Abstract
Since the advent of high angular resolution diffusion imaging (HARDI) techniques in diffusion MRI great efforts have been taken in order to reconstruct complex white-matter structures, such as crossing, branching and kissing fibers. However, even highly sophisticated fiber tracking schemes, such as probabilistic tracking, suffer from the data’s poor signal-to-noise (SNR) ratio. In this paper we present a novel regularization approach for q-ball fields, exploiting structural information within the data. We also propose a straightforward deterministic tracking algorithm, allowing delineation of even non-dominant pathways through crossing regions. Results from a phantom study with a biological phantom as well as a patient study, in which we reconstruct the pyramidal tract, emphasize the method’s efficiency.
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References
Tuch, D.S.: Q-ball imaging. Magn. Reson. Med. 52(6), 1358–1372 (2004)
Parker, G.J.M., Haroon, H.A., Wheeler-Kingshott, C.A.M.: A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of mri diffusion measurements. J. Magn. Reson. Imaging 18(2), 242–254 (2003)
Lazar, M., Alexander, A.L.: Bootstrap white matter tractography (BOOT-TRAC). Neuroimage 24(2), 524–532 (2005)
Behrens, T.E.J., Berg, H.J., Jbabdi, S., Rushworth, M.F.S., Woolrich, M.W.: Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? Neuroimage 34(1), 144–155 (2007)
Zalesky, A.: DT-MRI fiber tracking: a shortest paths approach. IEEE Trans. Med. Imaging 27(10), 1458–1471 (2008)
Parker, G.J., Schnabel, J.A., Symms, M.R., Werring, D.J., Barker, G.J.: Nonlinear smoothing for reduction of systematic and random errors in diffusion tensor imaging. J. Magn. Reson. Imaging 11(6), 702–710 (2000)
Martin-Fernandez, M., Muñoz-Moreno, E., Cammoun, L., Thiran, J.P., Westin, C.F., Alberola-López, C.: Sequential anisotropic multichannel wiener filtering with rician bias correction applied to 3d regularization of DWI data. Med. Image Anal. 13(1), 19–35 (2009)
Wang, Z., Vemuri, B.C., Chen, Y., Mareci, T.H.: A constrained variational principle for direct estimation and smoothing of the diffusion tensor field from complex DWI. IEEE Trans. Med. Imaging 23(8), 930–939 (2004)
Poupon, C., Clark, C.A., Frouin, V., Régis, J., Bloch, I., Bihan, D.L., Mangin, J.: Regularization of diffusion-based direction maps for the tracking of brain white matter fascicles. Neuroimage 12(2), 184–195 (2000)
Coulon, O., Alexander, D.C., Arridge, S.: Diffusion tensor magnetic resonance image regularization. Med. Image Anal. 8(1), 47–67 (2004)
Savadjiev, P., Campbell, J.S.W., Pike, G.B., Siddiqi, K.: 3D curve inference for diffusion MRI regularization. In: Int. Conf. on Med. Image Comput Assist Interv., vol. 8(Pt 1), pp. 123–130 (2005)
Jonasson, L., Bresson, X., Thiran, J.P., Wedeen, V.J., Hagmann, P.: Representing diffusion MRI in 5-D simplifies regularization and segmentation of white matter tracts. IEEE Trans. Med. Imaging 26(11), 1547–1554 (2007)
Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R.: Regularized, fast, and robust analytical q-ball imaging. Magn. Reson. Med. 58(3), 497–510 (2007)
Descoteaux, M., Deriche, R., Knösche, T.R., Anwander, A.: Deterministic and probabilistic tractography based on complex fibre orientation distributions. IEEE Trans. Med. Imaging 28(2), 269–286 (2009)
Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 629–639 (1990)
Mori, S., Crain, B.J., Chacko, V.P., van Zijl, P.C.: Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 45(2), 265–269 (1999)
Lazar, M., Weinstein, D.M., Tsuruda, J.S., Hasan, K.M., Arfanakis, K., Meyerand, M.E., Badie, B., Rowley, H.A., Haughton, V., Field, A., Alexander, A.L.: White matter tractography using diffusion tensor deflection. Hum. Brain Mapp. 18(4), 306–321 (2003)
Ehricke, H.H., Klose, U., Grodd, W.: Visualizing MR diffusion tensor fields by dynamic fiber tracking and uncertainty mapping. Computers & Graphics 30(2), 255–264 (2006)
Behrens, T.E.J., Woolrich, M.W., Jenkinson, M., Johansen-Berg, H., Nunes, R.G., Clare, S., Matthews, P.M., Brady, J.M., Smith, S.M.: Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Reson. Med. 50(5), 1077–1088 (2003)
Perrin, M., Poupon, C., Cointepas, Y., Rieul, B., Golestani, N., Pallier, C., Rivière, D., Constantinesco, A., Bihan, D.L., Mangin, J.F.: Fiber tracking in q-ball fields using regularized particle trajectories. Inf. Process Med. Imaging 19, 52–63 (2005)
Campbell, J.S.W., Siddiqi, K., Rymar, V.V., Sadikot, A.F., Pike, G.B.: Flow-based fiber tracking with diffusion tensor and q-ball data: validation and comparison to principal diffusion direction techniques. Neuroimage 27(4), 725–736 (2005)
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Ehricke, HH., Otto, K.M., Kumar, V., Klose, U. (2009). Diffusion MRI Tractography of Crossing Fibers by Cone-Beam ODF Regularization. In: Denzler, J., Notni, G., Süße, H. (eds) Pattern Recognition. DAGM 2009. Lecture Notes in Computer Science, vol 5748. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03798-6_42
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DOI: https://doi.org/10.1007/978-3-642-03798-6_42
Publisher Name: Springer, Berlin, Heidelberg
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