Tracking Fiber Structures
Sonia Marie-Aurore Pujol1

1Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States

Synopsis

Diffusion MRI tractography enables unprecedented visualization of the trajectory of white matter pathways in vivo. This course will introduce the fundamental principles of tracking fiber structures in diffusion MRI data, and will provide an overview of different tractography methods. Participants will learn about the current capabilities and limitations of tractography techniques for investigating white matter anatomy. Clinical applications of tractography will be presented and challenges of using tractography findings for clinical decision support will be discussed.

Highlights

  • Diffusion MRI tractography provides non-invasive mapping of white matter pathways.
  • Awareness of the capabilities and limitations of fiber tracking algorithms is essential for interpreting tractography results.
  • Future refinement of tractography techniques will continue to advance knowledge on white matter organization in health and disease.

Brain Wiring

The cerebral white matter is an intricated network of myelinated axonal fibers that connect cortical and subcortical gray matter regions, and enable rapid transfer of information between different parts of the brain. The origin, trajectory and destination of white matter tracts subserve specific brain function. Thus, knowledge of white matter organization can advance understanding of the human brain and help improve neurological patient care. However, structural MRI scans lack the contrast to allow differentiation of fibers connecting different brain regions. Most of the current knowledge on the neuroanatomy of the human brain is derived from post-mortem dissection.

Diffusion MRI

The advent of diffusion MRI (dMRI) and the subsequent development of Diffusion Tensor Imaging (DTI) have opened up the first non-invasive window on the organization of the cerebral white matter in vivo (1-3). Diffusion MRI provides a unique contrast that is sensitive to the diffusion profile of water molecules in the brain. In gray matter and cerebrospinal fluid, the diffusion is isotropic. In white matter, as the axonal membranes and myelin sheets act as barriers, water molecules diffuse predominantly in the direction parallel to the fibers and the diffusion is anisotropic. Thus, by mapping the displacement of water molecules in the brain, diffusion MRI provides an indirect probe of the organization of human brain tissues. From the diffusion tensor model to sophisticated multi-fiber approaches, numerous mathematical models have been developed to recover the orientation of fiber populations from diffusion MRI data (3-4). Such orientational information can be used to reconstruct the trajectory of white matter bundles using fiber tracking techniques referred to as tractography.

Tractography

By propagating streamlines using the local fiber orientation derived from the main eigenvector of the diffusion tensor, tractography algorithms developed in the late nineties enabled the first three-dimensional representation of white matter pathways in the human brain (5-10). Tractography reconstructions have provided unprecedented visualization of the trajectory of major white matter fascicles (11). In the past two decades, the complexity of dMRI data has fostered the development of a wide variety of computational approaches for tracing white matter pathways. Numerous deterministic, probabilistic and global tractography algorithms have been developed (12).

The diversity of tractography methods represents a wealth of technical resources to advance understanding of the architecture of white matter in health and disease. New insights on brain connectivity through collaborative efforts such as the Human Connectome Project and the FP7 Connect project are likely to accelerate the path of scientific discovery (13,14). Tractography is gaining increasing interest in the clinics due to the availability of dMRI sequences on clinical MRI scanners and the dissemination of fiber tracking tools as open-source packages or commercial software. Tractography holds great promise to advance clinical treatment of brain diseases including neurological, neurodegenerative and psychiatric disorders (15). In neurosurgical intervention, tractography reconstructions have the potential to provide clinically relevant information on perilesional tracts involved in motor, visual or language function (16).

However, tractography remains a clinical research tool based on complex data acquisition and geometrical models that rely on many assumptions. Tracking white matter pathways through complex anatomical regions where fibers cross, bend or fan as well as in pathological regions with altered diffusion properties is a difficult task (17,18). The technical limitations of tractography tools can lead to false-negative and false-positive tracts, which make the interpretation of diffusion MRI tractography findings difficult. Different tractography methods can produce very different results on healthy subjects data (19-21) as well as on datasets acquired on brain tumor patients (22). In that context, clinicians face the challenge of selecting the appropriate tractography method and tract tracing parameters. To address this issue, the DTI Challenge Working Group has been initiated as a community-based effort to provide standardized evaluation of tractography algorithms on clinical diffusion MRI data, and help define guidelines and best practices on the use of tractography for neurosurgical decision making (23). Diffusion MRI tractography is a powerful brain mapping tool that combines MRI physics, applied mathematics, computational neuroscience and biomedical engineering. When integrated with functional MRI and direct electrical stimulation, tractography could help understand the link between structure and function (24,25). Joint multidisciplinary efforts with experts from neuroanatomy, neurosurgery, neuroradiology and neurobiology will help research scientists push the limits of tractography and advance knowledge on the connectivity of the human brain.

Learning Objectives

Upon completion of the course, participants will be able to:

  • understand the principles of tracking fiber structures in diffusion MRI data,
  • describe the characteristics of deterministic, probabilistic and global tractography algorithms,
  • learn about white matter tractography applications for the study of brain disorders,
  • assess the current capabilities and limitations of tractography for mapping white matter pathways.

Target Audience

The course is intended for research scientists, clinicians and research fellows interested in investigating the architecture of white matter pathways using diffusion MRI tractography.

Acknowledgements

Neuroimage Analysis Center (NIH P41RR013218)

References

1. LeBihan D, Breton E, Lallemand D, et al. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 1986; 161:401-7.

2. Chenevert TL, Brunberg JA, Pipe JG. Anisotropic diffusion in human white matter: demonstration with MR techniques in vivo. Radiology 1990; 177(2):401–405.

3. Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophys J. 1994; 66:259-67.

4. Seunarine KK, and Alexander DC. Multiple fibers: beyond the diffusion tensor. Diffusion MRI: From quantitative measurement to in-vivo neuroanatomy. Eds. Behrens TE and Johansen-Berg H. Elsevier 2009.

5. Mori S, Crain BJ, Chacko VP, van Zijl PC. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 1999; 45:265–9.

6. Conturo TE, Lori NF, Cull TS, et al. Tracking neuronal fiber pathways in the living human brain. Proceedings of the National Academy of Sciences of the United States of America 1999; 96(18):10422-10427.

7. Basser PJ, Pajevic S, Pierpaoli C, Duda J, Aldroubi A. In vivo fiber tractography using DT-MRI data. Magn. Reson. Med. 2000; 44:625–32.

8. Jones DK, Simmons A, Williams SC, Horsfield MA. Non-invasive assessment of axonal fiber connectivity in the human brain via diffusion tensor MRI. Magn. Reson. Med. 1999; 42:37–41.

9. Stieltjes B, Kaufmann WE, van Zijl PCM, Fredericksen K, Pearlson GD, Mori S. Diffusion tensor imaging and axonal tracking in the human brainstem. NeuroImage 2001; 14: 723–735.

10. Poupon C, Clark CA, Frouin V, Régis J, Bloch I, Le Bihan D, Mangin J. Regularization of diffusion-based direction maps for the tracking of brain white matter fascicles. Neuroimage 2000 Aug; 12(2):184-95.

11. Catani M, Howard RJ, Pajevic S, Jones DK. Virtual in vivo interactive dissection of white matter fasciculi in the human brain. Neuroimage 2002; 17:77–94.

12. MR Diffusion Tractography. Behrens TE, Sotiropoulos SN, Jbabdi S. Diffusion MRI: From quantitative measurement to in-vivo neuroanatomy. Eds. Behrens TE and Johansen-Berg H. Elsevier 2009.

13. Setsompop K, Kimmlingen R, Eberlein E, et al. Pushing the limits of in vivo diffusion MRI for the Human Connectome Project. NeuroImage 2013; 80:220-233.

14. Assaf Y, Alexander DC, Jones DK, Bizzi A, Behrens TE, Clark CA, Cohen Y, Dyrby TB, Huppi PS, Knoesche TR et al. The CONNECT project: combining macro- and micro-structure. Neuroimage 2013; 80: 273–829.

15. Le Bihan D, Johansen-Berg H. Diffusion MRI at 25: exploring brain tissue structure and function. Neuroimage 2012; 61(2):324–41.

16. Pujol S. Imaging White Matter Anatomy for Brain Tumor Surgery. In: Image-Guided Neurosurgery, Alexandra Golby Editor. 1st London, UK: Academic Press; 2015.

17. Jones DK. Challenges and limitations of quantifying brain connectivity in vivo with diffusion MRI. Imaging in Medicine. 2010; 2(3):341-355.

18. Nimsky C, Bauer M, Carl B. Merits and Limits of Tractography Techniques for the Uninitiated. Adv Tech Stand Neurosurg. 2016; (43):37-60.

19. Pujol S, Westin CF, Whitaker R, Gerig G, Fletcher T, Magnotta V, Bouix S, Kikinis R, Wells WM, Gollub R. Preliminary results on the use of STAPLE for evaluating DT-MRI tractography in the absence of ground truth. Proceedings of the 17th Scientific Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2009); Apr 18-24, 2009; Honolulu, Hawaii.

20. Feigl GC, Hiergeist W, Fellner C, Schebesch KMM, Doenitz C, Finkenzeller T, et al. Magnetic resonance imaging diffusion tensor tractography: Evaluation of anatomic accuracy of different fiber tracking software packages. World Neurosurg. 2014; 81: 144–150.

21. Burgel U, Madler B, Honey CR, Thron A, Gilsbach J, Coenen VA. Fiber tracking with distinct software tools results in a clear diversity in anatomical fiber tract portrayal. Cent. Eur. Neurosurg. 2009: 70;27–35.

22. Pujol S, Wells W, Pierpaoli C, Brun C, Gee J, Cheng G, Vemuri B, Commowick O, Prima S, Stamm A, Goubran M, Khan A, Peters T, Neher P, Maier-Hein KH, Shi Y, Tristan-Vega A, Veni G, Whitaker R, Styner M, Westin C-F, Gouttard S, Norton I, Chauvin L, Mamata H, Gerig G, Nabavi A, Golby A, Kikinis R. The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery. J Neuroimaging 2015 Nov; 25(6): 875-82.

23. DTI Challenge Working Group. http://dti-challenge.org

24. Golby AJ, Kindlmann G, Norton I, Yarmarkovich A, Pieper S, Kikinis R. Interactive diffusion tensor tractography visualization for neurosurgical planning. Neurosurgery 2011; 68:496–505.

25. Duffau H. Stimulation mapping of white matter tracts to study brain functional connectivity. Nat. Rev. Neurol. 2015; 11: 255–265.

Proc. Intl. Soc. Mag. Reson. Med. 25 (2017)