VISUALIZING NEURONAL STRUCTURES IN THE HUMAN BRAIN VIA DIFFUSION TENSOR MRI

Acquisition, analysis, and visualization of diffusion tensor magnetic resonance imaging (DT-MRI) is still an evolving technology. This article reviews the fundamentals of the data acquisition process and the pipeline leading to visual results that are interpretable by physicians in their clinical practice. The limitations of common approaches for visualizing the retrieved data are discussed and a new statistical method is presented to assess the reliability of the acquired tensor field. A novel visualization method is proposed which is discussed in light of neurophysiological considerations of the perception of colored patterns. It is argued that this method is more accurate for medical data while providing a nearly optimal visual stimulus. The method is evaluated on a patient study with a brain tumor.

[1]  J. P. Jones,et al.  The two-dimensional spatial structure of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.

[2]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[3]  P. Basser,et al.  In vivo fiber tractography using DT‐MRI data , 2000, Magnetic resonance in medicine.

[4]  Bernd Jähne,et al.  Practical handbook on image processing for scientific applications , 1997 .

[5]  Carl-Fredrik Westin,et al.  New Approaches to Estimation of White Matter Connectivity in Diffusion Tensor MRI: Elliptic PDEs and Geodesics in a Tensor-Warped Space , 2002, MICCAI.

[6]  F. Floeth,et al.  Survival in malignant glioma: analysis of prognostic factors with special regard to cytoreductive surgery. , 1996, Zentralblatt fur Neurochirurgie.

[7]  Thomas Martin Deserno,et al.  Survey: interpolation methods in medical image processing , 1999, IEEE Transactions on Medical Imaging.

[8]  A. Watson,et al.  The optimal motion stimulus , 1995, Vision Research.

[9]  Akram Aldroubi,et al.  B-spline signal processing. II. Efficiency design and applications , 1993, IEEE Trans. Signal Process..

[10]  Thomas Martin Deserno,et al.  Addendum: Spline Interpolation in Medical Image Processing , 2001, IEEE Trans. Medical Imaging.

[11]  B. Wandell,et al.  Pattern—color separable pathways predict sensitivity to simple colored patterns , 1996, Vision Research.

[12]  Werner Benger,et al.  Tensor splats , 2004, Visualization and Data Analysis.

[13]  David H. Laidlaw,et al.  Visualization and image processing of tensor fields , 2006 .

[14]  Lee Bowman,et al.  Utilization and Cost of Health Care Services Associated with Primary Malignant Brain Tumors in the United States , 2006, Journal of Neuro-Oncology.

[15]  E. Meijering A chronology of interpolation: from ancient astronomy to modern signal and image processing , 2002, Proc. IEEE.

[16]  Maher Moakher,et al.  Symmetric Positive-Definite Matrices: From Geometry to Applications and Visualization , 2006, Visualization and Processing of Tensor Fields.

[17]  Carl-Fredrik Westin,et al.  Processing and visualization for diffusion tensor MRI , 2002, Medical Image Anal..

[18]  Jianying Hu,et al.  Matching and retrieval based on the vocabulary and grammar of color patterns , 2000, IEEE Trans. Image Process..

[19]  T. A. Carpenter,et al.  Tissue signature characterisation of diffusion tensor abnormalities in cerebral gliomas , 2004, European Radiology.

[20]  Gordon Kindlmann,et al.  Superquadric tensor glyphs , 2004, VISSYM'04.

[21]  P. Bailey,et al.  A classification of the tumors of the glioma group on a histogenetic basis with a correlated study of prognosis , 1970 .

[22]  Terrence J. Sejnowski,et al.  Edges are the Independent Components of Natural Scenes , 1996, NIPS.

[23]  Akram Aldroubi,et al.  B-SPLINE SIGNAL PROCESSING: PART I-THEORY , 1993 .

[24]  Michael Unser,et al.  B-spline signal processing. I. Theory , 1993, IEEE Trans. Signal Process..

[25]  M. Mikhael,et al.  Supratentorial gliomas: surgical considerations and immediate postoperative results. Gross total resection versus partial resection. , 1987, Neurosurgery.

[26]  Peter J. Basser,et al.  Continuous Tensor Field Approximation of Diffusion Tensor MRI data , 2006, Visualization and Processing of Tensor Fields.

[27]  P. Basser,et al.  A continuous tensor field approximation of discrete DT-MRI data for extracting microstructural and architectural features of tissue. , 2002, Journal of magnetic resonance.

[28]  A. Einstein Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen [AdP 17, 549 (1905)] , 2005, Annalen der Physik.

[29]  Gordon L. Kindlmann,et al.  Tensorlines: advection-diffusion based propagation through diffusion tensor fields , 1999, Proceedings Visualization '99 (Cat. No.99CB37067).

[30]  T. Mareci,et al.  Generalized diffusion tensor imaging and analytical relationships between diffusion tensor imaging and high angular resolution diffusion imaging , 2003, Magnetic resonance in medicine.

[31]  B. Scheithauer,et al.  The New WHO Classification of Brain Tumours , 1993, Brain pathology.

[32]  Vision Research , 1961, Nature.

[33]  Nelson Max,et al.  Texture splats for 3D scalar and vector field visualization , 1993, Proceedings Visualization '93.

[34]  Robert B. Haber,et al.  Visualization techniques for engineering mechanics , 1990 .

[35]  Akram Aldroubi,et al.  B-SPLINE SIGNAL PROCESSING: PART II-EFFICIENT DESIGN AND APPLICATIONS , 1993 .

[36]  E. Meijering,et al.  A chronology of interpolation: from ancient astronomy to modern signal and image processing , 2002, Proc. IEEE.

[37]  Yu-Chien Wu,et al.  Quantitative analysis of diffusion tensor orientation: Theoretical framework , 2004, Magnetic resonance in medicine.

[38]  B. Schutz Gravity from the ground up , 2003 .

[39]  Gordon L. Kindlmann,et al.  Hue-balls and lit-tensors for direct volume rendering of diffusion tensor fields , 1999, Proceedings Visualization '99 (Cat. No.99CB37067).

[40]  J. E. Tanner,et al.  Spin diffusion measurements : spin echoes in the presence of a time-dependent field gradient , 1965 .

[41]  Lee Westover,et al.  Footprint evaluation for volume rendering , 1990, SIGGRAPH.

[42]  D. Le Bihan,et al.  Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. , 1988, Radiology.

[43]  D. Tuch High Angular Resolution Diffusion Imaging of the Human Brain , 1999 .

[44]  Alex T. Pang,et al.  Topological lines in 3D tensor fields , 2004, IEEE Visualization 2004.

[45]  Joachim Weickert,et al.  Tensor Field Interpolation with PDEs , 2006, Visualization and Processing of Tensor Fields.

[46]  Stanley Osher,et al.  Image Decomposition and Restoration Using Total Variation Minimization and the H1 , 2003, Multiscale Model. Simul..

[47]  A. Einstein,et al.  Die Grundlage der allgemeinen Relativitätstheorie , 1916 .

[48]  Carl-Fredrik Westin,et al.  Tensor Splats: Visualising Tensor Fields by Texture Mapped Volume Rendering , 2003, MICCAI.

[49]  Christopher Bingham An Antipodally Symmetric Distribution on the Sphere , 1974 .

[50]  T. Lehmann,et al.  Addendum: B-spline interpolation in medical image processing , 2001, IEEE Transactions on Medical Imaging.

[51]  M. Scerrati,et al.  Prognostic factors in low grade (WHO grade II) gliomas of the cerebral hemispheres: the role of surgery. , 1996, Journal of neurology, neurosurgery, and psychiatry.

[52]  Joseph P. Hornak,et al.  The Basics of MRI , 2003, WWW 2003.

[53]  Khader M Hasan,et al.  Diffusion-tensor imaging of white matter tracts in patients with cerebral neoplasm. , 2002, Journal of neurosurgery.

[54]  B A Wandell,et al.  Color appearance: the effects of illumination and spatial pattern. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[55]  S. Osher,et al.  IMAGE DECOMPOSITION AND RESTORATION USING TOTAL VARIATION MINIMIZATION AND THE H−1 NORM∗ , 2002 .

[56]  Arvid Lundervold,et al.  Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time , 2003, IEEE Trans. Image Process..

[57]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[58]  Gordon L. Kindlmann,et al.  Strategies for Direct Volume Rendering of Diffusion Tensor Fields , 2000, IEEE Trans. Vis. Comput. Graph..

[59]  Eva Syková,et al.  Diffusion parameters of the extracellular space in human gliomas , 2003, Glia.

[60]  Albert Einstein,et al.  Die Grundlage der allgemeinen Relativitätstheorie [AdP 49, 769 (1916)] , 2005 .

[61]  Michael Unser,et al.  Polynomial spline signal approximations: Filter design and asymptotic equivalence with Shannon's sampling theorem , 1992, IEEE Trans. Inf. Theory.

[62]  J. Smirniotopoulos The new WHO classification of brain tumors. , 1999, Neuroimaging clinics of North America.

[63]  Werner Benger,et al.  ANALYSING CURVED SPACETIMES WITH TENSOR SPLATS , 2006 .

[64]  John Missimer,et al.  Measurement of the extracellular space in brain tumors using 76Br-bromide and PET. , 2003, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[65]  Gerald F. Tremblay Neuropathology , 1989, Neurology.

[66]  P. Grenier,et al.  MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. , 1986, Radiology.

[67]  Peter E. Jupp,et al.  Modifications of the Rayleigh and Bingham Tests for Uniformity of Directions , 2001 .

[68]  David H. Laidlaw,et al.  Visualizing diffusion tensor images of the mouse spinal cord , 1998, Proceedings Visualization '98 (Cat. No.98CB36276).

[69]  Akram Aldroubi,et al.  Reconstruction of vector and tensor fields from sampled discrete data , 2003 .

[70]  V. Wedeen,et al.  Fiber crossing in human brain depicted with diffusion tensor MR imaging. , 2000, Radiology.

[71]  Khader M Hasan,et al.  Diffusion tensor eigenvector directional color imaging patterns in the evaluation of cerebral white matter tracts altered by tumor , 2004, Journal of magnetic resonance imaging : JMRI.