In this work we provide a preliminary assessment of the reproducibility of the Neurite Orientation Dispersion and Density Imaging (NODDI), a recent diffusion MRI technique for directly quantifying microstructural indices of neurites in vivo, in the human brain. It is important to assess the reproducibility of such a technique to verify the precision of the method, which has implications for translation to clinical studies. NODDI outputs indices which reflect the functional and computational complexity of various regions of the brain and thus can provide useful information, non-invasively, for understanding pathology of the brain. We compare the parameter maps derived from diffusion MRI data acquired using the NODDI protocol from a normal subject, at two separate imaging sessions. We show that the NODDI indices have reproducibility comparable to that of the DTI indices. We additionally show that the clinically feasible NODDI protocol maintains good reproducibility of parameter estimates, comparable to that of a more comprehensive protocol

Alexander, DC

Schneider, T

Tariq, M

Wheeler-Kingshot, C

Zhang, H

UCL Discovery

TARIQ et al.: SCAN-RESCAN REPRODUCIBILITY OF NODDI 1Scan-rescan reproducibility of neuritemicrostructure estimates using NODDIMaira Tariq1maira.tariq.11@ucl.ac.ukTorben Schneider2t.schneider@ucl.ac.ukDaniel C. Alexander1d.alexander@cs.ucl.ac.ukClaudia A.M. Wheeler-Kingshott2c.wheeler-kingshott@ucl.ac.ukHui Zhang1g.zhang@cs.ucl.ac.uk1 Department of Computer Science &Centre for Medical Image ComputingUniversity College LondonLondon, United Kingdom2 NMR Research Unit, Department ofNeuro-inflammationUCL Institute of NeurologyUniversity College LondonLondon, United KingdomAbstractIn this work we provide a preliminary assessment of the reproducibility of the NeuriteOrientation Dispersion and Density Imaging (NODDI), a recent diffusion MRI techniquefor directly quantifying microstructural indices of neurites in vivo, in the human brain.It is important to assess the reproducibility of such a technique to verify the precision ofthe method, which has implications for translation to clinical studies. NODDI outputsindices which reflect the functional and computational complexity of various regionsof the brain and thus can provide useful information, non-invasively, for understandingpathology of the brain. We compare the parameter maps derived from diffusion MRIdata acquired using the NODDI protocol from a normal subject, at two separate imagingsessions. We show that the NODDI indices have reproducibility comparable to that of theDTI indices. We additionally show that the clinically feasible NODDI protocol maintainsgood reproducibility of parameter estimates, comparable to that of a more comprehensiveprotocol.1 IntroductionNODDI is a practical diffusion MRI technique, introduced in [15], for estimating the in-tegrity of the axons and dendrites, collectively known as neurites, in the human brain. Den-drites and axons are the extensions from the neural cell bodies, which have a vital role in thecommunication network of the brain. The morphology and complexity of these structureshave been shown to indicate the function as well as pathology of the brain[8, 9, 10]. Forexample dispersion of dendrites is linked with brain development [6], and their density withageing [9]. Thus by quantifying the physical characteristics of neurites, promising markersof progression of brain diseases can be obtained and used in the diagnosis, prognosis andtreatment of brain disorders.c© 2012. The copyright of this document resides with its authors.It may be distributed unchanged freely in print or electronic forms.2 TARIQ et al.: SCAN-RESCAN REPRODUCIBILITY OF NODDINeurite density and their orientation dispersion are the two microstructural parametersestimated by NODDI. This is in contrast to Diffusion Tensor Imaging (DTI) [4], the standardclinical diffusion MRI technique, which outputs Fractional Anisotropy (FA) and Mean Diffu-sivity (MD). Although both NODDI and DTI probe microstructure from the diffusion of thewater molecules within the brain, DTI is limited in its ability to provide information aboutthe specific changes in microstructure [12]; the indices obtained are affected simultaneouslyby a number of microstructural changes (e.g. demyelination, inflammation, axonal loss,gliosis), which give rise to the same alterations of their values. NODDI on the other handestimates the microstructure directly, using an analytical model relating these parameters tothe diffusion MRI signal. The NODDI parameters have been shown in [15] to disentan-gle the microstructural indices which contribute to the changes in FA and thus can provideinformation about specific changes in microstructure for particular pathologies.NODDI is underpinned by a multi-compartment signal model of tissue diffusion, whichdescribes analytically how presence of certain structures within the brain affects the diffusionMR signal. Numerous similar techniques exist to infer microstructural features directly (See[3] for a review), but differ from NODDI in the features they model (mostly model onlywhite matter), as well as how they are modelled. But the main limitation of the other modelsis that they are not clinically feasible. The original work [15] shows results which validateand evaluate the accuracy as well as clinical viability of NODDI, but the precision of theparameters estimated, over multiple imaging sessions, is not explored.In this work we evaluate the precision of the NODDI by assessing its reproducibility forestimating the microstructural parameters. Such an assessment is important for justifying theuse of this technique in clinical and research studies. This is especially true for longitudinalstudies since the variability in the parameters needs to be purely due to the changes in brainmicrostructure.The paper is organised as follows: Section 2 details the tissue model adapted by NODDI,the data acquisition and the fitting procedure; Section 3 contains the results; Section 4 eval-uates and summarises the key findings and discusses future work.2 Materials and MethodsNODDI combines a three-compartment tissue model with a two-shell high-angular-resolutiondiffusion imaging (HARDI) protocol, optimised for clinical feasibility. The following sec-tions summarise the NODDI model used to infer the microstructure indices, as well as theprotocols used to acquire the images.2.1 NODDI tissue ModelNODDI is an analytical tissue model, which enables differentiation of three microstructuralenvironments: Intracellular (IC), Extracellular (EC) and CerebroSpinal Fluid (CSF). Eachenvironment affects the diffusion of water molecules within and around them in a charac-teristic manner and thus contributes distinguishingly to the normalised MR signal, obtainedfrom diffusion imaging. The full normalised signal in a NODDI model is formed from con-tributions from all of these environments, as shown in the following equation:A= (1−νiso)[νicAic+(1−νic)Aec]+νisoAiso (1)TARIQ et al.: SCAN-RESCAN REPRODUCIBILITY OF NODDI 3The intracellular compartment, representing the neurites, has a neurite density givenby the volume fraction νic, and contributing diffusion signal Aic. Axons and dendrites arestructurally similar to cylinders, and are modelled for simplicity as sticks, i.e. zero radiuscylinders. This reflects the highly restricted diffusion perpendicular to and unhindered dif-fusion along the length of the structures. The IC diffusion signal also incorporates the ori-entation dispersion of the neurites, utilising a Watson Distribution [11]. Watson distributionis described by κ , which measures the extent of orientation dispersion about µ , the dom-inant orientation. The orientation dispersion index (ODI), output by NODDI, is definedas in equation (2) and ranges from 0, for coherently oriented structures, to 1 for isotropicstructures.OD= (2/pi)arctan(1/κ) (2)The glial cells and, in grey matter, the cell bodies make up the extracellular compartmentof NODDI. Diffusion of molecules in this compartment is hindered by the presence of neu-rites, and is thus modelled by Gaussian anisotropic diffusion, i.e. a cylindrically symmetrictensor.The final compartment models the CSF as isotropic Gaussian diffusion.2.2 Data AcquisitionDiffusion MR data was acquired on a Philips 3T system with maximum gradient strengthof 60mT/m. One healthy volunteer was scanned twice over two sessions separated by 4weeks. Same imaging protocol was used as the original study [15], which was optimisedusing the technique described in [1], with a constraint of 30 mins on acquisition time andmaximum gradient strength of 60 mT/m. This optimised NODDI protocol consists of a711s/mm2 shell with 30 gradient directions and a 2855s/mm2 shell with 60 directions, and9 b=0 measurements. The NODDI protocol is clinically feasible as it takes less than 30mins to acquire. A more comprehensive 4-shell protocol was utilised to provide a pseudo-gold-standard to assess the reproducibility of the NODDI protocol against and determinethe effect of acquiring fewer shells on the reproducibility of the parameters estimated. Anadditional 1000s/mm2 shell of 30 directions and a 2000s/mm2 shell of 60 directions werethus acquired and combined with the NODDI protocol to form this comprehensive protocol.The results from the two protocols were compared to assess the precision in estimation ofthe indices, when utilising more clinically feasible protocol.2.3 ProcessingThe model was fitted to the acquired data using the procedure described in [2], withoutthe MCMC procedure, for computational efficiency [15]. The parameter maps obtainedwere subsequently aligned using DTI-TK [13] to enable voxel-wise assessment of parameterreproducibility. To assess the parameters’ reliably, the maps were segmented into WhiteMatter (WM), Grey Matter (GM) and CSF regions, using MD and linearity measure [14] ofthe diffusion tensor. CSF was segmented as areas where MD was higher than 80%, WM aslinearity higher than 0.2 and GM as rest of the brain regions.4 TARIQ et al.: SCAN-RESCAN REPRODUCIBILITY OF NODDIFA	  0 1MD	  0 2e-­‐9	  ODI	  0 1νIC	  0 1νISO	  0 1Scan	  1	  Scan	  2	  Scan	  1	  Scan	  2	  4-Shell protocols 2-Shell NODDI  protocols Figure 1: Scan-rescan parameter maps obtained for mid-axial slice, for DTI (first twocolumns) and NODDI (last three columns), using 4-Shell (first two rows) and NODDI (lasttwo rows) protocolsFigure 2: Scatter plots of scan-rescan for WM voxels, for DTI indices (top row) and NODDIparameters (bottom row). R values shown in plots represent the correlation coefficient forthe two scans, for each parameterTARIQ et al.: SCAN-RESCAN REPRODUCIBILITY OF NODDI 5Figure 3: Scatter plots of scan-rescan for GM voxels, for DTI indices (top row) and NODDIparameters (bottom row). R values shown in plots represent the correlation coefficient forthe two scans, for each parameter3 ResultsFigure 1, using an exemplar slice, illustrates qualitatively the scan-rescan reproducibilityof the NODDI parameters in comparison to the standard DTI indices. The scatter plotsin Figures 2 and 3 demonstrate this over the whole brain and separately for WM and GMregions. The quantitative assessment of the reproducibility using the correlation coefficientsbetween the scan-rescan parameters are overlaid on the scatter plots.These results show that NODDI is able to estimate the microstructure indices of neuriteswith a high reproducibility, comparable to DTI’s ability to reproduce its less specific indices.The comparison with the results from the 4-shell protocol demonstrates additionally thatthe much more economical NODDI protocol does not result in a loss of the scan-rescanreproducibility, while allowing the acquisition time to be reduced to less than 30 mins.4 DiscussionWe set out to evaluate the reproducibility of NODDI, which is evident from the results pre-sented in this work. We demonstrate that NODDI outputs precise estimates of brain mi-crostructure, utilising the optimised protocol. The accuracy of NODDI parameters, usingthese protocols, has been established in [15]. Thus NODDI has the potential to help advancethe research for understanding brain microstructure and developing normal brain atlases, aswell as ones for specific pathologies.However, just like DTI, NODDI parameters are also affected by drift in the scanner. Aslight upward bias in the estimation of νic can be seen in the scatter plots, by the shift of thedata points above the line of equality. This can be attributed to the drift in intensities betweenthe two scans and corresponds to the visible downward bias in the MD scatter plots (lowerMD values are associated with IC regions, resulting in a higher νic in those regions).6 TARIQ et al.: SCAN-RESCAN REPRODUCIBILITY OF NODDIThe correlation between the two estimates of ODI in GM is particularly low, especiallycompared to that in WM. This is because the dendrites have a high orientation dispersion,which leads to high ODI values that are more difficult to estimate precisely, as noted in theoriginal work [15].The acquisition time for NODDI protocol can potentially be reduced to as little as 10mins by utilising fewer gradient directions in the shells, with no significant loss in accuracyof the estimates (see [15]). The reproducibility of parameters estimates using these NODDIsub-shells will be part of a more comprehensive reproducibility study, in future.The results obtained show tremendous promise for utilisation of NODDI as a techniqueto accurately and precisely estimate microstructure. However further work is required toestablish it as a clinical diagnostic tool. This would include a region based quantification ofthe error associated with the estimated parameters (similar to [5, 7]), which will determinethe ability of the method to distinguish a patient image from that of a normal subject.4.1 Future workFuture extensions to the work will include a more comprehensive evaluation of reproducibil-ity of the NODDI technique and the optimised protocol, using more subjects. To evaluate themodel’s use as diagnostic tool for certain brain disorders, it will be important to compare theprecision of NODDI parameters, with the expected differences between normal and patientbrain.The NODDI model currently captures the neurite density by a symmetric Watson model.A more comprehensive Bingham model will be incorporated in NODDI which accountsfor more realistic dispersion in orientation of neurites. The NODDI model will also beimplemented and available for open access within the Camino framework.4.2 AcknowledgementsThis work is supported by the future and emerging technologies (FET) program of the EUFP7 framework through the CONNECT consortium, and the MS society of Great Britainand Northern Ireland, the ISRT and the CBRC. DCA is additionally funded by ESPRC undergrant EP/E007748.References[1] D. C. Alexander. A general framework for experiment design in diffusion MRI and itsapplication in measuring direct tissue-microstructure features. Magnetic Resonance inMedicine, 60:439–448, 2008.[2] D. C. Alexander, P. L. Hubbard, M. G. Hall, E. A. Moore, M. Ptito, G. J. M. Parker,and T. B. Dyrby. Orientationally invariant indices of axon diameter and density fromdiffusion MRI. NeuroImage, 52:1374–1389, 2010.[3] Y. Assaf and Y Cohen. Inferring microstructural information of white matter formdiffuion MRI. In H. Johansen-Berg and T. E. J. Behrens, editors, Diffusion MRI: fromquantitative measurement to in vivo neuroanatomy, pages 127–146. Academic Press,2009.TARIQ et al.: SCAN-RESCAN REPRODUCIBILITY OF NODDI 7[4] P. J. Basser, J. Matiello, and D. Le Bihan. MR diffusion tensor spectroscopy and imag-ing. Biophysical Journal, 66:259–267, 1994.[5] D. Bonekamp, L. M. Nagae, M. Degaonkar, M. Matson, W. M. A. Abdalla, P. B. Barker,S. Mori, and A. HorskG˘. Diffusion tensor imaging in children and adolescents: Re-producibility, hemispheric, and age-related differences. NeuroImage, 34(2):733–742,2007.[6] J. L. Conel. The postnatal development of the human cerebral cortex. Harvard Univer-sity Press, Cambridge, USA, 1939.[7] L. E. Danielian, N. K. Iwata, D. M. Thomasson, and M. K. Floeter. Reliability of fibertracking measurements in diffusion tensor imaging for longitudinal study. NeuroImage,49(2):1572–80, 2010.[8] J.C. Fiala, J. Spacek, and K.M. Harris. Review: Dendritic spine pathology: Cause orconsequence of neurological disorders. Brain Research Reviews, 39:29–54, 2002.[9] B. Jacobs, L. Driscoll, and M. Schall. Life-span dendritic and spine changes in areas 10and 18 of human cortex: A quantitative golgi study. Journal of comparative neurology,386:661–680, 1997.[10] B. Jacobs, M. Schall, M. Prather, E. Kapler, L. Driscoll, S. Baca, J. Jacobs, K. Ford,M. Wainwright, and M. Treml. Regional dendritic and spine variation in human cere-bral cortex: a quantitative golgi study. Cerebral Cortex, 11:558–571, 2001.[11] K.V Mardia and P.E Jupp. Directional statistics. Wiley series in probability and statis-tics, 1990.[12] C. Pierpaoli, P. Jezzard, P. J. Basser, A. Barnett, and G. Di Chiro. Diffusion tensor MRimaging of the human brain. Radiology, 201:637–648, 1996.[13] Diffusion Tensor Imaging ToolKit. January 2012. URL http://dti-tk.sourceforge.net.[14] C. F Westin, S. E. Maier, H. Mamata, A. Nabavi, F. A. Jolesz, and R Kikinis. Process-ing and visualization for diffusion tensor MRI. Medical Image Analysis, 6(2):93–108,2002.[15] H. Zhang, T. Schneider, C.A. Wheeler-Kingshott, and D.C. Alexander. NODDI: Prac-tical in vivo neurite orientation dispersion and density imaging of the human brain.NeuroImage, 61:1000–1016, 2012.

Scan-rescan reproducibility of neurite microstructure estimates using NODDI

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Scan-rescan reproducibility of neurite microstructure estimates using NODDI

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