WO2007036859A2 - Procede, systeme et programme d'ordinateur de resolution de croisement de fibres - Google Patents

Procede, systeme et programme d'ordinateur de resolution de croisement de fibres Download PDF

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Publication number
WO2007036859A2
WO2007036859A2 PCT/IB2006/053433 IB2006053433W WO2007036859A2 WO 2007036859 A2 WO2007036859 A2 WO 2007036859A2 IB 2006053433 W IB2006053433 W IB 2006053433W WO 2007036859 A2 WO2007036859 A2 WO 2007036859A2
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WO
WIPO (PCT)
Prior art keywords
fiber
model
image data
volume
accessing
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Application number
PCT/IB2006/053433
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English (en)
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WO2007036859A3 (fr
Inventor
Ronald Holthuizen
Nicolas Toussaint
Arianne Van Muiswinkel
Frank G. C. Hoogenraad
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Koninklijke Philips Electronics N.V.
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Publication of WO2007036859A2 publication Critical patent/WO2007036859A2/fr
Publication of WO2007036859A3 publication Critical patent/WO2007036859A3/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/12—Edge-based segmentation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/563—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution of moving material, e.g. flow contrast angiography
    • G01R33/56341—Diffusion imaging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • G06T2207/10092—Diffusion tensor magnetic resonance imaging [DTI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30016—Brain

Definitions

  • the invention relates to a method of resolving fiber crossings in a volume of image data comprising at least a first fiber and a second fiber and a further matter.
  • the invention relates to a system for resolving fiber crossings in a volume of image data comprising at least a first fiber and a second fiber and a further matter.
  • the invention relates to a computer program for resolving fiber crossings in a volume of image data comprising at least a first fiber and a second fiber and a further matter.
  • the known method is arranged for imaging a subject including anisotropic structures, notably neural fibers and further matter.
  • the known method uses image data acquired using diffusion-tensor magnetic resonance imaging method, whereby a three-dimensional diffusion tensor map of at least a volume of interest is provided.
  • the diffusion tensor is processed in the known method to obtain eigenvectors and eigenvalues.
  • the three-dimensional fiber representation is rendered as a hyperstreamline representation. It is a disadvantage of the known method that it produces inferior results for volumes where multiple fibers are present, because a single tensor in incapable of representing multiple fibers.
  • the method according to the invention comprises the steps of:
  • model representative of a composition of said volume comprising a set of values representative of the first fiber, the second fiber and the further matter, whereby for the further matter an isotropic component is used;
  • the technical measure of the invention is based on the insight that in order to perform fiber tracking it is necessary to determine a composition of a volume element.
  • a volume, or volume element within the terms of present invention is defined either as a single imaging voxel, or as a combination of imaging voxels.
  • the method according to the invention uses a model representative of a composition of said volume, said model comprising a set of values representative of the first fiber, the second fiber and the further matter, whereby for the further matter an isotropic component is used. It is noted that a single fiber can be represented accurately by a single tensor.
  • the underlying insight is based on understanding of basic physiology, as it is expected that individual fibers are identical in their diffusion behavior and that it is only orientation and volume fraction of the two fibers that may be different from one place to another.
  • a restricted tensor per fiber is used, whereby the degree of freedom of such tensor is being restricted.
  • a more accurate and robust fiber crossing resolution is achieved.
  • operating with reduced tensors saves computation time.
  • the restriction can be advantageously achieved by using an axially symmetrical tensors for the first and the second fiber, or introducing an allowable range of values for the primary eigenvalue of each tensor, or introducing an allowable 'thickness' range of the fiber, i.e. the ratio of the primary and secondary eigenvalues of the tensor.
  • the method of the invention is not limited to a tensor-based model.
  • parameters to describe a fiber like orientation, size, for example, relating to the primary eigenvalue, and thickness.
  • calculation of the eigenvector is no longer needed.
  • the method according to the invention is suited for dealing with 3, 4, ..., n fiber crossings in a volume.
  • This technical measure is based on the insight that after resolving the respective volume contents, the fiber direction can be used to perform fiber tracking in crossing fiber situations, similar to performing fiber tracking in single fiber regions, as is described in US 6, 642, 716 Bl , whereby eigenvalues and eigenvectors of respective tensors are used for tracking purposes.
  • the system according to the invention comprises:
  • model representative of a composition of said volume comprising a set of values representative of the first fiber, the second fiber and the further matter, whereby for the further matter an isotropic component is used;
  • the system according to the invention is advantageously arranged to enable a fiber crossing resolution, particularly in circumstances where multiple (two or more) fibers are present in a volume element.
  • the system according to the invention is particularly useful for cases where the image data is acquired with suboptimal resolution leading to impossibility to define voxel elements per sole fibers.
  • a second fiber and the further matter respective fiber directions are obtained.
  • the fibers can be then tracked using, for example, a per se known technique.
  • a suitable example of fiber crossing resolution and fiber tracking will be discussed with reference to Figure 1. Further advantageous embodiments of the system according to the invention are given in Claim 7 and Claim 8.
  • the computer program according to the invention comprises instructions to make a processor to carry out the following steps:
  • the computer program according to the invention is based on the insight that in order to perform fiber tracking it is necessary to determine a composition of a volume element.
  • the method according to the invention uses a model representative of a composition of said volume, said model comprising a set of values representative of the first fiber, the second fiber and the further matter, whereby for the further matter an isotropic component is used. It is noted that a single fiber can be represented accurately by a single tensor.
  • Figure 1 presents a schematic view of an embodiment of a method according to the invention.
  • Figure 2 presents a schematic view of an embodiment of a system according to the invention.
  • Figure 3 presents a schematic view of an embodiment of a flow-chart of a computer program according to the invention.
  • Figure 1 presents a schematic view of an embodiment of a method according to the invention.
  • the method of the invention relates to data processing arts, in particularly to resolving of fiber crossing in volume elements of diagnostic data and tracking of individual fibers in said data.
  • Nerve tissue in humans comprise neurons with elongated axonal portions arranged to form neural fibers or fiber bundles along which electrical signals are propagating.
  • the axonal fiber bundles are substantially surrounded by a further tissue.
  • image data is accessed. It is possible that image data is accessed from a memory unit of a computer implementing the method, or from a remote location, it being a remote computer or a data storage unit, for example of a suitable data acquisition apparatus.
  • the method according to the invention is arranged to resolve fiber crossings using an insight that a volume element in image data can be represented by a set of values per each fiber, and an isotropic component representative of the further matter.
  • Suitable examples of the further matter comprise white matter, like genu of the corpus callosum.
  • the method 1 according to the invention accesses the model 7 at step 5, said model comprising a set of values representative of the first fiber 8a, the second fiber 8b and the further matter 8c, whereby for the further matter an isotropic component is used.
  • the model can be tensor-based, as a single fiber can be represented accurately by a single tensor.
  • the underlying insight is based on understanding of basic physiology, as it is expected that individual fibers are identical in their diffusion behavior and that it is only orientation and volume fraction of the two fibers that may be different from one place to another.
  • a restricted tensor per fiber is used, whereby the degree of freedom of such tensor is being restricted.
  • a more accurate and robust fiber crossing resolution is achieved.
  • operating with reduced tensors saves computation time.
  • the restriction can be advantageously achieved by using an axially symmetrical tensors for the first and the second fiber, or introducing an allowable range of values for the primary eigenvalue of each tensor, or introducing an allowable 'thickness' range of the fiber, i.e. the ratio of the primary and secondary eigenvalues of the tensor.
  • the method of the invention is not limited to a tensor-based model.
  • parameters to describe a fiber like orientation, size, for example, relating to the primary eigenvalue, and thickness.
  • calculation of the eigenvector is no longer needed.
  • the method according to the invention is suited for dealing with 3, 4, ..., n fiber crossings in a volume.
  • the method according to the invention may proceed to a further phase of tracking 20 individual fibers in the image data.
  • a seed 21 is placed in a volume element of the image data and eigenvalues and eigenvectors 22 of respective fiber tensors are determined.
  • a local direction of the fiber in the voxel can be determined. Adjacent voxels are subsequently analyzed to determine whether they are situated nearby the current seed along the local direction. It is possible to use both a positive and a negative tracking, in accordance with the method known from US 6, 642, 716 Bl.
  • the method 1 detects a termination of a fiber 24, it may proceed to a further step 28, which may be an exit or a continuation of data processing.
  • An example of a continuation of a data processing is initiation of a suitable volume rendering algorithm to visualize the tracked fibers in the image data.
  • Figure 2 presents a schematic view of an embodiment of a system according to the invention for resolving fiber crossings in a volume of image data comprising at least a first fiber and a second fiber and a further matter.
  • the system 30 preferably is arranged as a computer 32 with an input 34 for accessing the image data 34a; for accessing a model 34b representative of a composition of said volume, said model comprising a set of values representative of the first fiber, the second fiber and the further matter, whereby for the further matter an isotropic component is used; for accessing an optimization function 34c conceived to optimize said set of values spatially matching at least said fibers with the image data.
  • the computer 32 preferably further comprises a processor 38, which can be operated by a computer program 37 arranged to cause said processor to carry out the steps of the method as is described with reference with Figure 1.
  • the processor uses the optimization function 34c and searches for a global optimum of it with respect to the fiber crossing resolution in accordance with a suitable algorithm 39.
  • the system 30 further comprises a data acquisition unit 31 arranged to acquire image data.
  • the apparatus 31 comprises a magnetic resonance apparatus arranged to carry out data acquisition in a diffusion tensor magnetic resonance imaging, which is known per se in the art of magnetic resonance imaging techniques.
  • the system 30 according to the invention comprises a viewer 33 arranged to display results of fiber crossing resolution and/or results of fiber tracking on a suitable display 35.
  • Figure 3 presents a schematic view of an embodiment of a flow-chart of a computer program according to the invention.
  • the computer program 40 of the invention relates to data processing arts, in particularly to the art of resolving of fiber crossing in volume elements of diagnostic data and tracking of individual fibers in said data.
  • the computer program comprises an instruction 44 for causing a processor of a suitable computer to access image data conceived to be subjected to fiber crossing resolution. It is possible that image data is accessed from a memory unit of a computer implementing the method, or from a remote location, it being a remote computer or a data storage unit, for example of a suitable data acquisition apparatus.
  • the computer program 40 comprises an instruction 46 to accesses the model 47 using an instruction 45, said model comprising a set of values representative of the first fiber 48a, the second fiber 48b and the further matter 48c, whereby for the further matter an isotropic component is used.
  • the model can be tensor-based, as a single fiber can be represented accurately by a single tensor.
  • the underlying insight is based on understanding of basic physiology, as it is expected that individual fibers are identical in their diffusion behavior and that it is only orientation and volume fraction of the two fibers that may be different from one place to another.
  • a restricted tensor per fiber is used, whereby the degree of freedom of such tensor is being restricted.
  • a more accurate and robust fiber crossing resolution is achieved.
  • operating with reduced tensors saves computation time.
  • the restriction can be advantageously achieved by using an axially symmetrical tensors for the first and the second fiber, or introducing an allowable range of values for the primary eigenvalue of each tensor, or introducing an allowable 'thickness' range of the fiber, i.e. the ratio of the primary and secondary eigenvalues of the tensor.
  • the computer program 40 of the invention of the invention is not limited to operate with tensor-based models.
  • the parameter-based model calculation of the eigenvector is no longer needed.
  • the method according to the invention is suited for dealing with 3, 4, ..., n fiber crossings in a volume.
  • the instruction 48 causes the processor (not shown) to optimize the values of the first fiber tensor 48a, the fiber tensor 48b and the isotropic component 48c using a suitable optimization algorithm arranged to fit the model to the image data.
  • a suitable optimization algorithm arranged to fit the model to the image data.
  • the computer program according to the invention may proceed to a further phase of tracking 60 individual fibers in the image data.
  • a seed is placed, using a suitable instruction 61, in a volume element of the image data and eigenvalues and eigenvectors of respective fiber tensors are determined using instruction 62.
  • a local direction of the fiber in the voxel can be determined. Adjacent voxels are subsequently analyzed to determine whether they are situated nearby the current seed along the local direction. It is possible to use both a positive and a negative tracking, in accordance with the method known from US 6, 642, 716 Bl.
  • the computer program detects a termination of a fiber by means of a suitable instruction 64, it may proceed to a further instruction 68, which may be an exit instruction or a continuation instruction.
  • a continuation instruction is an instruction conceived to cause the processor to initiate of a suitable volume rendering algorithm to visualize the tracked fibers in the image data.

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  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Medical Informatics (AREA)
  • Quality & Reliability (AREA)
  • Radiology & Medical Imaging (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)
  • Image Analysis (AREA)

Abstract

La présente invention concerne le domaine du traitement de données, notamment la résolution du croisement de fibres dans des éléments de volume de données de diagnostic et la localisation de fibres individuelles dans lesdites données. Dans l'étape 4 du procédé 1 selon l'invention, des données d'image sont obtenues. Puis le procédé 1 selon l'invention accède au modèle (7) dans l'étape 5, ledit modèle comprenant un ensemble de valeurs représentant la première fibre (8a), la deuxième fibre (8b) et l'autre matière (8c), pour laquelle une composante isotrope est utilisée. Pour un modèle fondé sur un tenseur, dans l'étape 8 du procédé 1, les valeurs du tenseur de la première fibre (8a), du tenseur de la fibre (8b) et de la composante isotrope (8c) sont optimisées au moyen d'un algorithme d'optimisation approprié prévu pour correspondre au modèle des données d'image. Après cette étape, les directions de la fibre sont obtenues dans l'étape 10. Ensuite le procédé selon l'invention peut se poursuivre avec une autre phase de localisation (20) de fibres individuelles dans les données d'image. A cette fin, un noyau (21) est placé dans un élément de volume des données d'image et des valeurs propres et des vecteurs propres (22) des tenseurs de fibres respectifs sont déterminés. A partir du noyau et avec les vecteurs propres, une direction locale de la fibre peut être déterminée dans le voxel. Des voxels adjacents sont ensuite analysés en vue de déterminer s'ils sont situés à proximité du noyau du moment dans la direction locale. Lorsque le procédé 1 détecte un événement de terminaison d'une fibre (24), ce dernier peut passer à l'étape 28, qui peut être une sortie ou une poursuite du traitement de données. Cette invention concerne également un système et un programme d'ordinateur assurant la résolution de croisements de fibres.
PCT/IB2006/053433 2005-09-29 2006-09-22 Procede, systeme et programme d'ordinateur de resolution de croisement de fibres WO2007036859A2 (fr)

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EP05108998 2005-09-29

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014036153A1 (fr) * 2012-08-28 2014-03-06 Molecular Devices, Llc Procédés et systèmes d'analyse de fibres et de structures de branchement dans une image d'un échantillon
US20140233819A1 (en) * 2013-02-20 2014-08-21 Industry-University Cooperation Foundation Hanyang University Method and apparatus for acquiring nerve fiber structure information of object by using mri system
US9255979B2 (en) 2012-04-11 2016-02-09 General Electric Company Measuring diffusional anisotropy of ODF lobes having maxima peaks and minima troughs with diffusion weighted MRI

Citations (1)

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Publication number Priority date Publication date Assignee Title
WO2005076030A1 (fr) * 2004-02-06 2005-08-18 Koninklijke Philips Electronics N.V. Irm ponderee par diffusion de resolution extremement angulaire

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005076030A1 (fr) * 2004-02-06 2005-08-18 Koninklijke Philips Electronics N.V. Irm ponderee par diffusion de resolution extremement angulaire

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
BEHRENS T E J ET AL: "Characterization and propagation of uncertainty in diffusion-weighted MR imaging" MAGNETIC RESONANCE IN MEDICINE WILEY USA, vol. 50, no. 5, November 2003 (2003-11), pages 1077-1088, XP002441211 ISSN: 0740-3194 *
DELL'ACQUA F ET AL: "A deconvolution approach based on multi-tensor model to solve fiber crossing in diffusion-MRI" 27TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (IEEE CAT. NO.05CH37611C) IEEE PISCATAWAY, NJ, USA, 1 September 2005 (2005-09-01), pages 1415-1418, XP002441212 ISBN: 0-7803-8740-6 *
SHAHRUM NEDJATI-GILANI, PHILIP A. COOK, GEOFF J. M. PARKER AND DANIEL C. ALEXANDER: "Voxel-Based Classification of White Matter Fibre Complexity in Diffusion MRI" PROC. OF MEDICAL IMAGE UNDERSTANDING AND ANALYSIS, 19 July 2005 (2005-07-19), pages 199-202, XP002441210 Bristol, UK *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9255979B2 (en) 2012-04-11 2016-02-09 General Electric Company Measuring diffusional anisotropy of ODF lobes having maxima peaks and minima troughs with diffusion weighted MRI
WO2014036153A1 (fr) * 2012-08-28 2014-03-06 Molecular Devices, Llc Procédés et systèmes d'analyse de fibres et de structures de branchement dans une image d'un échantillon
US9646194B2 (en) 2012-08-28 2017-05-09 Molecular Devices, Llc Methods and systems for analysis of fibers and branching structures within an image of a sample
US20140233819A1 (en) * 2013-02-20 2014-08-21 Industry-University Cooperation Foundation Hanyang University Method and apparatus for acquiring nerve fiber structure information of object by using mri system
US9436869B2 (en) * 2013-02-20 2016-09-06 Samsung Electronics Co., Ltd. Method and apparatus for acquiring nerve fiber structure information of object by using MRI system

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