Biros G, Davatzikos C (2009) Non-diffeomorphic registration of brain tumor images by simulating tissue loss and tumor growth. NeuroImage 46(3):762\u2013774. https:\/\/doi.org\/10.1016\/j.neuroimage.2009.01.051","journal-title":"NeuroImage"},{"issue":"12","key":"2617_CR19","doi-asserted-by":"publisher","first-page":"3713","DOI":"10.1109\/TBME.2021.3085523","volume":"68","author":"B Tunc","year":"2021","unstructured":"Tunc B, Hormuth D, Biros G, Yankeelov TE (2021) Modeling of Glioma growth with mass effect by longitudinal magnetic resonance imaging. IEEE Trans Biomed Eng 68(12):3713\u20133724. https:\/\/doi.org\/10.1109\/TBME.2021.3085523","journal-title":"IEEE Trans Biomed Eng"},{"issue":"1","key":"2617_CR20","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1109\/TBME.2011.2163406","volume":"59","author":"S Bauer","year":"2012","unstructured":"Bauer S, May C, Dionysiou D, Stamatakos G, Buchler P, Reyes M (2012) Multiscale modeling for image analysis of brain tumor studies. IEEE Trans Biomed Eng 59(1):25\u201329. https:\/\/doi.org\/10.1109\/TBME.2011.2163406","journal-title":"IEEE Trans Biomed Eng"},{"issue":"13","key":"2617_CR21","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1088\/0031-9155\/58\/13\/R97","volume":"58","author":"S Bauer","year":"2013","unstructured":"Bauer S, Wiest R, Nolte L-PP, Reyes M (2013) A survey of MRI-based medical image analysis for brain tumor studies. Phys Med Biol 58(13):97. https:\/\/doi.org\/10.1088\/0031-9155\/58\/13\/R97","journal-title":"Phys Med Biol"},{"issue":"5","key":"2617_CR22","doi-asserted-by":"publisher","first-page":"429","DOI":"10.3414\/ME11-02-0036","volume":"51","author":"A Mang","year":"2012","unstructured":"Mang A, Toma A, Schuetz TA, Becker S, Buzug TM (2012) A generic framework for modeling brain deformation as a constrained parametric optimization problem to aid non-diffeomorphic image registration in brain tumor imaging. Methods Inf Med 51(5):429\u2013440. https:\/\/doi.org\/10.3414\/ME11-02-0036","journal-title":"Methods Inf Med"},{"key":"2617_CR23","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1016\/j.cma.2018.12.008","volume":"347","author":"K Scheufele","year":"2019","unstructured":"Scheufele K, Mang A, Gholami A, Davatzikos C, Biros G, Mehl M (2019) Coupling brain-tumor biophysical models and diffeomorphic image registration. Comput Methods Appl Mech Eng 347:533\u2013567. https:\/\/doi.org\/10.1016\/j.cma.2018.12.008","journal-title":"Comput Methods Appl Mech Eng"},{"issue":"2","key":"2617_CR24","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1002\/mrm.20948","volume":"56","author":"M Descoteaux","year":"2006","unstructured":"Descoteaux M, Angelino E, Fitzgibbons S, Deriche R (2006) Apparent diffusion coefficients from high angular resolution diffusion imaging: estimation and applications. Magn Reson Med 56(2):395\u2013410. https:\/\/doi.org\/10.1002\/mrm.20948","journal-title":"Magn Reson Med"},{"key":"2617_CR25","unstructured":"Clayden JD, Deligianni F (2020) EEG, fMRI and NODDI dataset. https:\/\/osf.io\/94c5t\/"},{"key":"2617_CR26","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.neuroimage.2014.07.061","volume":"103","author":"B Jeurissen","year":"2014","unstructured":"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\u2013426. https:\/\/doi.org\/10.1016\/j.neuroimage.2014.07.061","journal-title":"NeuroImage"},{"issue":"4","key":"2617_CR27","doi-asserted-by":"publisher","first-page":"1459","DOI":"10.1016\/j.neuroimage.2007.02.016","volume":"35","author":"JD Tournier","year":"2007","unstructured":"Tournier JD, Calamante F, Connelly A (2007) Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35(4):1459\u20131472. https:\/\/doi.org\/10.1016\/j.neuroimage.2007.02.016","journal-title":"NeuroImage"},{"key":"2617_CR28","unstructured":"Dhollander T, Raffelt D, Connelly A (2016) Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion MR data without a co-registered T1 image Predicting stroke impairment using machine learning techniques View project A novel sparse partial correlation method fo. ISMRM Workshop on Breaking the Barriers of Diffusion MRI, vol 35, pp 1\u20132. https:\/\/www.researchgate.net\/publication\/307863133"},{"key":"2617_CR29","unstructured":"Tournier J-D, Calamante F, Connelly a (2010) Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions, vol\u00a018, 1670"},{"key":"2617_CR30","doi-asserted-by":"publisher","first-page":"116137","DOI":"10.1016\/j.neuroimage.2019.116137","volume":"202","author":"J-D Tournier","year":"2019","unstructured":"Tournier J-D, Smith R, Raffelt D, Tabbara R, Dhollander T, Pietsch M, Christiaens D, Jeurissen B, Yeh C-H, Connelly A (2019) MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation. NeuroImage 202:116137. https:\/\/doi.org\/10.1016\/j.neuroimage.2019.116137","journal-title":"NeuroImage"},{"key":"2617_CR31","doi-asserted-by":"publisher","unstructured":"Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL (2011) Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54(1):313\u2013327. https:\/\/doi.org\/10.1016\/j.neuroimage.2010.07.033","DOI":"10.1016\/j.neuroimage.2010.07.033"},{"key":"2617_CR32","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.neuroimage.2013.12.047","volume":"94","author":"T Dhollander","year":"2014","unstructured":"Dhollander T, Emsell L, Van Hecke W, Maes F, Sunaert S, Suetens P (2014) Track orientation density imaging (TODI) and track orientation distribution (TOD) based tractography. NeuroImage 94:312\u2013336. https:\/\/doi.org\/10.1016\/j.neuroimage.2013.12.047","journal-title":"NeuroImage"},{"issue":"8","key":"2617_CR33","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.1016\/j.acra.2005.04.018","volume":"12","author":"WL Nowinski","year":"2005","unstructured":"Nowinski WL, Belov D (2005) Toward atlas-assisted automatic interpretation of MRI morphological brain scans in the presence of tumor. Acad Radiol 12(8):1049\u20131057. https:\/\/doi.org\/10.1016\/j.acra.2005.04.018","journal-title":"Acad Radiol"},{"issue":"3","key":"2617_CR34","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1002\/hbm.10062www.fmrib.ox.ac.uk\/steve","volume":"17","author":"SM Smith","year":"2002","unstructured":"Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17(3):143\u2013155. https:\/\/doi.org\/10.1002\/hbm.10062www.fmrib.ox.ac.uk\/steve","journal-title":"Hum Brain Mapp"},{"key":"2617_CR35","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1016\/j.neuroimage.2019.06.039","volume":"200","author":"L Cordero-Grande","year":"2019","unstructured":"Cordero-Grande L, Christiaens D, Hutter J, Price AN, Hajnal JV (2019) Complex diffusion-weighted image estimation via matrix recovery under general noise models. NeuroImage 200:391\u2013404. https:\/\/doi.org\/10.1016\/j.neuroimage.2019.06.039","journal-title":"NeuroImage"},{"key":"2617_CR36","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/j.neuroimage.2016.08.016","volume":"142","author":"J Veraart","year":"2016","unstructured":"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\u2013406. https:\/\/doi.org\/10.1016\/j.neuroimage.2016.08.016","journal-title":"NeuroImage"},{"issue":"SUPPL. 1","key":"2617_CR37","doi-asserted-by":"publisher","first-page":"S208","DOI":"10.1016\/j.neuroimage.2004.07.051","volume":"23","author":"SM Smith","year":"2004","unstructured":"Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J, Zhang Y, De Stefano N, Brady JM, Matthews PM (2004) Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23(SUPPL. 1):S208\u2013S219. https:\/\/doi.org\/10.1016\/j.neuroimage.2004.07.051","journal-title":"NeuroImage"},{"issue":"2","key":"2617_CR38","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1016\/S1053-8119(02)91132-8https:\/\/pubmed.ncbi.nlm.nih.gov\/12377157\/","volume":"17","author":"M Jenkinson","year":"2002","unstructured":"Jenkinson M (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17(2):825\u2013841. https:\/\/doi.org\/10.1016\/S1053-8119(02)91132-8https:\/\/pubmed.ncbi.nlm.nih.gov\/12377157\/","journal-title":"NeuroImage"},{"key":"2617_CR39","unstructured":"Dhollander T, Mito R, Raffelt D, Connelly A (2019) Improved white matter response function estimation for 3-tissue constrained spherical deconvolution, vol 555"},{"key":"2617_CR40","doi-asserted-by":"crossref","unstructured":"Wadhwa A, Bhardwaj A, Singh Verma V (2019) A review on brain tumor segmentation of MRI images","DOI":"10.1016\/j.mri.2019.05.043"},{"key":"2617_CR41","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.patrec.2019.11.020","volume":"131","author":"A Tiwari","year":"2020","unstructured":"Tiwari A, Srivastava S, Pant M (2020) Brain tumor segmentation and classification from magnetic resonance images: review of selected methods from 2014 to 2019. Pattern Recognit Lett 131:244\u2013260. https:\/\/doi.org\/10.1016\/j.patrec.2019.11.020","journal-title":"Pattern Recognit Lett"},{"key":"2617_CR42","doi-asserted-by":"publisher","unstructured":"Beare R, Alexander B, Warren A, Kean M, Seal M, Wray A, Maixner W, Yang JY-M (2021) Karawun: assisting evaluation of advances in multimodal imaging for neurosurgical planning and intraoperative neuronavigation. medRxiv 2021.09.09.21262253. https:\/\/doi.org\/10.1101\/2021.09.09.21262253.https:\/\/www.medrxiv.org\/content\/10.1101\/2021.09.09.21262253v1. https:\/\/www.medrxiv.org\/content\/10.1101\/2021.09.09.21262253v1.abstract","DOI":"10.1101\/2021.09.09.21262253"},{"key":"2617_CR43","unstructured":"Mewes D, Tournier J-D, Picht T, Fekonja LS (2020) Implementation of OpenIGTLink tool in MRtrix3\u2019s mrview. https:\/\/zenodo.org\/record\/3755569"}],"updated-by":[{"DOI":"10.1007\/s11548-022-02667-3","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2022,5,26]],"date-time":"2022-05-26T00:00:00Z","timestamp":1653523200000}}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-022-02617-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-022-02617-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-022-02617-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,9]],"date-time":"2022-09-09T15:02:41Z","timestamp":1662735761000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-022-02617-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,25]]},"references-count":43,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["2617"],"URL":"https:\/\/doi.org\/10.1007\/s11548-022-02617-z","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s11548-022-02667-3","asserted-by":"object"}]},"ISSN":["1861-6429"],"issn-type":[{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,25]]},"assertion":[{"value":"13 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2022","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s11548-022-02667-3","URL":"https:\/\/doi.org\/10.1007\/s11548-022-02667-3","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant competing interests to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study was approved by UCL REC (ID2780\/003) and the UCL Institute of Child Health\/GOSH joint R &D office, reference 19NI12. Use of the data in Fig.  was approved under retrospective research ethics at the National Hospital for Neurology and Neurosurgery (University College London Hospitals NHS Foundation Trust). As no identifying information of any subject is present, there is no need for informed consent.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and Informed consent"}}]}}