WO2010017524A2 - Système, procédé et support accessible par un ordinateur pour obtenir une imagerie d'aplatissement de diffusion en temps réel - Google Patents

Système, procédé et support accessible par un ordinateur pour obtenir une imagerie d'aplatissement de diffusion en temps réel Download PDF

Info

Publication number
WO2010017524A2
WO2010017524A2 PCT/US2009/053223 US2009053223W WO2010017524A2 WO 2010017524 A2 WO2010017524 A2 WO 2010017524A2 US 2009053223 W US2009053223 W US 2009053223W WO 2010017524 A2 WO2010017524 A2 WO 2010017524A2
Authority
WO
WIPO (PCT)
Prior art keywords
measure
kurtosis
diffusional
diffusion
restrictivity
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2009/053223
Other languages
English (en)
Other versions
WO2010017524A3 (fr
Inventor
Jens Jenson
Joseph A. Helpern
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
New York University NYU
Original Assignee
New York University NYU
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by New York University NYU filed Critical New York University NYU
Priority to CN200980139435.1A priority Critical patent/CN102202572B/zh
Publication of WO2010017524A2 publication Critical patent/WO2010017524A2/fr
Publication of WO2010017524A3 publication Critical patent/WO2010017524A3/fr
Priority to US13/022,488 priority patent/US8811706B2/en
Anticipated expiration legal-status Critical
Priority to US14/462,215 priority patent/US9965862B2/en
Ceased legal-status Critical Current

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/66—Analysis of geometric attributes of image moments or centre of gravity
    • 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 OR CALCULATING; 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 OR CALCULATING; 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 present disclosure relates to exemplary embodiments of systems, methods and computer-accessible mediums for providing real-time diffusional kurtosis imaging.
  • Diffusion anisotropy of water in biological tissues is conventionally quantified with the diffusion tensor (DT) and related indices such as the fractional anisotropy.
  • DT describes the diffusion displacement probability using a Gaussian distribution function.
  • One of the main applications of the DT is tracing the white matter pathways in the brain using local estimates of the fiber orientations.
  • DT likely fails to describe the full directional information of the diffusion process. Most notably, the DT is not able to resolve fiber crossing, which occurs in many brain regions.
  • PDF probability density function
  • Diffusional kurtosis is a quantitative measure of the degree to which the diffusion displacement probability distribution deviates from a Gaussian form.
  • Diffusional Kurtosis Imaging is a magnetic resonance imaging (MRI) technique for measuring this quantity.
  • MRI magnetic resonance imaging
  • conventional DKI methods require substantial time (approximately 1 hour or more) for post-processing of the acquired images which may be a significant disadvantage in clinical practice.
  • a method for determining a measure of diffusional kurtosis which comprises receiving data relating to at least one diffusion weighted image, and using a computer arrangement, determining a measure of a diffusional kurtosis as a function of the received data using a closed form solution procedure.
  • the method can further comprise performing (i) providing the measure of the diffusional kurtosis to a display device, and/or (ii) recording the measure of the diffusional kurtosis.
  • At least one diffusion weighted image can be acquired for three or more b-values. Such one or more diffusion weighted images can be acquired for 15 or more gradient directions.
  • the measure of the diffusional kurtosis can be determined using a mean kurtosis procedure.
  • the mean kurtosis can be determined by averaging measures of a diffusion and a kurtosis over each gradient direction.
  • the closed form solution procedure can include at least one or more elliptic integrals, and such one or more elliptic integrals can be associated with eigenvalues of at least one diffusion tensor based on the at least one diffusion weighted image.
  • Such one or more elliptic integrals can be associated with at least one Carlson symmetric form of an elliptic integral.
  • the measure of the diffusional kurtosis can be an axial kurtosis and/or a radial kurtosis.
  • At least a portion of the received data can relate to at least one orientation distribution function, the portion of the received data being calculated using the measure of the diffusional kurtosis.
  • the exemplary method can further comprise resolving at least one fiber crossing using the received data from the at least one orientation distribution function. Such one or more fibers crossing can be a crossing of at least one of two fibers, three fibers or four fibers.
  • the exemplary method can further comprise providing a directional color map of the at least one fiber crossing, whereas the directional color map can provide fiber direction estimates as a function of the data associated with the orientation distribution function(s).
  • a fiber tractography can be performed using the data associated with the orientation distribution function(s).
  • the method can further comprise analyzing white matter connectivity patterns using the data associated with the at least one orientation distribution function, and estimating white matter pathways using the data associated with the at least one orientation distribution function.
  • the exemplary method can further comprise analyzing fibers tracts which at least one of cross, kiss, branch, merge or splay, using the data associated with the orientation distribution function(s).
  • the received data associated with the orientation distribution function(s) can include Gaussian diffusion contributions and substantially exclude non-Gaussian diffusion contributions, include non-Gaussian diffusion contributions and substantially exclude Gaussian diffusion contributions or include Gaussian diffusion contributions and non-Gaussian diffusion contributions.
  • the data associated with the orientation distribution function(s) can be an approximation of an integral of a function depending on diffusion and kurtosis coefficients over a perpendicularly-oriented great circle.
  • the method can further comprise assessing at least one medical condition of a subject using the measure of the diffusional kurtosis.
  • the medical condition(s) can be a neurological disease and/or a neuro-degenerative diseases.
  • the medical condition(s) can be Alzheimer's disease, stroke, head trauma, attention deficit hyperactivity disorder and/or schizophrenia.
  • the at least one medical condition can be assessed by comparing further data relating to the subject with predetermined control data, and the control data can comprise age-matched control data.
  • the method can further comprise determining a measure of a diffusional restrictivity using the measure of the diffusional kurtosis.
  • the measure of the diffusional restrictivity can be determined from a diffusion tensor and a kurtosis tensor relating to the diffusion weighted image(s).
  • the measure of the diffusional restrictivity can include a measure of Gaussian restrictivity which substantially excludes non-Gaussian restrictivity, include a measure of non-Gaussian restrictivity which substantially excludes Gaussian restrictivity, or include a measure of Gaussian restrictivity and non-Gaussian restrictivity.
  • the measure of diffusional restrictivity can be determined using a first measure dependent on diffusivity and a second measure dependent on diffusional kurtosis.
  • the second measure can reflect non-Gaussian diffusion contributions to a diffusion signal.
  • the exemplary method can further comprise identifying at least one region of interest in an anatomical structure based on the measure of the diffusional kurtosis and/or the measure of the diffusional restrictivity.
  • the anatomical structure can comprise a brain tissue.
  • the at least one region of interest can indicate differences in microstructure between different portions of the anatomical structure.
  • the at least one region of interest can at least partially distinguish between a reversibly injured tissue and an irreversibly injured tissue.
  • a system for determining a measure of diffusional kurtosis comprising a first arrangement configured to receive data relating to at least one diffusion weighted image, and a second arrangement configured to determine a measure of a diffusional kurtosis as a function of the received data using a closed form solution procedure.
  • the second arrangement can be further configured to perform (i) provide the measure of the diffusional kurtosis to a display device, and/or (ii) record the measure of the diffusional kurtosis.
  • the diffusion weighted image(s) can be acquired for three or more b-values, and the diffusion weighted image(s) can be acquired for 15 or more gradient directions.
  • the second arrangement can be further configured to assess at least one medical condition of a subject using the measure of the diffusional kurtosis, and can be further configured to determine a measure of a diffusional restrictivity using the measure of the diffusional kurtosis.
  • the second arrangement can be further configured to identify at least one region of interest in an anatomical structure based on the measure of the diffusional kurtosis and/or the measure of the diffusional restrictivity.
  • a computer- accessible medium for determining a measure of diffusional kurtosis
  • the computer- accessible medium including instructions thereon, wherein, when a computing arrangement executes the instructions, the computing arrangement is configured to perform procedures comprising receiving data relating to at least one diffusion weighted image, and determining a measure of a diffusional kurtosis as a function of the received data using a closed form solution procedure.
  • the computing arrangement can be further configured to (i) provide the measure of the diffusional kurtosis to a display device, and/or (ii) record the measure of the diffusional kurtosis.
  • FIGs. 1 (a)- 1 (f) are illustrations of exemplary three dimensional surfaces of exact and estimated ODF's for exemplary diffusion models
  • FIGs. 2(a)-2(f) are other illustrations of further exemplary three dimensional surfaces of exact and estimated ODF's for the exemplary diffusion models;
  • FIGs. 3(a)-3(e) are illustrations of exemplary ODF maps of the brainstem
  • FIGs. 4(a)-4(e) are other illustrations of further exemplary ODF maps of the brainstem;
  • Fig. 5(a)-5(e) are is illustrations of the exemplary ODF maps of the intersection between the posterior region of the superior longitudinal fasciculus with the projection fibers of the corona radiata and the posterior transverse association fibers;
  • Figs. 6(a)-6(d) are illustrations of exemplary brain images of a mean diffusivity, axial diffusivity, radial diffusivity and a fractional anisotropy, respectively, provided by conventional diffusion tensor imaging;
  • Figs. 7(a)-7(c) are illustrations of exemplary parametric maps derived with realtime diffusional kurtosis imaging obtained using exemplary embodiments of the present disclosure
  • FIG. 8 is a flow diagram according to an exemplary embodiment of a method of an exemplary real-time diffusional kurtosis imaging
  • Fig. 9 is a flow diagram according to an exemplary embodiment of a method of the present disclosure.
  • Fig. 10 is a block diagram of an exemplary embodiment of a system according to the present disclosure.
  • Diffusional Kurtosis Imaging is an exemplary magnetic resonance imaging technique, which can be useful for the assessment of a variety of diseases including stroke, Alzheimer's disease, head trauma, schizophrenia and attention deficit hyperactivity disorder.
  • results should ideally be available on-line within seconds after the scanning so that timely treatment decisions can be made.
  • Real-time DKI analysis routinely available on commercial Magnetic Resonance Imaging (MRI) scanners would also greatly facilitate the application and increase the usefulness of DKI for many other diseases.
  • “Real-time” can comprise but is not limited to, e.g., an analysis in which results, measurements, etc. are obtained within approximately one minute, and sometimes within approximately 30 seconds.
  • real-time DKI method, system and computer-accessible medium can be provided, which can be based both on a particular data acquisition scheme and on a mathematical prescription for rapid analysis of the images.
  • An exemplary DKI data set utilizes that diffusion-weighted (DW) images can be acquired for approximately 3 or more b-values and approximately 15 or more gradient directions.
  • a b-value is a factor of diffusion weighted sequences, and summarizes the influence of the gradients on diffusion weighted images. The higher the b-value, the stronger the diffusion weighting. Particularly, there can be an advantage in using precisely 3 b-values in that this can allow one to avoid numerical nonlinear fitting procedures which are both time-consuming and often suffer from convergence problems. In this exemplary manner, the post-processing time can be significantly reduced.
  • the gradient directions can typically be distributed uniformly on a sphere.
  • An exemplary selection can be a set of 30 directions defined by a truncated icosahedron. For each exemplary b-value and direction, one or more images may be acquired. If more than one image is acquired, such exemplary images can be co-registered and averaged prior to data processing. Exemplary Data Processing Procedure( s)
  • a first procedure in the exemplary data processing methodology can be to determine the diffusion coefficient (Di) and diffusional kurtosis (K;) for the ith gradient direction in each voxel.
  • Voxels can be processed independently according to the same or similar procedure, and so it can be sufficient to describe the procedure for a single voxel.
  • An exemplary closed form solution can be used to determine a measure of diffusional kurtosis, as will be set out below.
  • An equation or system of equations can have a closed- form solution, e.g., when at least one solution can be expressed as a closed-form expression.
  • a closed-form expression can, e.g., be expressed analytically in terms of a bounded number of certain functions.
  • the diffusion tensor and the diffusional kurtosis tensor can be constructed by linear inversion.
  • the exemplary linear system for the diffusion tensor can be represented by:
  • N can be the number of gradient directions
  • n can be the ith component of the nth gradient direction vector and can be the components of the diffusion tensor.
  • the linear system for the diffusional kurtosis tensor can then be represented by:
  • Wjjki can be the components of the diffusional kurtosis tensor and D can be the mean diffusivity (MD) which may be calculated from
  • the diffusion tensor can takes the form of:
  • the exemplary rotation matrix can be given by: where can be the component of the ith eigenvector for
  • the eigenvalues and eigenvectors can be determined by using any of several exemplary standard techniques, such as Jacobi's method. Without loss of generality, it is possible to order the eigenvalues so that
  • the axial diffusivity can be obtained by:
  • MK mean kurtosis
  • Eqs. [10] - [12] can be standard results used in diffusional tensor imaging data processing. However, Eqs. [13] - [25] can be important for real-time DKI data processing.
  • orientation distribution function for diffusion displacement probability distribution is that it can resolve fiber crossings in a model independent manner.
  • a mathematical relationship between the ODF and the DK can demonstrate the ability of the DK-ODF to resolve fiber crossings by using simulations.
  • an exemplary application of the DK-ODF to the brain is provided by exemplary examples of DK-ODF's calculated from human imaging data.
  • Exemplary diffusional kurtosis imaging procedures can extend the Gaussian approximation of the diffusion distribution function by considering non-Gaussian contributions through an additional kurtosis term.
  • the diffusion signal can be written as:
  • D can represent the mean diffusivity D ⁇ Trace(D)/3.
  • the exemplary diffusion and kurtosis coefficients along the n direction can be related to the respective tensors by the following equations:
  • the ODF can be defined as its radial projection:
  • Eq. [30] can be one-dimensional and corresponds to a Funk transform, which can reduce the determination of the ODF value along a direction n to an integration of the signal values on a perpendicularly oriented great circle.
  • Eq. [30] takes the form:
  • an exemplary approximation for the ODF in a particular direction can be obtained by integrating a function depending on the diffusion and kurtosis coefficients over the perpendicularly oriented great circle.
  • the exemplary DK-ODF can facilitate the resolution of fiber crossings. Since a standard DKI data set can provide estimates for both the diffusion tensor and the kurtosis tensor, it is possible to use Eq. [31] to also determine an approximate ODF. For example, and under the integrand can be approximated at any location on the great circle from the diffusion and kurtosis tensors using Eqs. [27a] and [27b].
  • the ODF can be represented as a sum of two terms as follows:
  • the total ODF can then be written as:
  • the displacement probability distribution can have the form of:
  • An exemplary disadvantage of the DT-ODF is that it may not allow fiber crossings to be resolved.
  • Equation [35] can be used to fix the normalizations of the ODF approximations by setting:
  • N is the number of compartments, the diffusion tensor for the mth compartment, and its corresponding water fraction with the water fractions adding to
  • the exemplary diffusion and kurtosis tensors can be obtained as combinations of the diffusion tensors describing the individual compartments:
  • the ODF-DK can be estimated using Eq. [32], where D(n) and K(n) values can be obtained from the diffusion and kurtosis tensor, respectively.
  • Exemplary mixed fiber models having two to four compartments were used.
  • Mixtures with different weights were investigated.
  • the exact and DK and QB estimated ODFs were calculated and displayed, as well as the exact and DK estimated non-Gaussian ODFs (NG-ODF).
  • a b value of 4000s/mm 2 was assumed for calculating the QB-ODF as an approximate and typical value.
  • the Gaussian (DT) ODF was also calculated for each model.
  • a diffusional restrictivity at a time t can is defined as:
  • Exemplary imaging experiments were conducted on a 3 T Trio MR system (Siemens Medical Solutions, Er Weg, Germany) using a body coil for transmission and an eight-element phase array coil for reception.
  • Exemplary DKI data was obtained for six healthy volunteers using a predetermined protocol.
  • Diffusion weighted images were acquired for 30 uniformly distributed gradient directions and for five b-values (500, 1000, 1500, 2000 and 2500 s/mm 2 ) using a twice refocused-spin-echo EPI sequence, which has been shown to significantly reduce the eddy-current-related distortions in the diffusion weighted images.
  • the total duration for acquiring a DKI data set was 12 minutes.
  • the diffusion weighted images were first corrected for motion and spatially smoothed using SPM using a two-dimensional Gaussian filter with FWHM of 2.5 mm.
  • the diffusion and kurtosis tensors were subsequently calculated. Only those data points that exceeded the value representing the 90th percentile of the noise range were included in the calculation, where noise values were sampled using a 10x10x13 volume situated outside a brain.
  • the tensor calculation included: 1) estimation of the apparent diffusivity and kurtosis values along each of the thirty encoding directions using Levenberg- Marquardt nonlinear fitting algorithm for Eq.
  • the exemplary diffusion tensor eigenvectors and eigenvalues, and the fractional anisotropy were calculated and used to obtain directional color maps and to depict the fiber direction estimates using a Gaussian approximation.
  • the exemplary color maps were used for anatomical reference.
  • the non-Gaussian (NG), Gaussian, and total ODFs were calculated at each voxel using Eq. [32] on a grid of 60 x 60 data points (corresponding to equally spaced ⁇ and ⁇ values).
  • the ODF value was estimated by integrating along the equator circle perpendicular to a grid point direction, and the D(n) and K(n) values in the integrand were obtained from the diffusion and kurtosis tensors, respectively.
  • a 0 to 1 resealed min-max version of the non-Gaussian ODF was also calculated (i.e., the minimum ODF value is scaled to 0 and the maximum ODF value is scaled to 1).
  • the exemplary fiber directions at each voxel were determined from the ODF peaks.
  • the ODF surfaces were superimposed onto mean kurtosis maps and were color-coded using the typical mapping of the x, y, and z spatial directions to a red, green, and blue color triad.
  • Fig. l(a) illustrates an exemplary exact ODF
  • Fig. l(b) illustrates an exemplary DK estimation of the ODF
  • Fig. l(c) illustrates an exemplary exact NG-ODF
  • Fig. l(d) illustrates an exemplary DK estimation of the NG-ODF
  • l(e) illustrates an exemplary q-ball estimation of the ODF (min-max scaled) for a b-value of 4000s/mm 2
  • Fig. l(f) illustrates an exemplary Gaussian estimation of the ODF.
  • Figs. 2(a)-2(f) exemplary images of exemplary three dimensional surfaces of the exact and estimated ODF are illustrated for diffusion models with two fibers with volume fractions of 30% and 70% (top row) and two equally contributing fibers intersecting at an angle of 30° (bottom row).
  • Fig. 2(a) illustrates an exemplary exact ODF
  • Fig. 2(b) illustrates an exemplary DK estimation of the ODF
  • Fig. 2(c) illustrates an exemplary exact NG-ODF
  • Fig. 2(d) illustrates an exemplary DK estimation of the NG-ODF
  • FIG. 2(e) illustrates an exemplary q-ball estimation of the ODF (min-max scaled) for a b value of 4000 s/mm 2
  • Fig. 2(f) illustrates an exemplary Gaussian estimation of the ODF.
  • the directions of the exemplary component fibers are shown by dashed lines.
  • the fiber orientations shown in Fig. 2(a) are the same as those in Fig. l(a).
  • the DK-ODF approximation appears to resolve orthogonal or close to orthogonal fiber configurations (as illustrated in Fig. l(b)). Offsets of the exemplary ODF peaks with respect to the component fiber directions are also apparent at small intersection angles for the exact ODF.
  • FIG. 3-5 show the absolute and min-max normalized DK-derived NG-ODF maps and the corresponding DT- ODF maps for several brain regions where complex fiber architecture is present. Voxels with multiple fiber components can be distinguished on the DK-ODF maps. In general, the min-max normalization can improve the visualization of the ODF peaks. The fiber orientations resolved using the DK approximation are consistent with known anatomy. Complex fiber architecture is not apparent on the DT-ODF maps.
  • Figs. 3(a)-3(e), 4(a)-4(e) and 5(a)-5(d) show exemplary ODF maps of an axial slice situated at the cerebral pons level.
  • Such exemplary region contains several fiber populations including the superiorly - inferiorly oriented pyramidal and central tegmental tracts and the transverse pontine fibers that are running from left to right.
  • Fig. 3 (a) illustrates an exemplary DT approximation
  • Fig. 3(b) illustrates an exemplary DK-derived NG-ODF
  • Fig. 3(c) illustrates an exemplary min-max scaled version of Fig. 3(b).
  • FIG. 3(d) An exemplary DT derived color map of the same axial slice and location of the ODF maps are illustrated in Fig. 3(d).
  • Fiber directions obtained using the DK (left - unsealed ODF and center - min-max scaled ODF) and DT (right) approximations for one voxel with apparent partial volume averaging of two fiber populations are illustrated, as an example,in Fig. 3(e).
  • Two fiber directions transverse and superior-inferior
  • These fiber directions appear to be in agreement with known anatomy.
  • the DT approximation does not only fail to resolve the two fiber populations in voxels affected by partial volume averaging, but appears to fail to describe either orientation correctly (e.g., see Fig 3(e)).
  • Figs. 4(a)-4(e) show exemplary intersections of the superior longitudinal fasciculus (SLF), corona radiata, and corpus callosum in the centrum semiovale region.
  • SLF superior longitudinal fasciculus
  • Fig. 4(a) illustrates an exemplary DT approximation
  • Fig. 4(b) illustrates an exemplary DK-derived NG-ODF
  • Fig. 4(c) illustrates an exemplary min-max scaled version of Fig. 4(b).
  • An exemplary color map in Fig. 4(d) illustrates a position of the ODF maps with respect to other brain structures.
  • Fig. 4(a) illustrates an exemplary DT approximation
  • Fig. 4(b) illustrates an exemplary DK-derived NG-ODF
  • Fig. 4(c) illustrates an exemplary min-max scaled version of Fig. 4(b).
  • An exemplary color map in Fig. 4(d) illustrates a position of the
  • FIG. 4(e) illustrates exemplary ODFs and corresponding fiber directions for a voxel where three distinct fiber populations are apparent using the DK approximation. Exemplary regions with two and three fiber populations are illustrated on the DK-ODF map. On the DK-ODF map, the SLF runs continuously in the anterior-posterior direction, whereas it is interrupted on the color map which was obtained using the exemplary DT-ODF approximation.
  • Figs. 5(a)-5(d) illustrate exemplary ODF maps of the intersection between the posterior region of the superior longitudinal fasciculus with the projection fibers of the corona radiata and the posterior transverse association fibers for acquisition.
  • Fig. 5 (a) illustrates an exemplary DT approximation
  • Fig. 5(b) illustrates an exemplary DK-derived NG-ODF (min-max scaled)
  • Fig. 5(c) illustrates an exemplary DT-derived color map that shows the position of the magnified views with respect to other brain structures
  • Fig. 5 (a) illustrates an exemplary DT approximation
  • Fig. 5(b) illustrates an exemplary DK-derived NG-ODF (min-max scaled)
  • Fig. 5(c) illustrates an exemplary DT-derived color map that shows the position of the magnified views with respect to other brain structures
  • 5(d) illustrates exemplary fiber directions obtained using the DK (left -unsealed ODF and center — min-max scaled ODF) and DT (right) approximations for several voxels.
  • Voxels with two-fiber populations are also shown in the DK-ODF maps illustrating regions where the posterior SLF interfaces with either corona radiata or transverse association fibers running from left to right.
  • the ODF shows changes in shape and orientation as it represents either mixtures of fibers running anteriorly-posteriorly and vertically oblique or mixtures of fibers oriented anteriorly-posteriorly and from right to left, as illustrated, as example, in Figs. 5(b) and 5(c).
  • Figs. 6(a)-6(d) are illustrations of exemplary brain images of a mean diffusivity, axial diffusivity, radial diffusivity and a fractional anisotropy, respectively, provided by conventional diffusion tensor imaging.
  • the data can be obtained using a, e.g., magnetic resonance imaging scanner.
  • the mean diffusivity illustrated in Fig. 6(a) can be derived, e.g., using Eq. [6].
  • the axial diffusivity illustrated in Fig. 6(b) can be derived, e.g., using Eq. [10].
  • the radial diffusivity illustrated in Fig. 6(c) can be derived, e.g., using Eq.
  • Fig. 6(d) the fractional anisotropy illustrated in Fig. 6(d) can be derived, e.g., using Eq. [12].
  • the mean, axial, and radial diffusivities, provided in Figs. 6(a) - 6(c), respectively, and the fractional anisotropy provided in Fig. 6(d) are similar to what can be generated with conventional diffusion tensor imaging.
  • Calibration bars shown in these figures are in units of ⁇ m 2 /ms for the mean, axial, and radial diffusivities, and calibration bars can be dimensionless for the fractional anisotropy.
  • Figs. 7(a)-7(c) are illustrations of exemplary parametric maps derived with realtime diffusional kurtosis imaging for a single axial brain slice using an exemplary embodiment of the present disclosure.
  • the mean, axial, and radial kurtoses, as illustrated in Figs. 7(a)-7(c), respectively, can be exemplary metrics provided by DKI.
  • the mean kurtosis illustrated in Fig. 7(a) can be derived, e.g., using Eq. [13].
  • the axial kurtosis illustrated in Fig. 7(b) can be derived, e.g., using Eq. [20]
  • the radial kurtosis illustrated in Fig. 7(c) can be derived, e.g., using Eq. [21].
  • Calibration bars can be dimensionless for the mean, axial, and radial kurtoses illustrated in Figs. 7(a)-7(c), respectively.
  • the exemplary simulations included use analytical representations of the diffusion probability distribution function and the ODFs, and may not model the influence of the imaging acquisition scheme and signal-to-noise ratio.
  • the DK-ODF maps of the brain anatomy can correspond to the current knowledge of white matter fiber architecture. Configurations with both two and three crossing fibers as well as unidirectional voxels are all apparent on the ODF maps.
  • One of the exemplary advantages of DKI is that can use low b values (b ⁇ 2500 s/mm2). This can result in diffusion-weighted images with relatively higher signal to noise ratio compared to the signal-to-noise ratio of images used by other ODF techniques. Moreover, the exemplary DK approximation can include only the lower moments of the water diffusion distribution (up to the forth order), thus retaining only the low frequency components of the ODF spherical harmonic spectrum.
  • Additional smoothing may be also introduced by the linearization of the signal in the kurtosis coefficient K that is used to derive the ODF approximation and by using the diffusion and kurtosis tensors to estimate the diffusion and kurtosis coefficients used in the Funk-Radon transform. Consequently, the reconstructed ODFs can be inherently smooth, and do not require further regularization (e.g., spherical convolution), which is usually employed by other ODF techniques. Further, the exemplary derivation of the diffusion and kurtosis tensor employed by DK-ODF can use a limited number of measurements.
  • An alternative exemplary approach for obtaining directional information in regions with complex fiber architecture is to use q-space imaging (QSI) techniques to extract the full diffusion displacement probability distribution.
  • QSI q-space imaging
  • the QSI techniques require a large number of samples and high b values, thus resulting in long imaging times and images with low signal to noise ratio.
  • the DK-ODF model in contrast with the QBI-ODF approximations, has no explicit b- value dependence.
  • Fig. 8 illustrates a flow diagram according to an exemplary embodiment of a method of an exemplary real-time diffusional kurtosis imaging according to the present disclosure.
  • data can be acquired, e.g., using a diffusion-weighted imaging MRI sequence with 3 b-values and 15 or more diffusion directions.
  • the diffusion D and kurtosis K can be calculated for each diffusion direction. Such calculation can be provided, e.g., using the analytic formulae of Eqs. [l]-[3].
  • linear systems as exemplified by Eqs. [4] and [5], can be solved for the diffusion tensor DT and kurtosis tensor KT.
  • the method can rotate to eigenframes that diagonalizes the diffusion tensor, by applying any of several exemplary standard techniques, such as Jacobi's method..
  • analytic formulae or procedures can be used to calculate the desired diffusion metrics, such as mean, axial and radial diffusivities and mean, axial and radial kurtoses.
  • the mean, axial, and radial diffusivities can be derived using Eqs. [6], [10], and [H].
  • the mean, axial, and radial kurtoses can be derived using Eqs. [13], [20], and [21].
  • Such diffusion metrics can provide for real-time diffusional kurtosis imaging.
  • Fig. 9 illustrates a flow diagram of an exemplary method for determining a measure of a diffusional kurtosis according to an exemplary embodiment of the present disclosure.
  • an image can be obtained of an anatomical structure, such as a portion of a brain of a subject, such as a human subject.
  • This exemplary image can be provided by several different methods or procedures, such as, e.g., by MRI.
  • a diffusion weighted image of the image can be provided at procedure 620.
  • the diffusion weighted image can be acquired for three or more b-values, and can be acquired for 15 or more gradient directions. Data can then be received relating to the diffusion weighted image at 630.
  • a measure of a diffusional kurtosis can be determined.
  • This diffusional kurtosis can be measured using a closed form solution at procedure 650, as described above.
  • the measure of the diffusional kurtosis can be determined using a mean kurtosis as well, which can be determined by averaging measures of a diffusion and a kurtosis over each gradient direction.
  • the measure of the diffusional kurtosis can also be an axial kurtosis or a radial kurtosis.
  • an orientation distribution function can be determined at procedure 660.
  • One or more fiber crossings can be resolved using the orientation distribution function at procedure 670.
  • the fiber crossing can be a crossing of two, three or four fibers, or more.
  • a medical condition of the subject can be evaluated using the measure of the diffusional kurtosis at procedure 680, such as Alzheimer's disease, stroke, head trauma, attention deficit hyperactivity disorder or schizophrenia.
  • a measure of diffusional restrictivity can also be determined using the measure of the diffusional kurtosis, at procedure 690.
  • Fig. 10 illustrates a block diagram of an exemplary embodiment of a system according to the present disclosure.
  • a computer 700 can be provided which can have a processor 730 that can be configured or programmed to perform the exemplary steps and/or procedures of the exemplary embodiments of the techniques described above, and those as described herein with respect to the exemplary procedure shown in Fig. 9.
  • a subject/specimen 710 can be positioned and an anatomical region of interest can be selected on the subject/specimen 710, as provided for in procedure 610 above.
  • the imaging device 720 can be used to obtain data for one or more images of the anatomical region of interest.
  • the data/images e.g., diffusion weighted images
  • the data can be stored in a storage arrangement 740 (e.g., hard drive, memory device, such as RAM, ROM, memory stick, floppy drive, etc.).
  • the processor 730 can access the storage arrangement 740 to execute a computer program or a set of instructions (stored on or in the storage arrangement 740) which perform the procedures according to the exemplary embodiments of the present invention.
  • the processor 730 can be configured to perform the exemplary embodiments of the procedures according to the present invention, as described above herein.
  • the processor 730 can be programmed to receive data relating to at least one diffusion weighted image, and determine a measure of a diffusional kurtosis as a function of the received data. This information can be received directly from the imaging device 720 or accessed from the storage arrangement 740. The processor 730 can also be programmed to determine information for measuring a diffusional kurtosis, and can then resolve one or more fiber crossings, evaluate a medical condition of the subject, and/or measure a diffusion restrictivity.
  • a display 750 can also be provided for the exemplary system of Fig. 7.
  • the storage arrangement 740 and the display 750 can be provided within the computer 700 or external from the computer 700.
  • the information received by the processor 730 and the information determined by the processor 730, as well as the information stored on the storage arrangement 740 can be displayed on the display 750 in a user- readable format.
  • exemplary procedures described herein can be stored on any computer accessible medium, including a hard drive, RAM, ROM, removable discs, CD-ROM, memory sticks, etc., and executed by a processing arrangement which can be a microprocessor, mini, macro, mainframe, etc.

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • General Health & Medical Sciences (AREA)
  • Radiology & Medical Imaging (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Veterinary Medicine (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Pathology (AREA)
  • Biophysics (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Public Health (AREA)
  • Biomedical Technology (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Physics & Mathematics (AREA)
  • Geometry (AREA)
  • Quality & Reliability (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)

Abstract

L'invention concerne un procédé exemplaire, un système et un support accessible par un ordinateur pouvant être obtenus pour déterminer une mesure d'aplatissement de diffusion par la réception de données relatives à au moins une image pondérée de diffusion, et pour déterminer une mesure d'un aplatissement de diffusion en fonction des données reçues à l'aide d'une procédure de résolution analytique.
PCT/US2009/053223 2008-08-07 2009-08-07 Système, procédé et support accessible par un ordinateur pour obtenir une imagerie d'aplatissement de diffusion en temps réel Ceased WO2010017524A2 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
CN200980139435.1A CN102202572B (zh) 2008-08-07 2009-08-07 用于提供实时扩散峭度成像的系统和方法
US13/022,488 US8811706B2 (en) 2008-08-07 2011-02-07 System, method and computer accessible medium for providing real-time diffusional kurtosis imaging and for facilitating estimation of tensors and tensor-derived measures in diffusional kurtosis imaging
US14/462,215 US9965862B2 (en) 2008-08-07 2014-08-18 System, method and computer accessible medium for providing real-time diffusional kurtosis imaging and for facilitating estimation of tensors and tensor-derived measures in diffusional kurtosis imaging

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US8711108P 2008-08-07 2008-08-07
US61/087,111 2008-08-07

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US13/022,488 Continuation-In-Part US8811706B2 (en) 2008-08-07 2011-02-07 System, method and computer accessible medium for providing real-time diffusional kurtosis imaging and for facilitating estimation of tensors and tensor-derived measures in diffusional kurtosis imaging

Publications (2)

Publication Number Publication Date
WO2010017524A2 true WO2010017524A2 (fr) 2010-02-11
WO2010017524A3 WO2010017524A3 (fr) 2010-04-01

Family

ID=41664226

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2009/053223 Ceased WO2010017524A2 (fr) 2008-08-07 2009-08-07 Système, procédé et support accessible par un ordinateur pour obtenir une imagerie d'aplatissement de diffusion en temps réel

Country Status (2)

Country Link
CN (1) CN102202572B (fr)
WO (1) WO2010017524A2 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110282183A1 (en) * 2010-05-17 2011-11-17 Washington University In St. Louis Diagnosis Of Central Nervous System White Matter Pathology Using Diffusion MRI
CN102542534A (zh) * 2010-12-31 2012-07-04 北京海思威科技有限公司 基于图像轮廓的图像畸变校正方法和装置

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103142229B (zh) * 2013-02-22 2015-12-02 天津大学 扩散峭度张量成像的高阶张量特征参数提取方法
GB201319619D0 (en) * 2013-11-06 2013-12-18 Diamentech Ltd System for assessing a mental health disorder
CN105574849A (zh) * 2015-11-25 2016-05-11 天津大学 一种基于扩散峭度张量的白质微结构特征可视化方法
CN110018432B (zh) * 2019-02-14 2020-09-11 清华大学 一种基于弥散张量的神经成像方法、装置和磁共振成像设备
CN117233676B (zh) * 2023-11-15 2024-03-26 之江实验室 一种回波时间依赖的磁共振扩散成像信号生成方法和装置

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6882876B2 (en) * 2001-03-27 2005-04-19 Cornell Research Foundation, Inc. Diagnosis of normal pressure hydrocephalus by automated processing of MR images
US7995825B2 (en) * 2001-04-05 2011-08-09 Mayo Foundation For Medical Education Histogram segmentation of FLAIR images
WO2005012926A2 (fr) * 2003-07-08 2005-02-10 The Government Of The United States Of America, Represented By The Secretary Of The Department Of Health And Human Services Caracterisation d'echantillon par irm du tenseur de diffusion et irm q-space
JP4752719B2 (ja) * 2006-10-19 2011-08-17 ソニー株式会社 画像処理装置、画像取得方法及びプログラム

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110282183A1 (en) * 2010-05-17 2011-11-17 Washington University In St. Louis Diagnosis Of Central Nervous System White Matter Pathology Using Diffusion MRI
US9494669B2 (en) * 2010-05-17 2016-11-15 Washington University Diagnosis of central nervous system white matter pathology using diffusion MRI
US10962619B2 (en) 2010-05-17 2021-03-30 Washington University Diagnosis of central nervous system white matter pathology using diffusion MRI
CN102542534A (zh) * 2010-12-31 2012-07-04 北京海思威科技有限公司 基于图像轮廓的图像畸变校正方法和装置

Also Published As

Publication number Publication date
CN102202572A (zh) 2011-09-28
CN102202572B (zh) 2014-11-19
WO2010017524A3 (fr) 2010-04-01

Similar Documents

Publication Publication Date Title
Yeh et al. Generalized ${q} $-sampling imaging
Vos et al. The influence of complex white matter architecture on the mean diffusivity in diffusion tensor MRI of the human brain
US8811706B2 (en) System, method and computer accessible medium for providing real-time diffusional kurtosis imaging and for facilitating estimation of tensors and tensor-derived measures in diffusional kurtosis imaging
Alexander et al. Orientationally invariant indices of axon diameter and density from diffusion MRI
Jeurissen et al. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data
US5969524A (en) Method to significantly reduce bias and variance of diffusion anisotrophy measurements
US9965862B2 (en) System, method and computer accessible medium for providing real-time diffusional kurtosis imaging and for facilitating estimation of tensors and tensor-derived measures in diffusional kurtosis imaging
Alexander An introduction to computational diffusion MRI: the diffusion tensor and beyond
US7034531B1 (en) Diffusion MRI using spherical shell sampling
Velázquez et al. Cortical thickness estimation: a comparison of FreeSurfer and three voxel-based methods in a test–retest analysis and a clinical application
WO2010017524A2 (fr) Système, procédé et support accessible par un ordinateur pour obtenir une imagerie d'aplatissement de diffusion en temps réel
EP2147330B1 (fr) Procédé de traitement d'image
US6853189B1 (en) Method and system of quantitatively assessing diffusion characteristics in a tissue
US11789106B2 (en) Magnetic resonance method, software product, and system for determining a diffusion propagator or related diffusion parameters for spin-labelled particles
Alexander et al. Mathematics of crossing fibers
Griffanti et al. Signal-to-noise ratio of diffusion weighted magnetic resonance imaging: Estimation methods and in vivo application to spinal cord
Hirsch et al. Deviations from the diffusion tensor model as revealed by contour plot visualization using high angular resolution diffusion-weighted imaging (HARDI)
Zhan et al. How many gradients are sufficient in high-angular resolution diffusion imaging (HARDI)
Ulloa et al. Extra-axonal contribution to double diffusion encoding-based pore size estimates in the corticospinal tract
Dhollander et al. Feasibility and advantages of diffusion weighted imaging atlas construction in Q-space
Haghighi et al. Improved human brain tractographs using multi-shell q-ball diffusion magnetic resonance imaging compared with DTI
Veraart et al. Diffusion kurtosis imaging
Pathak Improved quantification of connectivity in human brain mapping
Jensen Frontiers of microstructural with diffusion MRI imaging
Blommaert et al. using diffusion MRI

Legal Events

Date Code Title Description
WWE Wipo information: entry into national phase

Ref document number: 200980139435.1

Country of ref document: CN

121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 09805635

Country of ref document: EP

Kind code of ref document: A2

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC, EPO FORM 1205A OF 17-06-2011

122 Ep: pct application non-entry in european phase

Ref document number: 09805635

Country of ref document: EP

Kind code of ref document: A2