White matter fiber tractography: why we need to move beyond DTI

Clinical article

Restricted access

Object

Diffusion-based MRI tractography is an imaging tool increasingly used in neurosurgical procedures to generate 3D maps of white matter pathways as an aid to identifying safe margins of resection. The majority of white matter fiber tractography software packages currently available to clinicians rely on a fundamentally flawed framework to generate fiber orientations from diffusion-weighted data, namely diffusion tensor imaging (DTI). This work provides the first extensive and systematic exploration of the practical limitations of DTI-based tractography and investigates whether the higher-order tractography model constrained spherical deconvolution provides a reasonable solution to these problems within a clinically feasible timeframe.

Methods

Comparison of tractography methodologies in visualizing the corticospinal tracts was made using the diffusion-weighted data sets from 45 healthy controls and 10 patients undergoing presurgical imaging assessment. Tensor-based and constrained spherical deconvolution–based tractography methodologies were applied to both patients and controls.

Results

Diffusion tensor imaging–based tractography methods (using both deterministic and probabilistic tractography algorithms) substantially underestimated the extent of tracks connecting to the sensorimotor cortex in all participants in the control group. In contrast, the constrained spherical deconvolution tractography method consistently produced the biologically expected fan-shaped configuration of tracks. In the clinical cases, in which tractography was performed to visualize the corticospinal pathways in patients with concomitant risk of neurological deficit following neurosurgical resection, the constrained spherical deconvolution–based and tensor-based tractography methodologies indicated very different apparent safe margins of resection; the constrained spherical deconvolution–based method identified corticospinal tracts extending to the entire sensorimotor cortex, while the tensor-based method only identified a narrow subset of tracts extending medially to the vertex.

Conclusions

This comprehensive study shows that the most widely used clinical tractography method (diffusion tensor imaging–based tractography) results in systematically unreliable and clinically misleading information. The higher-order tractography model, using the same diffusion-weighted data, clearly demonstrates fiber tracts more accurately, providing improved estimates of safety margins that may be useful in neurosurgical procedures. We therefore need to move beyond the diffusion tensor framework if we are to begin to provide neurosurgeons with biologically reliable tractography information.

Abbreviations used in this paper:AVM = arteriovenous malformation; CSD = constrained spherical deconvolution; DTI = diffusion tensor imaging; DWI = diffusion-weighted imaging; FA = fractional anisotropy; iPAT = integrated parallel acquisition technique; ROI = region of interest.

Article Information

Address correspondence to: Shawna Farquharson, M.Sc., Melbourne Brain Centre, 245 Burgundy Street, Melbourne 3084, Australia. email: s.farquharson@brain.org.au.

Please include this information when citing this paper: published online March 29, 2013; DOI: 10.3171/2013.2.JNS121294.

© AANS, except where prohibited by US copyright law.

Headings

Figures

  • View in gallery

    A: Schematic showing the 3 key steps required to perform tractography, with the resulting fiber tracks illustrated by a coronal section from a “whole-brain” tractography data set. The color of the fiber tracks indicates the local fiber orientation (red: left-right, green: dorsal-ventral, blue: cranial-caudal; see colored arrows). B: Simulation data showing a detailed illustration of Step 2 of Panel A. Schematic images of voxels (i) containing white matter fibers (single fiber populations, left and middle; 2 fiber populations, right) are shown with illustrations corresponding to each of these voxels, showing the DWI signal (ii), diffusion tensor ellipsoids derived from the DWI signal within each voxel (iii), and fiber orientations derived using CSD (iv). Note that in the voxel containing 2 fiber populations, the DTI model not only fails to represent the number of fiber populations within each voxel, but also does not provide an orientation estimate that corresponds to either of the constituent fiber populations. The CSD-based method correctly identifies 2 appropriately oriented fiber populations in the third voxel (see i).

  • View in gallery

    Coronal template FA image overlaid with seed and target regions defined in the right and left sensorimotor motor cortices (red and blue, respectively) and brainstem (yellow). These regions were warped back into each control participant's native space to perform all fiber tracking in native space (see Methods: Tractography).

  • View in gallery

    Comparison of control data tractography results obtained using seed and target ROIs identified in the brainstem and sensorimotor cortex, as shown in Fig. 2. For the individual subject data, the color of the fiber tracks indicates the local fiber orientation (with red indicating left-right, green indicating dorsal-ventral, and blue indicating cranial-caudal). A: Tractography results for DTI combined with a deterministic algorithm, DTI combined with a probabilistic algorithm, and CSD combined with a probabilistic algorithm. All data were acquired using 60 diffusion-weighted directions with b = 3000 sec/mm2. In each case, tracking was performed both from superior to inferior (left group of 6 images, labeled “motor to brain stem”) and from inferior to superior (right group of 6 images, labeled “brain stem to motor”). The upper row shows coronal T1-weighted images overlaid with tractography results from a representative normal control subject. The bottom row shows coronal FA template image overlaid with frequency maps representing the number of subjects from the 45 control subjects in which tracks were identified in any given voxel (range = 0–45). B: Tractography results using DTI combined with a deterministic algorithm and CSD combined with a probabilistic algorithm, across a range of acquisition protocols that differed in the number of diffusion directions and b-value used, as indicated in the figure. Panel B(i) shows coronal T1-weighted images overlaid with tractography results using DTI (left column) and CSD (right column) from a representative normal control subject. Panel B(ii) shows a coronal FA template image overlaid with frequency maps representing the number of subjects (out of 12 control subjects) in whom tracks were identified in any given voxel, using DTI (left column) and CSD (right column) (range 0–12).

  • View in gallery

    Coronal FA images overlaid with fiber tracking results using CSD (left) and DTI (right) from a representative healthy control subject. The magnified regions in the orange boxes show the fiber orientation estimates within individual voxels. The CSD fiber orientation estimates (upper left image) confirm the presence of many voxels containing multiple fiber orientations, within individual voxels. The tensor-derived orientation in equivalent voxels (upper right image) does not represent any of the constituent fiber populations in regions where the DTI-based tractography method failed to produce tracks.

  • View in gallery

    Results of DTI-based tractography (blue) and CSD-based tractography (red) (derived from the same DWI data set) with segmented pathology volumes (green) overlaid on coronal T1-weighted images for Case A (A) and Case B (B). Case A involved a 49-year-old woman with a large left-sided temporoparietal AVM. See MR images in Panel A(v). Case B involved a 24-year-old woman with a right focal cortical dysplasia situated in the right posterior frontal lobe. See MR images in Panel B(v). The DTI-based tractography results in Case A suggest a clear margin surrounding the lesion (i and ii), whereas the CSD-based tractography results indicate that lateral projections of the corticospinal pathway may be at risk (iii and iv). The DTI-based tractography results in Case B suggest that only the medial aspect of the lesion impinges on the corticospinal tracts (i and ii), whereas the CSD-based tractography results suggest that the lesion is enveloped by medial and lateral projections of corticospinal fibers (iii and iv).

  • View in gallery

    Results of DTI-based tractography (i) and CSD-based tractography (ii) for Cases C–J with segmented pathology volumes (green) overlaid on coronal T1-weighted images. The panel labels correspond to the cases listed in Table 1. Note: The difference in apparent safe margins of resection is substantially greater using the DTI-based tractography method (blue) compared with the CSD-based tractography method (red).

  • View in gallery

    Dorsal views of the right pyramidal tract. A: Coronal plane [sketch] depicting the pyramidal tract of the right hemisphere (dorsal view) and its landmarks (1 = wall of the lateral ventricle, 2 = upper circular [periinsular] sulcus, 3 = the cingular sulcus). B: Specimen of the right pyramidal tract (dorsal view) and its landmarks (lateral wall of the lateral ventricle, superior circular sulcus). C: Tractography results using CSD and a probabilistic algorithm. D: Tractography results using DTI and a deterministic algorithm. Panels A and B are reprinted from Ebeling U, Reulen HJ: Subcortical topography and proportions of the pyramidal tract. Acta Neurochir (Wien) 118:164–171, 1992 (Figs. 1 and 5), with kind permission from Springer Science and Business Media.

References

1

Alexander ALHasan KMLazar MTsuruda JSParker DL: Analysis of partial volume effects in diffusion-tensor MRI. Magn Reson Med 45:7707802001

2

Anderson AW: Measurement of fiber orientation distributions using high angular resolution diffusion imaging. Magn Reson Med 54:119412062005

3

Assaf YFreidlin RZRohde GKBasser PJ: New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter. Magn Reson Med 52:9659782004

4

Avants BBEpstein CLGrossman MGee JC: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 12:26412008

5

Basser PJ: Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR Biomed 8:3333441995

6

Basser PJMattiello JLeBihan D: MR diffusion tensor spectroscopy and imaging. Biophys J 66:2592671994

7

Behrens TEBerg HJJbabdi SRushworth MFWoolrich MW: Probabilistic diffusion tractography with multiple fibre orientations: what can we gain?. Neuroimage 34:1441552007

8

Berman JIBerger MSChung SNagarajan SSHenry RG: Accuracy of diffusion tensor magnetic resonance imaging tractography assessed using intraoperative subcortical stimulation mapping and magnetic source imaging. J Neurosurg Pediatr 107:4884942007

9

Berman JIBerger MSMukherjee PHenry RG: Diffusiontensor imaging-guided tracking of fibers of the pyramidal tract combined with intraoperative cortical stimulation mapping in patients with gliomas. J Neurosurg 101:66722004

10

Bozzao ARomano AAngelini AD'Andrea GCalabria LFCoppola V: Identification of the pyramidal tract by neuronavigation based on intraoperative magnetic resonance tractography: correlation with subcortical stimulation. Eur Radiol 20:247524812010

11

Ciccarelli OParker GJMToosy ATWheeler-Kingshott CAMBarker GJBoulby PA: From diffusion tractography to quantitative white matter tract measures: a reproducibility study. Neuroimage 18:3483592003

12

Clark CABarrick TRMurphy MMBell BA: White matter fiber tracking in patients with space-occupying lesions of the brain: a new technique for neurosurgical planning?. Neuroimage 20:160116082003

13

Ebeling UReulen HJ: Subcortical topography and proportions of the pyramidal tract. Acta Neurochir (Wien) 118:1641711992

14

Fernandez-Miranda JCPathak SEngh JJarbo KVerstynen TYeh FC: High-definition fiber tractography of the human brain: neuroanatomical validation and neurosurgical applications. Neurosurgery 71:4304532012

15

Frank LR: Characterization of anisotropy in high angular resolution diffusion-weighted MRI. Magn Reson Med 47:108310992002

16

Heiervang EBehrens TEJMackay CERobson MDJohansen-Berg H: Between session reproducibility and between subject variability of diffusion MR and tractography measures. Neuroimage 33:8678772006

17

Holodny AIOllenschleger MDLiu WCSchulder MKalnin AJ: Identification of the corticospinal tracts achieved using blood-oxygen-level-dependent and diffusion functional MR imaging in patients with brain tumors. AJNR Am J Neuroradiol 22:83882001

18

Itoh DAoki SMaruyama KMasutani YMori HMasumoto T: Corticospinal tracts by diffusion tensor tractography in patients with arteriovenous malformations. J Comput Assist Tomogr 30:6186232006

19

Jansons KMAlexander DC: Persistent angular structure: new insights from diffusion magnetic resonance imaging data. Inf Process Med Imaging 18:6726832003

20

Jeurissen BLeemans ATournier JDJones DKSijbers J: Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Hum Brain Mapp [epub ahead of print]2012

21

Jones DK: Tractography gone wild: probabilistic fibre tracking using the wild bootstrap with diffusion tensor MRI. IEEE Trans Med Imaging 27:126812742008

22

Jones DKHorsfield MASimmons A: Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magn Reson Med 42:5155251999

23

Kinoshita MYamada KHashimoto NKato AIzumoto SBaba T: Fiber-tracking does not accurately estimate size of fiber bundle in pathological condition: initial neurosurgical experience using neuronavigation and subcortical white matter stimulation. Neuroimage 25:4244292005

24

Maruyama KKamada KShin MItoh DMasutani YIno K: Optic radiation tractography integrated into simulated treatment planning for Gamma Knife surgery. J Neurosurg 107:7217262007

25

Mikuni NOkada TEnatsu RMiki YHanakawa TUrayama S: Clinical impact of integrated functional neuronavigation and subcortical electrical stimulation to preserve motor function during resection of brain tumors. J Neurosurg 106:5935982007

26

Mori SCrain BJChacko VPvan Zijl PC: Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 45:2652691999

27

Mori Svan Zijl PCM: Fiber tracking: principles and strategies— a technical review. NMR Biomed 15:4684802002

28

Newton JMWard NSParker GJMDeichmann RAlexander DCFriston KJ: Non-invasive mapping of corticofugal fibres from multiple motor areas—relevance to stroke recovery. Brain 129:184418582006

29

Nimsky CGanslandt OFahlbusch R: Implementation of fiber tract navigation. Neurosurgery 58:4 Suppl 2ONS292ONS3042006

30

Okada TMiki YKikuta KMikuni NUrayama SFushimi Y: Diffusion tensor fiber tractography for arteriovenous malformations: quantitative analyses to evaluate the corticospinal tract and optic radiation. AJNR Am J Neuroradiol 28:110711132007

31

Özarslan EMareci TH: Generalized diffusion tensor imaging and analytical relationships between diffusion tensor imaging and high angular resolution diffusion imaging. Magn Reson Med 50:9559652003

32

Parker GJMHaroon HAWheeler-Kingshott CAM: A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements. J Magn Reson Imaging 18:2422542003

33

Romano AD'Andrea GMinniti GMastronardi LFerrante LFantozzi LM: Pre-surgical planning and MR-tractography utility in brain tumour resection. Eur Radiol 19:279828082009

34

Sanai NBerger MS: Glioma extent of resection and its impact on patient outcome. Neurosurgery 62:7537642008

35

Tournier JDCalamante FConnelly A: How many diffusion gradient directions are required for HARDI?. Proc Intl Soc Mag Reson Med 17:3582009. (Abstract)

36

Tournier JDCalamante FConnelly A: MRtrix: diffusion tractography in crossing fibre regions. Int J Imaging Syst Technol 22:53662012

37

Tournier JDCalamante FConnelly A: Robust determination of the fibre orientation distribution in diffusion MRI: nonnegativity constrained super-resolved spherical deconvolution. Neuroimage 35:145914722007

38

Tournier JDCalamante FGadian DGConnelly A: Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage 23:117611852004

39

Tournier JDMori SLeemans A: Diffusion tensor imaging and beyond. Magn Reson Med 65:153215562011

40

Tournier JDYeh CHCalamante FCho KHConnelly ALin CP: Resolving crossing fibres using constrained spherical deconvolution: validation using diffusion-weighted imaging phantom data. Neuroimage 42:6176252008

41

Tuch DS: Q-ball imaging. Magn Reson Med 52:135813722004

42

Tuch DSReese TGWiegell MRMakris NBelliveau JWWedeen VJ: High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn Reson Med 48:5775822002

43

Wedeen VJHagmann PTseng WYReese TGWeisskoff RM: Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn Reson Med 54:137713862005

44

Yamada KKizu OIto HNishimura T: Tractography for an arteriovenous malformation. Neurology 62:6692004

45

Yushkevich PAPiven JHazlett HCSmith RGHo SGee JC: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31:111611282006

TrendMD

Metrics

Metrics

All Time Past Year Past 30 Days
Abstract Views 107 107 47
Full Text Views 556 556 55
PDF Downloads 109 109 20
EPUB Downloads 0 0 0

PubMed

Google Scholar