WO2001038895A1 - Procedes et systemes permettant de generer des representations des faisceaux snc - Google Patents

Procedes et systemes permettant de generer des representations des faisceaux snc Download PDF

Info

Publication number
WO2001038895A1
WO2001038895A1 PCT/US2000/032358 US0032358W WO0138895A1 WO 2001038895 A1 WO2001038895 A1 WO 2001038895A1 US 0032358 W US0032358 W US 0032358W WO 0138895 A1 WO0138895 A1 WO 0138895A1
Authority
WO
WIPO (PCT)
Prior art keywords
diffusion
images
magnetic resonance
anisotropy
determining
Prior art date
Application number
PCT/US2000/032358
Other languages
English (en)
Inventor
Xiaohong Zhou
Original Assignee
Board Of Regents, The University Of Texas System
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 Board Of Regents, The University Of Texas System filed Critical Board Of Regents, The University Of Texas System
Priority to AU18034/01A priority Critical patent/AU1803401A/en
Publication of WO2001038895A1 publication Critical patent/WO2001038895A1/fr

Links

Classifications

    • 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

Definitions

  • the present invention relates generally to the field of locating fiber tracts in a nervous system. More particularly, it concerns methods and systems for generating brain and spinal tractograms using diffusion-weighted magnetic resonance imaging.
  • the human central nervous system is a network of communications established by fiber tracts for the nerve cells to emit and receive signals. Exploring the neuronal connectivity by tracing the fiber tracts may provide valuable information to understand nervous system functions (including brain functions), to explain the mechanisms of neurological disorders, and to serve as a guide to surgical intervention. Over the past century, considerable efforts have been made towards tracing the neuronal connectivity of the central nervous system. In spite of enormous progress, almost all techniques to-date are invasive and/or destructive, thus excluding them from clinical use on human subjects. Therefore, a non-invasive tractographic technique that may be directly applied to patients would be desirable.
  • MRI Magnetic resonance Imaging
  • diffusion imaging may be used to detect cerebral ischemia shortly after onset. This is important since therapeutic intervention using tissue perfusive agents is effective only within a narrow time window. Stimulated by the success in qualitative diffusion-weighted imaging, the interest in quantitative diffusion imaging is growing.
  • ADC apparent diffusion coefficient
  • Diffusion in biological tissues is a complicated phenomenon. Depending on the tissue structures, diffusion may be spatially isotropic (such as in cerebrospinal fluid), or anisotropic (such as in white matter tracts). Diffusion anisotropy in biological tissues has been recognized for many years. Recently, a number of research groups began to study certain aspects of tissue diffusion anisotropy using MR imaging techniques. However, a robust 3D tractographic technique for routine clinical use has not been developed because of numerous technical difficulties such as insufficient contrast, low signal-to-noise ratio, problems due to patient movement, and problems due to image misregistration. It would therefore be advantageous to have the ability to generate non-invasive tractograms. exhibiting few if any technical difficulties, for routine clinical applications and for scientific and medical research.
  • the invention is a method for generating a tractogram of a subject.
  • a plurality of diffusion-weighting gradients are applied to the subject.
  • a plurality of magnetic resonance signals arising from the application of the plurality of diffusion-weighting gradients are received.
  • a plurality of diffusion-weighted magnetic resonance images are determined using the plurality of magnetic resonance signals.
  • a plurality of diffusion anisotropy images are determined using the plurality of diffusion-weighted magnetic resonance images.
  • the tractogram are generated using the plurality of diffusion anisotropy images.
  • the plurality of diffusion-weighting gradients may include two or more sets of gradients, each set of gradients including a different diffusion- weighting gradient amplitude.
  • Each set of the plurality of diffusion-weighting gradients may include six or more non-overlapping orientations, the orientations being substantially identical in each of the sets.
  • the orientations may be evenly distributed in three dimensions. Three of the orientations may be applied along three orthogonal axes with respect to the subject and the remaining orientations may be evenly distributed in three dimensions.
  • One of the sets may include a diffusion- weighting gradient amplitude of about zero, and at least one other set may include a diffusion-weighting gradient that is non-zero.
  • the one set including a diffusion- weighting gradient amplitude of about zero may include a single orientation, and the at least one other set may include six or more non-overlapping orientations.
  • the diffusion-weighted magnetic resonance images and the anisotropy images may be three-dimensional.
  • the diffusion-weighted magnetic resonance images and the anisotropy images may be obtained by interleaving a plurality of two-dimensional slice images.
  • the two-dimensional slice images may be offset by a predetermined amount in a slice-selection direction.
  • the method may also include averaging a plurality of the diffusion-weighted magnetic resonance images from repeated acquisition.
  • Determining the plurality of diffusion anisotropy images may include determining diffusion tensor elements using the plurality of diffusion- weighted magnetic resonance images; determining eigenvalues of a matrix defined by the diffusion tensor elements; and determining a relative anisotropy, a fraction anisotropy, a volume ratio, an anisotropy index, or any combination thereof using the eigenvalues.
  • the determining diffusion tensor elements may include using a least- squares algorithm.
  • the method may also include compensating the plurality of magnetic resonance signals for eddy currents.
  • Applying a plurality of diffusion- weighting gradients may include single-shot imaging.
  • the single-shot imaging may include echo planar imaging, fast spin echo imaging, or any combination thereof.
  • Applying a plurality of diffusion-weighting gradients may include multi-shot imaging.
  • the multi-shot imaging may include echo planar imaging, fast spin echo imaging, or any combination thereof.
  • Generating the tractogram may include using a maximum intensity projection algorithm.
  • the tractogram may include a brain tractogram.
  • the tractogram may include a spinal tractogram.
  • the invention is a method for generating a tractogram of a subject.
  • a first set of diffusion-weighting gradients is applied to the subject, the first set including six or more non-overlapping orientations, each gradient of the first set having a first gradient amplitude.
  • a second set of diffusion-weighting gradients is applied to the subject, the second set including the six or more non-overlapping orientations, each gradient of the second set having a second gradient amplitude not equal to the first gradient amplitude.
  • a plurality of magnetic resonance signals arising from the application of the first and second sets of diffusion-weighting gradients is received.
  • a plurality of diffusion-weighted magnetic resonance images are determined using the plurality of magnetic resonance signals.
  • Diffusion tensor elements are determined using the plurality of diffusion-weighted magnetic resonance images. Eigenvalues of a matrix defined by the diffusion tensor elements are determined. A plurality of diffusion anisotropy images are determined using the eigenvalues. The tractogram is generated using the plurality of diffusion anisotropy images.
  • the orientations may be evenly distributed in three dimensions.
  • Three of the orientations may be applied along three orthogonal axes with respect to the subject and the remaining orientations may be evenly distributed in three dimensions.
  • the diffusion-weighted magnetic resonance images and the anisotropy images may be three-dimensional.
  • the diffusion- weighted magnetic resonance images and the anisotropy images may be obtained by interleaving a plurality of two-dimensional slice images.
  • the two-dimensional slice images may be offset by a predetermined amount in a slice-selection direction.
  • the method may also include averaging a plurality of the diffusion-weighted magnetic resonance images from repeated acquisition.
  • Determining the plurality of diffusion anisotropy images may include determining a relative anisotropy, a fraction anisotropy, a volume ratio, an anisotropy index, or any combination thereof using the eigenvalues.
  • Determining diffusion tensor elements may include using a least-squares algorithm.
  • the method may also include compensating the plurality of magnetic resonance signals for eddy currents.
  • Applying the first and second sets of diffusion-weighting gradients may include single-shot imaging.
  • the single-shot imaging may include echo planar imaging, fast spin echo imaging, or any combination thereof.
  • Applying the first and second sets of diffusion-weighting gradients may include multi-shot imaging.
  • the multi-shot imaging may include echo planar imaging, fast spin echo imaging, or any combination thereof.
  • Generating the tractogram may include using a maximum intensity projection algorithm.
  • the invention is a system for generating a tractogram, including a magnetic resonance imaging device, a memory, and a microprocessor.
  • the magnetic resonance imaging device is configured to apply to a subject two or more sets of diffusion-weighting gradients, each set comprising six or more non- overlapping orientations and to receive a plurality of corresponding magnetic resonance signals.
  • the memory is configured to store information corresponding to the magnetic resonance signals.
  • the microprocessor is in communication with the memory and is configured to perform instructions including: determining a plurality of diffusion-weighted magnetic resonance images using the information corresponding to the magnetic resonance signals; determining a plurality of diffusion anisotropy images using the plurality of diffusion-weighted magnetic resonance images; and generating the tractogram using the plurality of diffusion anisotropy images.
  • determining a plurality of diffusion anisotropy images may include: determining diffusion tensor elements using the plurality of diffusion- weighted magnetic resonance images; determining eigenvalues of a matrix defined by the diffusion tensor elements; and determining a plurality of diffusion anisotropy images using the eigenvalues.
  • Determining diffusion tensor elements may include using a least-square algorithm.
  • Generating the tractogram may include using a maximum intensity projection algorithm.
  • the invention is a computer readable media containing program instructions for generating a tractogram.
  • the computer readable media includes instructions for determining a plurality of diffusion-weighted magnetic resonance images using information corresponding to a plurality of magnetic resonance signals. It also includes instructions for determining diffusion tensor elements using the plurality of diffusion-weighted magnetic resonance images. It also includes instructions for determining eigenvalues of a matrix defined by the diffusion tensor elements. It also includes instructions for determining a plurality of diffusion anisotropy images using the eigenvalues. It also includes instructions for generating the tractogram using the plurality of diffusion anisotropy images.
  • diffusion-weighting gradient means a gradient to encode diffusion information into one or more MRI signals.
  • Diffusion tensor means a mathematical description of diffusion properties of a material, including biological tissue.
  • Tractography means a technique to produce an image that highlights one or more fiber tracts and suppresses background tissues.
  • Tractogram means an image that highlights one or more fiber tracts with minimal signal from the background tissues.
  • Diffusion anisotropy means molecular diffusion with spatially preferred directions in certain materials, including biological tissues.
  • FIG.l shows a process flow for generating a tractogram of a subject in accordance with the present disclosure.
  • FIG.2 shows a diffusion-weighted multi-shot Echo Planar Imaging (EPI) pulse sequence in accordance with the present disclosure.
  • EPI Echo Planar Imaging
  • FIG.3 shows a diffusion-weighted single-shot Fast Spin Echo (FSE) pulse sequence in accordance with the present disclosure.
  • FSE Fast Spin Echo
  • FIG.4A shows a diffusion-weighted EPI image free of eddy currents.
  • FIG. 4B shows a diffusion-weighted EPI image shifted by spatially invariant eddy currents.
  • FIG.5A shows a diffusion-weighted EPI image that has been sheared by spatially linear eddy currents along the readout direction.
  • FIG.5B shows a diffusion-weighted EPI image that has been compressed by spatially linear eddy currents along the phase-encoding direction.
  • FIG. 6 shows a schematic diagram of a system for generating a tractogram in accordance with the present disclosure.
  • FIG. 8 shows a graph of contrast-to-noise ratio (CNR) versus b-value in accordance with the present disclosure.
  • FIG. 9 shows a graph of CNR versus number of gradient orientations, n, in accordance with the present disclosure.
  • FIG. 10 shows a diffusion-tensor image.
  • b 2500 s/mm in accordance with the present disclosure.
  • FIG. 11 shows a diffusion-tensor image.
  • A. 55
  • signal averaging 1.
  • b 1000 s/mm " in accordance with the present disclosure.
  • Fiber tracts in the central nervous system include bundles of axons grouped together along certain axes. Because of this directional linkage and connectivity, water molecules diffuse more freely along the fiber tracts than along other directions. By exploiting this diffusion anisotropy at each spatial location, fiber tracts may be separated from the background tissues. This provides a basis for certain tractographic techniques disclosed herein.
  • the present disclosure which uses techniques that may be referred to as magnetic resonance tractography (MRT), provides for the ability to visualize three-dimensional location and orientation of fiber tracts in the central nervous system, including tracts in the brain and spine.
  • MRT magnetic resonance tractography
  • Molecular diffusion in anisotropic media such as fiber tracts in the brain, may generally be characterized by a second rank tensor.
  • this tensor may be expressed as a 3x3 matrix:
  • one embodiment of the presently disclosed tractographic technique includes steps illustrated in FIG. 1.
  • step 20 of general process flow 10 diffusion- weighting gradients are applied to a subject by any one of several techniques known in the art. United States Patent No. 5,864,233. which is hereby incorporated by reference in its entirety, describes at least one technique suitable for this step.
  • the subject may be a human, but it will be understood that techniques disclosed herein apply equally well to non-human subjects.
  • step 30 magnetic resonance (MR) signals, arising from the subject in the presence of the diffusion- weighting gradients, are received.
  • step 40 diffusion-weighted MR images are reconstructed using the MR signals acquired in step 30.
  • step 50 diffusion anisotropy images -are determined using the diffusion-weighted MR images acquired in step 40.
  • a tractogram is generated using the diffusion anisotropy images acquired in step 50.
  • step 50 may, in one embodiment, include steps 42, 44, and 46.
  • step 42 diffusion tensor elements, such as those set forth in Equation [1] are determined using the diffusion-weighted MR images acquired in step 40.
  • step 44 eigenvalues of the tensor matrix are determined by any one of several methods known in the art, and in step 46 diffusion anisotropy is determined using those eigenvalues. The description below, directed mostly to specific embodiments of the present disclosure, more specifically explains the steps illustrated in FIG. 1.
  • step 20 of FIG. 1 involves the application of diffusion-weighting gradients to a subject.
  • a diffusion-weighting gradient G dl with a fixed amplitude G dl may be applied to the subject to be imaged.
  • the diffusion- weighting gradient may change its orientation in 3D space along n non- overlapping directions (with, in one embodiment, n being no less than 6): G ⁇ lll ,G ⁇ l ,...,G Jln .
  • a diffusion-weighted MR image may be obtained in 3D as illustrated in steps 30 and 40 of FIG. 1.
  • the intensities of these images which may be denoted as S n ,S l2 , S j3 , ..., S, n , may be mathematically expressed as
  • G d ⁇ JX , G ⁇ Jy and G d/JZ are the three orthogonal gradient components, respectively, and p q ⁇ and r ⁇ are the corresponding directional cosines of G dl .
  • an additional set of diffusion-weighted images may be acquired with a different diffusion-weighting gradient amplitude G dl .
  • This second data set may be denoted as S :l . S :2 , S 23 , ..., S 2 réelle .
  • G d2 0
  • only one single image is required since S : ⁇ ,S 22 , S,, , ..., S 2n are the same (except for noise).
  • EPI echo planar imaging
  • FSE fast-spin echo
  • EPI may be used for the brain
  • FSE may be used for the spine. This distinction is primarily based on the fact that FSE may be more immune to the magnetic susceptibility variations in the spine region while EPI may give better SNR and resolution (as well as contrast) in the brain.
  • an EPI sequence such as the one illustrated in FIG. 2 may be used.
  • FIG. 2 there is shown a diffusion-weighted multi-shot EPI pulse sequence.
  • the single-shot version may be obtained by fixing the pre-phase-encoding gradient to a constant.
  • the diffusion-weighting gradients (shaded) straddle the 180° RF pulse and their amplitude and orientation are varied throughout the acquisition.
  • an FSE sequence such as the one illustrated in FIG. 3 may be used to acquire the diffusion-weighting images.
  • FIG. 3 there is shown a diffusion-weighted FSE pulse sequence.
  • the pulse sequence may be used either in a single-shot acquisition or in a multi-shot acquisition. Diffusion-weighting gradients (shaded) are applied before and after the 180° RF pulse. The 90° pulse following the 180° pulse returns the magnetization to the longitudinal axis. Any residual magnetization on the transverse plane is crushed out by the spoiling gradient (Gsp) on the phase-encoding axis. Details of the sequence after the spoiling gradient may be found in the art. such as in D. G. Norris. P. Bornert, T. Reese. D. Leibfritz, "On the Application of Ultra-fast RARE Experiments," Magn. Reson. Med., 27: 142-164 (1992), which is hereby incorporated by reference in its entirety.
  • a single-shot, multi-slice technique may be used to produce diffusion-weighted images in multiple directions ( 6) with b-values ranging from 0 to about 3000 s/mm " .
  • a single-shot, multi-slice technique may be used to produce diffusion-weighted images in multiple directions ( 6) with b-values ranging from 0 to about 3000 s/mm " .
  • it is expected that one may obtain acceptable diffusion-weighted images with relatively low spatial resolution e.g., 128 matrix on a 24cm field-of-view for the brain with a slice thickness of about 5-7mm.
  • a multi-shot diffusion imaging techniques may be used, which may improve the spatial resolution. With such a technique, is expected that one may obtain a spatial resolution of about 1 mm for the brain and about 2mm for the spine (256x256 on 24 and 48 cm f ⁇ eld-of-views, respectively).
  • Example 1 discussed below addresses the optimization of gradient orientations.
  • signal averaging may, in some embodiments, be necessary to improve signal-to-noise ratio.
  • an acquisition scheme may be utilized that achieves signal averaging by increasing the total number of gradient orientations. For example, if n gradient orientations are required and m averages are planned for each orientation, instead of treating the two operations separately, one may acquire the images with a total of n *m different diffusion gradient orientations.
  • Another issue in diffusion gradient optimization involves how to determine the directions for a given number of gradient orientations.
  • one may evenly distribute the gradient orientations in a 3D sphere.
  • Such a scheme may provide an equal weighting for all fiber tracts irrespective of their orientations.
  • the first three gradient orientations may be selected along the three orthogonal axes x, y and z.
  • the remaining n-3 orientations may be selected with equal solid-angle weighting on the 3D sphere.
  • steps 50 and 60 of FIG. 1 involve the determination of diffusion anisotropy images and the generation of a tractogram.
  • Equation [4] where ⁇ l and ⁇ 2 are constants that are proportional to the square of the diffusion- weighting gradient amplitudes, G dl and G d2 , respectively, and are determined by the diffusion-weighting gradient waveforms (such as position, duration and shape).
  • the explicit expressions of ⁇ , and /L may be obtained by carrying out the integrals in Equation 3 above.
  • the six independent tensor elements may be uniquely determined (see step 42 of FIG. 1 ).
  • the tensor elements may be unambiguously obtained using a least-squares algorithm, as is known in the art (see step 42 of FIG. 1 ).
  • Equation [1] The diffusion tensor elements obtained above are patient-orientation dependent.
  • the matrix defined by Equation [1] may be mathematically manipulated. A common way, but not the only way, is to diagonalize the matrix in Equation [1] to the following form (see step 44 of FIG. 1):
  • Equation [5] is used only to demonstrate the concept that a set of orientation-independent parameters may be obtainable by diagonalization. Those having skill in the art will recognize that, under some circumstances, one may choose not to use the diagonalization technique due to its sensitivity to noise, as is known in the art through articles such as A.M. Ulug and P.C. van Zijl, JMRI, 9:804-13, 1999, which is hereby incorporated by reference in its entirety.
  • an anisotropy parameter may be used to form diffusion anisotropy images as illustrated in steps 46 and 50 of FIG. 1.
  • One or a combination of diffusion anisotropy parameters known in the art may be used in this step.
  • diffusion anisotropy parameters such as relative anisotropy (RA, Equation [12]), fractional anisotropy (FA, Equation [13]), volume ratio (VR. Equation [14]), and anisotropy index (AI, Equation [15]). Again, any one or any combination of such parameters may be used in accordance with the present disclosure.
  • AI 2( ' 3D c 2 vg - 2D s 2 urf f - D v 2 ol ,m ) '
  • 3D diffusion- weighted images may be acquired directly using a 3D pulse sequence.
  • multiple sets of multi- slice images may be acquired using a 2D EPI or FSE pulse sequence.
  • the multiple 2D image sets may be offset by a predetermined amount along the slice-selection direction, and interleaved together to form a 3D image. If a 3mm slice thickness is used with a 3mm gap, a total of two interleaves with 20 slices in each set should cover an entire brain. Since a relatively long TR (3-5s) may be used in the diffusion- weighted sequences, it is expected that all 20 images in an interleave group may be acquired within a single TR.
  • TR 3-5s
  • tractograms may be generated using maximum intensity projection (MIP) algorithms which may project a two-dimensional plane along a number of specified directions, as is known in the art through references such as United States Patent No. 5,566.282, which is hereby incorporated by reference in its entirety.
  • MIP maximum intensity projection
  • an algorithm such as the one published in W. Dixon, L. Du, D. Faul, et al., "Projection angiograms of blood labeled by adiabatic fast passage " . Magn. Reson. Med., 3:454-462. 1986, which is hereby incorporated by reference, may be used.
  • the projected 2D images constitute the tractograms mentioned in step 60 of FIG. 1.
  • eddy currents In diffusion-weighted imaging, strong diffusion-weighting gradients may induce eddy currents, which in turn produce a time-dependent perturbation magnetic field.
  • This magnetic field may be decomposed into a spatially invariant component, b ⁇ (t) , three linear gradient components, g x (t), g y (t), and g z (t) , and spatially higher-order terms (64).
  • b ⁇ (t) three linear gradient components, g x (t), g y (t), and g z (t)
  • spatially higher-order terms 64
  • various kinds of image quality problems can be produced, as is known in the art.
  • the spatially invariant component bo(t) may cause an EPI image to shift along the phase-encoding direction (FIGS. 4 A and 4B).
  • FIG. 6 there is shown a system 100 suitable for generating a tractogram in accordance with the present invention.
  • the system includes a magnetic resonance imaging (MRI) scanner 105, a memory 1 10, a network 120, a storage device 130. a microprocessor 140, a personal computer 150, a printer 160, and a hard-copy tractogram 170.
  • MRI scanner 105 is configured to apply diffusion- weighting gradients to a subject.
  • Suitable devices are varied and include systems such as those disclosed in United States Patent No. 5,864.233, which has been incorporated by reference and United States Patent No. 5,923,168, which is hereby incorporated by reference in its entirety.
  • pulse sequences may be implemented on a 1.5T GE Signa Lx-NV/i scanner, which is available commercially.
  • the GE Signa scanner is equipped with a gradient system capable of producing a maximum gradient of about 40 mT/m with a slew rate of about 150 T/m/s.
  • the gradient system is capable of producing a broad range of b- values (at least from 0 to about 3000 s/mm ).
  • Memory 110 may be configured to store information corresponding to magnetic resonance signals arising from the application of diffusion- weighting gradients to the subject.
  • memory 110 may be integrated with MRI scanner 105. Any device suitable for storing data, permanently or temporarily, may be used as memory 1 10. For instance, random access memory, a hard drive, a tape drive, an optical drive or the like may be used. In one embodiment, a separate memory 1 10 may not be needed. In such an embodiment, MRI scanner 105 may directly transfer information to a device suitable for making one or more calculations on-the-fly. without need for specific storage of that information.
  • Network 120 Shown in FIG. 6 is network 120.
  • Network 120 is illustrated to emphasize that MRI scanner 105 and memory 1 10 may be remotely connected to a calculation device, such as personal computer 150. In fact, MRI scanner 105 and memory 1 10 (or any equipment that may transfer information) may be remotely connected through a network or other suitable means.
  • Network 120 may be any one of various types of networks. In one embodiment, network 120 may be the Internet. In other embodiments, network 120 may be a Local Area Network (LAN), a Wide Area Network (WAN), an intranet, or the like. In one embodiment, network 120 may not be needed. In such an embodiment, one or more pieces of equipment may be coupled directly.
  • Storage device 130 illustrates that system 100 may employ more than one memory storage for information.
  • information from MRI scanner 105 is transferred to memory 110.
  • the information may then be transferred over network 120 to storage device 130 so that a calculation device, such as personal computer 150, may access that information.
  • storage device 130 may be an external hard drive.
  • storage device 130 may be an internal hard drive of personal computer 150.
  • storage device 130 may be random access memory, an optical drive, a tape drive, a floppy drive, or the like.
  • storage device 130 may not be needed.
  • information may be transferred from MRI scanner 105 directly to personal computer 150, or it may be transferred from MRI scanner 105 to memory 110 to personal computer 150.
  • Microprocessor 140 is configured to perform the instructions disclosed herein to convert information from MR device 110 into tractogram 170. Specifically, microprocessor 140 may be configured to determine a plurality of diffusion-weighted resonance images using signals from MR device 1 10, determine a plurality of diffusion anisotropy images using those images, and generate tractogram 170 using those anisotropy images. More generally, microprocessor 140 may be instructed to perform any one or combination of operations discussed in FIG. 1 and throughout this description. In the illustrated embodiment, microprocessor 140 is the Central
  • microprocessor 140 may be the processing unit of any number of other calculating devices, such as but not limited to, a hand-held computer, a laptop computer, a personal digital assistant, or the like.
  • software suitable to provide instructions to microprocessor 140 may be used. Specifically software may be used to perform any one or combination of steps depicted in FIG. 1 and described throughout this specification. More specifically, software may be utilized that includes instructions for determining a plurality of diffusion-weighted magnetic resonance images using information from MRI scanner 105 following application of diffusion gradients to a subject. The software may also include instructions for determining diffusion tensor elements as discussed herein and for determining eigenvalues for a matrix defined by those elements. The software may include instructions for determining a plurality of diffusion anisotropy images as discussed herein and may correspondingly produce tractogram 170.
  • a program may be written in a language such as C++, Fortran, or Visual Basic.
  • a language specifically designed to interface with MRI scanner 105 may be utilized.
  • commercially available mathematic processing software such as MATHEMATICA and MatLab may be instructed to carry out any one or combination of instructions disclosed in this specification.
  • all image processing software may be written in C or C++ as separate modules to an existing software package such as FuncTool (GE Medical Systems, Milwaukee. Wisconsin).
  • image visualization software may be developed using the Visualization Toolkit (Prentice Hall, Upper Saddle River, NJ).
  • tractogram 170 any one of several appropriate graphical programs may be used, as is known in the art.
  • the pre-calculated tractograms may be included in a cine loop and presented as a movie, as is known in the art.
  • the tractograms may also be displayed, in conjunction with high-resolution anatomic images, in any arbitrary orientations and view angles.
  • Personal computer 160 may be any device suitable for interfacing with microprocessor 140. Again, it may include but is not limited to personal computers, hand-held computers, laptop computers, personal digital assistants, and the like. In one embodiment, diffusion anisotropy image calculations, 3Dimage synthesis, and tractogram generation may all be performed on an SGI Indigo-2 Workstation (Silicon Graphics. Inc., Mountain View, CA) accessed remotely through a desktop personal computer.
  • SGI Indigo-2 Workstation Silicon Graphics. Inc., Mountain View, CA
  • tractogram 170 may be presented in various forms. It may be printed, viewed directly on a monitor, viewed as a movie, or by any other means suitable to convey the tractogram information to a user.
  • the following examples are included to demonstrate specific embodiments of the invention. It should be appreciated by those of skill in the art that the techniques disclosed in the examples which follow represent techniques discovered by the inventor to function well in the practice of the invention, and thus can be considered to constitute specific modes for its practice. However, those of skill in the art should, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the spirit and scope of the invention.
  • contrast between fiber tracts and surrounding tissues arises from diffusion anisotropy, calculated from a set of diffusion-weighted images acquired with different b-values and diffusion-gradient orientations. An optimal combination of these parameters is desired for a given acquisition time to maximize image quality.
  • different b-values and number of gradient orientations (n) are investigated on normal human volunteers. Image quality was quantitatively analyzed in terms of contrast ratio (CR) and contrast-to-noise ratio (CNR) between major tracts and surrounding tissue.
  • CR contrast ratio
  • CNR contrast-to-noise ratio
  • a diffusion-tensor pulse sequence which was a variation of a single-shot EPI sequence, was used. This sequence allows multiple diffusion-gradient orientations to be used in a single acquisition.
  • the diffusion images were processed with GE's FuncTool Analysis package, using singular-value decomposition to calculate the diffusion tensor elements and generate the diffusion anisotropy maps.
  • n 6
  • n 6
  • 8 NEX 8 NEX
  • NEX 8 NEX
  • Data sets with larger n were acquired for comparison, with correspondingly decreased number of averages, chosen to keep acquisition time constant at 4 minutes.
  • An iterative algorithm was used to calculate the orientations, ( ⁇ , ⁇ ) weighted equally by solid angle.
  • CR and CNR were computed for several regions of interest: splenium, left rightcorticospinal tracts, and left right arcuate fasciculus. Gray matter without tracts in the frontal lobe was used as a contrast reference.
  • FIG. 10 The dependence of maximum CNR on n is given in FIG. 9.
  • FIG. 10 Two relative diffusion anisotropy images are compared in FIG. 10 and FIG. 1 1.
  • DTI Diffusion tensor imaging
  • DTI may provide a wealth of information on molecular diffusion in biological tissues.
  • One application is to visualize the white- matter tracts using diffusion anisotropy maps.
  • Knowledge of the white-matter tracts is important in pre-surgical planning and post-surgical assessment for patients with brain lesions. For this purpose, the clinical applicability of a magnetic resonance tractography technique based on DTI has been evaluated.
  • the DTI pulse sequence used in this study was modified from a commercial diffusion- weighted, single-shot echo planar imaging (EPI) sequence.
  • This sequence is the single-shot version of the sequence shown in FIG. 2.
  • the sequence is capable of re-orienting the diffusion gradient in a number of pre-determined directions (>6).
  • the maximum available gradient 40mT/m
  • a correction mechanism as disclosed herein was included in the sequence.
  • SNR signal-to-noise ratio
  • the sequence employed magnitude averaging to avoid the phase inconsistency among the individual diffusion images.

Landscapes

  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Vascular Medicine (AREA)
  • General Health & Medical Sciences (AREA)
  • Radiology & Medical Imaging (AREA)
  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Condensed Matter Physics & Semiconductors (AREA)
  • General Physics & Mathematics (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)

Abstract

L'invention concerne des procédés et des systèmes de visualisation des faisceaux de substance blanche dans le système nerveux central, notamment le cerveau et la colonne vertébrale. Au moins deux ensembles d'images par résonance magnétique (RM) pondérée en diffusion sont obtenues à l'aide d'une séquence d'impulsions d'imagerie rapide, telle que l'imagerie écho planaire, le spin-écho rapide, ou une combinaison des deux. Les éléments de l'anisotropie du tenseur de diffusion pour chaque voxel de l'image sont calculés à l'aide d'un algorithme par les moindres carrés. Un image d'anisotropie de la diffusion à trois dimensions est obtenue soit directement à partir des images RM pondérées en diffusion à trois dimensions soit par reformattage des images RM multi-coupes à deux dimensions acquises séparément. L'invention concerne ainsi un système permettant de mettre en évidence les faisceaux de substance blanche selon un angle préféré à l'intérieur d'une plaque sélectionnée à l'aide d'un algorithme de projection à intensité maximale.
PCT/US2000/032358 1999-11-24 2000-11-22 Procedes et systemes permettant de generer des representations des faisceaux snc WO2001038895A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU18034/01A AU1803401A (en) 1999-11-24 2000-11-22 Methods and systems for generating tractograms

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US44917099A 1999-11-24 1999-11-24
US09/449,170 1999-11-24

Publications (1)

Publication Number Publication Date
WO2001038895A1 true WO2001038895A1 (fr) 2001-05-31

Family

ID=23783156

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2000/032358 WO2001038895A1 (fr) 1999-11-24 2000-11-22 Procedes et systemes permettant de generer des representations des faisceaux snc

Country Status (2)

Country Link
AU (1) AU1803401A (fr)
WO (1) WO2001038895A1 (fr)

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1474706A1 (fr) * 2002-01-16 2004-11-10 Washington University Procede et systeme a resonance magnetique pour la quantification de la diffusion anisotrope
US7411393B2 (en) 2005-11-30 2008-08-12 Bracco Imaging S.P.A. Method and system for fiber tracking
WO2011139745A2 (fr) * 2010-04-27 2011-11-10 Chunlei Liu Systèmes et procédés pour une imagerie de tenseur de susceptibilité
WO2014003643A1 (fr) * 2012-06-29 2014-01-03 Cr Development Ab Quantification de la quantité relative d'eau dans le réseau microcapillaire d'un tissu
JP2015104489A (ja) * 2013-11-29 2015-06-08 ジーイー・メディカル・システムズ・グローバル・テクノロジー・カンパニー・エルエルシー 磁気共鳴装置
US9285449B2 (en) 2011-06-15 2016-03-15 Chunlei Liu Systems and methods for imaging and quantifying tissue magnetism with magnetic resonance imaging
CN107727678A (zh) * 2017-10-19 2018-02-23 北京青檬艾柯科技有限公司 一种核磁共振弛豫高低本征模态耦合方法
US10324154B2 (en) 2015-05-13 2019-06-18 General Electric Company Generalized spherical deconvolution in diffusion magnetic resonance imaging
US10573087B2 (en) 2017-08-16 2020-02-25 Synaptive Medical (Barbados) Inc. Method, system and apparatus for rendering medical image data
WO2021072484A1 (fr) * 2019-10-18 2021-04-22 Omniscient Neurotechnology Pty Limited Analyse différentielle de réseau cérébral
US11152123B1 (en) 2021-01-08 2021-10-19 Omniscient Neurotechnology Pty Limited Processing brain data using autoencoder neural networks
US11151456B1 (en) 2021-01-08 2021-10-19 Omniscient Neurotechnology Pty Limited Predicting brain data using machine learning models
US11666266B2 (en) 2021-10-05 2023-06-06 Omniscient Neurotechnology Pty Limited Source localization of EEG signals

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5566282A (en) 1993-02-15 1996-10-15 U.S. Philips Corporation Apparatus and method for the visualization of a three-dimensional scene by means of maximum intensity projection
US5864233A (en) 1997-02-22 1999-01-26 General Electric Company Method to reduce eddy current effects in diffusion-weighted echo planar imaging
US5923168A (en) 1997-06-17 1999-07-13 General Electric Company Correction of artifacts caused by Maxwell terms in slice offset echo planar imaging

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5566282A (en) 1993-02-15 1996-10-15 U.S. Philips Corporation Apparatus and method for the visualization of a three-dimensional scene by means of maximum intensity projection
US5864233A (en) 1997-02-22 1999-01-26 General Electric Company Method to reduce eddy current effects in diffusion-weighted echo planar imaging
US5923168A (en) 1997-06-17 1999-07-13 General Electric Company Correction of artifacts caused by Maxwell terms in slice offset echo planar imaging

Non-Patent Citations (9)

* Cited by examiner, † Cited by third party
Title
BASSER P J ET AL: "MICROSTRUCTURAL AND PHYSIOLOGICAL FEATURES OF TISSUES ELUCIDATED BYQUANTITATIVE-DIFFUSION-TENSOR MRI", JOURNAL OF MAGNETIC RESONANCE. SERIES B,US,ACADEMIC PRESS, ORLANDO, FL, vol. 111, no. 3, 1 June 1996 (1996-06-01), pages 209 - 219, XP000626666, ISSN: 1064-1866 *
DATABASE JICST-EPLUS Japan Science and Tech Corp (JST); KINOSADA YASUTOMI ET AL.: "MR Tractography. Visualization of Structure of Nerve Fiber System from Diffusion Weighted Images with Maximum Intensity Projection Method", XP002162558 *
JONES D.K. ET AL.: "An Optimal Strategy for Precise Determination of the Diffusion Tensor", PROCEEDINGS OF THE INTERNATIONAL SOCIETY FOR MAGNETIC RESONANCE IN MEDICINE, vol. 3, 22 May 1999 (1999-05-22), Philadelphia, USA, pages 1793, XP002162420 *
LORI N.F. ET AL.: "Tracking Neuronal Fibers in the Living Human Brain with Diffusion MRI", PROCEEDINGS OF THE INTERNATIONAL SOCIETY FOR MAGNETIC RESONANCE IN MEDICINE, vol. 1, 22 May 1999 (1999-05-22), Philadelphia, USA, pages 324, XP002162423 *
MAKRIS N ET AL: "Morphometry of in vivo human white matter association pathways with diffusion-weighted magnetic resonance imaging.", ANNALS OF NEUROLOGY, vol. 42, no. 6, December 1997 (1997-12-01), pages 951 - 962, XP000986335, ISSN: 0364-5134 *
NIPPON ACTA RADIOLOGICA, vol. 53, no. 2, 1993, pages 171 - 179 *
P J BASSER ET AL: "A Simplified Method to Measure the Diffusion Tensor from Seven MR Images", MAGNETIC RESONANCE IN MEDICINE,ACADEMIC PRESS, DULUTH, MN,US, vol. 39, 1998, pages 928 - 934, XP002145650, ISSN: 0740-3194 *
PIERPAOLI C ET AL: "Toward a quantitative assessment of diffusion anisotropy", MAGNETIC RESONANCE IN MEDICINE, DEC. 1996, WILLIAMS & WILKINS, USA, vol. 36, no. 6, pages 893 - 906, XP002162422, ISSN: 0740-3194 *
ZHOU X. ET AL.: "Reduction of Image Misregistration in Diffusion-Weighted EPI", PROCEEDINGS OF THE INTERNATIONAL SOCIETY FOR MAGNETIC RESONANCE IN MEDICINE, vol. 3, 12 April 1997 (1997-04-12), Vancouver, Canada, pages 1724, XP002162421 *

Cited By (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1474706A1 (fr) * 2002-01-16 2004-11-10 Washington University Procede et systeme a resonance magnetique pour la quantification de la diffusion anisotrope
EP1474706A4 (fr) * 2002-01-16 2005-06-15 Univ Washington Procede et systeme a resonance magnetique pour la quantification de la diffusion anisotrope
US7078897B2 (en) 2002-01-16 2006-07-18 Washington University Magnetic resonance method and system for quantification of anisotropic diffusion
US7411393B2 (en) 2005-11-30 2008-08-12 Bracco Imaging S.P.A. Method and system for fiber tracking
US9383423B2 (en) 2010-04-27 2016-07-05 Chunlei Liu Systems and methods for susceptibility tensor imaging
WO2011139745A3 (fr) * 2010-04-27 2011-12-29 Chunlei Liu Systèmes et procédés pour une imagerie de tenseur de susceptibilité
US8447089B2 (en) 2010-04-27 2013-05-21 Chunlei Liu Systems and methods for susceptibility tensor imaging
WO2011139745A2 (fr) * 2010-04-27 2011-11-10 Chunlei Liu Systèmes et procédés pour une imagerie de tenseur de susceptibilité
US9285449B2 (en) 2011-06-15 2016-03-15 Chunlei Liu Systems and methods for imaging and quantifying tissue magnetism with magnetic resonance imaging
WO2014003643A1 (fr) * 2012-06-29 2014-01-03 Cr Development Ab Quantification de la quantité relative d'eau dans le réseau microcapillaire d'un tissu
US10788558B2 (en) 2012-06-29 2020-09-29 Cr Development Ab Quantification of the relative amount of water in the tissue microcapillary network
US10031204B2 (en) 2012-06-29 2018-07-24 Cr Development Ab Quantification of the relative amount of water in the tissue microcapillary network
JP2015104489A (ja) * 2013-11-29 2015-06-08 ジーイー・メディカル・システムズ・グローバル・テクノロジー・カンパニー・エルエルシー 磁気共鳴装置
US10324154B2 (en) 2015-05-13 2019-06-18 General Electric Company Generalized spherical deconvolution in diffusion magnetic resonance imaging
US10573087B2 (en) 2017-08-16 2020-02-25 Synaptive Medical (Barbados) Inc. Method, system and apparatus for rendering medical image data
CN107727678A (zh) * 2017-10-19 2018-02-23 北京青檬艾柯科技有限公司 一种核磁共振弛豫高低本征模态耦合方法
WO2021072484A1 (fr) * 2019-10-18 2021-04-22 Omniscient Neurotechnology Pty Limited Analyse différentielle de réseau cérébral
US11145119B2 (en) 2019-10-18 2021-10-12 Omniscient Neurotechnology Pty Limited Differential brain network analysis
US11704870B2 (en) 2019-10-18 2023-07-18 Omniscient Neurotechnology Pty Limited Differential brain network analysis
US11152123B1 (en) 2021-01-08 2021-10-19 Omniscient Neurotechnology Pty Limited Processing brain data using autoencoder neural networks
US11151456B1 (en) 2021-01-08 2021-10-19 Omniscient Neurotechnology Pty Limited Predicting brain data using machine learning models
US11961004B2 (en) 2021-01-08 2024-04-16 Omniscient Neurotechnology Pty Limited Predicting brain data using machine learning models
US11666266B2 (en) 2021-10-05 2023-06-06 Omniscient Neurotechnology Pty Limited Source localization of EEG signals

Also Published As

Publication number Publication date
AU1803401A (en) 2001-06-04

Similar Documents

Publication Publication Date Title
Bastin et al. A theoretical study of the effect of experimental noise on the measurement of anisotropy in diffusion imaging
Lansdown et al. Quantitative diffusion tensor MRI-based fiber tracking of human skeletal muscle
Sinha et al. In vivo diffusion tensor imaging of the human calf muscle
Duyn The future of ultra-high field MRI and fMRI for study of the human brain
Le Bihan Looking into the functional architecture of the brain with diffusion MRI
Mattiello et al. The b matrix in diffusion tensor echo‐planar imaging
Jones et al. Isotropic resolution diffusion tensor imaging with whole brain acquisition in a clinically acceptable time
Papadakis et al. A study of rotationally invariant and symmetric indices of diffusion anisotropy
AU682146B2 (en) Image neurography and diffusion anisotropy imaging
Wu et al. Inadequacy of motion correction algorithms in functional MRI: role of susceptibility‐induced artifacts
Nakada et al. Clinical application of three-dimensional anisotropy contrast magnetic resonance axonography
Fujii et al. High-resolution T2-reversed magnetic resonance imaging on a high-magnetic field system
WO2001038895A1 (fr) Procedes et systemes permettant de generer des representations des faisceaux snc
Liu et al. Imaging neural architecture of the brain based on its multipole magnetic response
WO2018187764A1 (fr) Mri par tenseur de diffusion généralisée isotrope
Cao et al. Optimized multi‐axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole‐brain high‐isotropic‐resolution quantitative imaging
Wang et al. A new method to derive white matter conductivity from diffusion tensor MRI
Wheeler‐Kingshott et al. D: the Diffusion of Water
Andrew Nuclear magnetic resonance and the brain
Matsuzawa et al. Magnetic resonance axonography of the rat spinal cord: postmortem effects
Yuan et al. T1rho MR imaging: principle, technology, and application
Burkhardt et al. Numerical determination of the susceptibility caused geometric distortions in magnetic resonance imaging
Cercignani et al. An optimized pulse sequence for isotropically weighted diffusion imaging
Seo et al. Diffusion tensor imaging of extraocular muscle using two‐dimensional single‐shot interleaved multiple inner volume imaging diffusion‐weighted EPI at 3 tesla
Theilenberg Imaging Meningiomas

Legal Events

Date Code Title Description
AK Designated states

Kind code of ref document: A1

Designated state(s): AU CA JP

AL Designated countries for regional patents

Kind code of ref document: A1

Designated state(s): AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE TR

121 Ep: the epo has been informed by wipo that ep was designated in this application
DFPE Request for preliminary examination filed prior to expiration of 19th month from priority date (pct application filed before 20040101)
122 Ep: pct application non-entry in european phase