Source: http://www.google.com/patents/US5560360?dq=6,928,433
Timestamp: 2014-08-22 08:14:42
Document Index: 458867414

Matched Legal Cases: ['Application No. 9301268', 'Application No. 9216383', 'Application No. 9210810', 'Application No. 9209648', 'Application No. 9207013', 'Application No. 9205541', 'Application No. 9205058']

Patent US5560360 - Image neurography and diffusion anisotropy imaging - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign in<nobr>Advanced Patent Search</nobr>PatentsA neurography system (10) is disclosed for generating diagnostically useful images of neural tissue (i.e., neurograms) employing a modified magnetic resonance imaging system (14). In one embodiment, the neurography system selectively images neural tissue by employing one or more gradients to discriminate...http://www.google.com/patents/US5560360?utm_source=gb-gplus-sharePatent US5560360 - Image neurography and diffusion anisotropy imagingAdvanced Patent SearchPublication numberUS5560360 APublication typeGrantApplication numberUS 08/028,795Publication dateOct 1, 1996Filing dateMar 8, 1993Priority dateMar 9, 1992Fee statusPaidAlso published asCA2131705A1, CA2131705C, DE69325508D1, DE69325508T2, EP0630481A1, EP0630481B1, WO1993018415A1Publication number028795, 08028795, US 5560360 A, US 5560360A, US-A-5560360, US5560360 A, US5560360AInventorsAaron G. Filler, Jay S. Tsurda, Todd L. Richards, Franklyn A. HoweOriginal AssigneeUniversity Of WashingtonExport CitationBiBTeX, EndNote, RefManPatent Citations (10), Referenced by (88), Classifications (7), Legal Events (10) External Links: USPTO, USPTO Assignment, EspacenetImage neurography and diffusion anisotropy imagingUS 5560360 AAbstract A neurography system (10) is disclosed for generating diagnostically useful images of neural tissue (i.e., neurograms) employing a modified magnetic resonance imaging system (14). In one embodiment, the neurography system selectively images neural tissue by employing one or more gradients to discriminate diffusion anisotropy in the tissue and further enhances the image by suppressing the contribution of fat to the image. The neurography system is part of a broader medical system (12), which may include an auxiliary data collection system (22), diagnostic system (24), therapeutic system (26), surgical system (28), and training system (30). These various systems are all constructed to take advantage of the information provided by the neurography system regarding neural networks, which information was heretofore unavailable.
This application is based upon an earlier filed U.K Patent Application No. 9301268.0, filed Jan. 22, 1993, which, in turn, is a continuation-in-part of U.K. Patent Application No. 9216383.1, filed Jul. 31, 1992, which, in turn, is a continuation-in-part of U.K. Patent Application No. 9210810.9, filed May 21, 1992, which, in turn, is a continuation-in-part of U.K. Patent Application No. 9209648.6, filed May 5, 1992, which, in turn, is a continuation-in-part of U.K. Patent Application No. 9207013.5, filed Mar. 30, 1992, which, in turn, is a continuation-in-part of U.K. Patent Application No. 9205541.7, filed Mar. 13, 1992, which, in turn, is a continuation-in-part of parent U.K. Patent Application No. 9205058.2, filed Mar. 9, 1992, the benefit of the filing dates of which is hereby claimed pursuant to 35 U.S.C. �119.
FIELD OF THE INVENTION The present invention relates generally to the field of imaging and, more particularly, to the imaging of nerve tissue and other diffusionally anisotropic structures.
Two factors influencing the rate of decay are known as the spin-lattice relaxation coefficient T1 and the spin-spin relaxation coefficient T2. The spin-spin relaxation coefficient T2 represents the influence that interactions between spins have on decay, while the spin-lattice relaxation coefficient T1 represents the influence that interactions between spins and fixed components have on decay. Thus, the rate at which the return coil output decays is dependent upon, and indicative of, the composition of the specimen.
Unfortunately, because peripheral nerve does not exhibit the flow-distinctiveness of blood vessels, MRI angiography systems and pulse sequences can not be used to generate suitable images of peripheral nerve. Further, conventional MRI systems and sequences used for general imaging of tissue and bone do not provide acceptable results. Given the poor signal-to-noise (S/N) ratio of the return signals (e.g., on the order of 1� to 1.5�) and the small size of the nerve, the conspicuity of imaged nerves relative to other tissue is collectively rendered so poor as to be diagnostically useless.
By way of elaboration, given its fibrous structure, muscle also exhibits diffusional anisotropy, as recognized in Moseley et al., Acute Effects of Exercise on Echo-Planar T2 and Diffusion-Weighted MRI of Skeletal Muscle in Volunteers, BOOK OF ABSTRACTS, SOCIETY OF MAGNETIC RESONANCE IN MEDICINE 108 (1991). As a result, the simple anisotropic analysis of Douek et al. is unable to distinguish peripheral nerve and muscle. While fat is isotropic and, therefore, distinguishable from nerve, it also impairs the imaging of peripheral nerves. Specifically, the relative signal strength of fat returns to neural returns is so high as to render peripheral nerves unidentifiable in images produced.
Circuit 74 also includes an rf pulse generator 84, which produces rf signal pulses used in the establishment of the excitation field. In the preferred arrangement, the pulse generator produces an rf output suitable for use in proton MRI, although frequencies specific to other MRI susceptible nuclei, such as, 19 fluorine, 13 carbon, 31 phosphorus, deuterium, or 23 sodium, may be used. The output of generator 84 is amplified by a high-power rf amplifier 86 before being selectively applied to the excitation coil 62 by a duplexer 88. The duplexer 88 is also controlled to selectively steer the low level MR returns received by the excitation coil 62 to a preamplifier 90.
Although in the preferred embodiment fat suppression is combined with other techniques such as diffusional weighting and long T2 processing, fat suppression by itself enhances conventional MRI processing sufficiently to generate clinically useful neurograms. Similarly, as will be described in greater detail below, other techniques employed by system 10 can be used without fat suppression to generate suitable neurograms.
S=A0 [exp (-TE/T2)][exp (-bD)]                   (1)
where A0 is the absolute signal intensity for a particular pixel and b is the gradient factor, determined in accordance with the expression:
b=&#947;2 (Gi 2)(&#948;2)(&#916;-&#948;/3)(2)
where γ is the gyromagnetic ratio, Gi is the polarizing field strength, δ is the length of a diffusional weighting gradient pulse, and Δ is the interval between diffusional weighting gradient pulses. As will be appreciated, in the first iteration before diffusional weighting is employed, the final term of equation (1) is, thus, unity.
Finally, the value of the apparent T2 relaxation time (or the apparent diffusion coefficient D, if diffusional weighting is employed) is computed for a particular ROI at block 128. These computations provide quantitative assessments of the various ROI in the image that are useful in subsequent image processing by other components of the medical system 12.
There, the computer determines whether the operator initially indicated that the axis of diffusional anisotropy is known. If the axis is known, a perpendicular diffusional gradient is employed, as indicated at block 134. Then, as indicated at block 136, a diffusion-weighted spin-echo sequence is performed (modified by the inclusion of the diffusional gradient in the manner described in greater detail below) and image generated, pursuant to blocks 102-122, before quantification of the image data occurs at blocks 124-128 to compute D or T2. If the operator indicated at initialization that orthogonal diffusion gradients are required for the particular imaging problem at hand, this process is then repeated at blocks 138 and 140 for a parallel diffusional gradient.
As noted briefly above, for each of the different diffusional gradients employed, the spin-echo sequence is repeated, followed by the generation of image data and the processing of that data to, for example, quantify the relaxation time T2 or diffusion coefficient D. In the preferred arrangement, the use of diffusion gradients influences a number of aspects of the spin-echo sequence.
Although image subtraction is employed in the preferred arrangement, it is not necessary. For example, in some applications of known anisotropy, subtraction is unnecessary and can be foregone in favor of a threshold analysis. Also, the subtraction process can be further supplemented, if desired. For example, the output of the subtraction process can be divided by the signal information from a fat suppressed, T2 -weighted spin echo sequence (e.g. using the aforementioned CHESS technique).
Changes in neural direction can be monitored by moving the patient relative to a fixed set of gradient coils or employing movable diffusional gradient coils mounted, on a track with a non-magnetic drive system, within the bore of the imager to adjustably control the orientation of the diffusional gradients applied to the region of interest. By monitoring changes in the ratio of Dpl /Dpr obtained for a given pixel using alternative gradient alignments, or for sequential pixels using the same gradient alignments, changes in neural direction can be estimated and suitable gradient directions selected. Alternatively, gradient coils oriented in three planes can be simultaneously activated in various combinations to achieve the effect of an infinite variety of differently oriented gradients.
In that regard, image information is collected from, for example, four "multi-slice" sets using a zero diffusion gradient B0 and diffusion gradients Bx, By, Bz in the x-, y-, and z-orthogonal directions, respectively. For each pixel in the image to be produced, information concerning the corresponding pixels in the four diffusion gradients images is combined to produce a diffusion vector, representative of water molecule movement along the nerve fiber in either direction. This vector has a magnitude representative of the image intensity of the pixel and a direction representative of an "effective" diffusion gradient associated with the pixel.
More particularly, the image intensity Sn of a given pixel in the new image is calculated using the following vector equation:
Sn =vector length=[(Sx 2 +Sy 2 +Sz 2)So 2 ]1/2                    (3)
where Sx, Sy, and Sz are the image intensities of the corresponding pixels in the images produced by the Bx, By, and Bz gradients. S0 is the image intensity of the corresponding pixel in the image produced by the B0 gradient and is included in equation (3) to normalize the resultant image intensity Sn. The direction of the effective gradient associated with this pixel image includes components θxy, θxz, and θyz, computed in the following manner:
&#952;xy =diffusion vector angle between Bx and By =arc tan (Sy /Sx)                                        (4)
&#952;xz =diffusion vector angle between Bx and Bz =arc tan (Sx /Sz)                                        (5)
&#952;yz =diffusion vector angle between By and Bz =arc tan (Sy /Sz)                                        (6)
The parameters computed in equations (3), (4), (5), and (6) can be used to generate images in a variety of different ways. For example, the intensities of the pixels can be displayed as a "vector length" image. An illustration of a vector length CNS image, in which the intensity of the image is proportional to the magnitude of Sn is shown in FIG. 16.
The image of FIG. 16 is a brain scan of a monkey (macaca fascicularis) weighing 2-2.5 kg, performed using diffusion imaging (spin-echo) on a General Electric CSI II imager/spectrometer (2 Tesla, equipped with actively shielded gradients). The acquisition parameters were: TR=1000 ms, TE=80 ms, diffusion gradients=5 Gauss/cm, diffusion gradient duration=20 ms, diffusion gradient separation=40 ms. Four slices of thickness 4 mm were imaged. T2 -weighted images were used to reproducibly select the diffusion images.
By way of illustration, for a gradient strength of 7 G/cm and an echo time of 50 ms, an nerve image signal intensity (Sn) of 17 and a muscle image signal intensity (Sm) of 7 were calculated, based upon the difference between signal intensities with pulsed gradients oriented perpendicular and parallel to the nerve. A nerve-to-muscle contrast parameter R of 2.43 was then computed as the ratio Sn /Sm. Similarly, a comparison of the apparent diffusion coefficients for diffusional gradients perpendicular (Dpr) and parallel (Dpl) to nerve and muscle are as follows:
______________________________________Apparent Diffusion Coefficients (10-5 cm2 /sec)         Muscle               Nerve______________________________________Dpr        1.17    0.65Dpl        2.18    2.00Dpl /Dpr           1.9     3.1______________________________________
Although not entirely understood, there are several potential explanations for the synergistic relationship between fat suppression and diffusional weighting. First, it appears that fat suppression may increase the apparent diffusional anisotropy of nerve, enhancing the utility of diffusional weighting gradients in the detection of neural tissue. By way of illustration, an indicated in the following test data, obtained with the signal from fat and "short T2 " water removed, the intensity of the remaining image signal was due largely to anisotropically diffusing water.
______________________________________    CHESS Applied                CHESS Not Applied    Gradient Direction                Gradient DirectionNerve Imaged      pr     pl     Ratio pr    pl    Ratio______________________________________Ulnar Nerve      29     &lt;8     &gt;3.6  62    49    1.3Median Nerve      30     &lt;8     &gt;3.8  46    22    2.1Muscle     14      8      1.8  18    12    1.5______________________________________
vii. Long TE/TR/T2 Processing
As an alternative to the use of diffusional gradients described above, in some regions of interest, it is possible to achieve adequate enhanced isolation of the nerve image by use of a spin echo fat suppression technique with a relatively long TE (echo time) or TR (repetition time) to achieve a T2 -weighted image. In that regard, after fat suppression, the dominant component remaining in the echo F is returned from muscle. Because the T2 of peripheral nerve has been measured by the inventors to be roughly twice as long as the T2 of muscles, the use of a relatively long TE or TR in the spin echo sequence allows the muscular return to be removed.
The basic operation of a neurography system 14 employing this feature remains the same as that shown in FIGS. 9 and 10 except that the initialized value for TE is extended. In that regard, the operator may be called upon to initially consider whether the desired imaging is likely (e.g., neural imaging in a patient's limbs) or unlikely (e.g., CNS imaging) to be disrupted by the presence of muscle. If muscular interference is likely, a relatively long TE of between 50 and 100 milliseconds or even longer is initialized at block 100. The particular TE or TR selected depends upon the degree of T2 weighting desired. Alternatively, the system 14 may be programmed to compare the imaging data separately collected using long TE processing and diffusional weighting to assess which provides the best results.
The use of extended TE processing had previously been considered unfeasible. In that regard, as described in Moseley et al., Anisotropy in Diffusion-Weighted MRI, 19 MAGNETIC RESONANCE IN MEDICINE 321, 325 (1991), nerve was believed to exhibit a relatively short T2 time. Surprisingly, however, measurements have been conducted indicating that the T2 of muscle is approximately 27 milliseconds, while the T2 of peripheral nerve is approximately 55 milliseconds, providing a factor of two difference between the two types of tissue.
Finally, carefully adjusted water suppression techniques can be used to limit the contribution of the blood vessels and cerebro-spinal fluid (CSF) to the neural image generated by system 14. One such technique is fluid-attenuated, inversion recovery (FLAIR), described in, for example, Bydde et al., Comparison of FLAIR Pulse Sequences with Heavily T2 Weighted SE Sequences in MR Imaging of the Brain, 185 RADIOLOGY SUPP. 151 (1992).
Two axial series of images were produced using a two dimensional Fourier transformation. The first series consisted of 24, five mm thick sections, a 512�512 matrix, one mm skip, and one nex (number of excitations). The second series consisted of 41, three mm thick axial sections, a 256�256 matrix, zero mm skip, and two nex. The field of view was 18 cm and acquisition time was 10.6 minutes for both series.
For example, a magnetization transfer pulse sequence can be employed after the fat suppression sequence to enhance neural imaging. Magnetization transfer involves the excitation of chemically shifted protons with an "off resonance" pulse. These protons in a short T2 isotropically diffusing water compartment then exchange into a long T2 anisotropically diffusing compartment. In doing so, they carry the high intensity magnetization signal with them, thus inducing a transfer of magnetization to surrounding neural tissue to increase its conspicuity in the image. Nerve may exhibit efficient exchange between the off-resonance, relatively stationary protons in the myelin sheath and the resonant, mobile protons of axoplasmic water. On the other hand, muscle does not exhibit exchange with a large off-resonant proton pool to a comparable degree. The magnetization transfer pulse sequence is designed to exploit this differential sensitivity between nerve and muscle by using stimulation methods similar to fat suppression to synergistically improve the neurographic selectivity of the image in two ways simultaneously.
Other alternative pulse sequences can also be used. For example, a version of steady state free precession (SSFP), as described in Patz et al., The Application of Steady-State Free Precession to the Study of Very Slow Fluid Flow, 3 MAG. RES. MED. 140-145 (1986), can be used. The SSFP is, however, modified to be included in an imaging protocol to achieve fat suppression. Similarly, a magnetization prepared rapid gradient echo (MP-RAGE) sequence, as described in Mugler et al., Three Dimensional Magnetization Prepared Rapid Gradient-Echo Imaging (3D MP RAGE), 15 MAG. RES. MED. 152-157 (1990) can be used if modified to improve T2 contrast. In addition, neural selectivity can be achieved by employing proton fast exchange rates or T1 relaxation rates.
The diagnostic system 24 is selected to process the image neurograms and other information (such as D and T2) provided by neurography system 10 to provide an attending physician with, for example, diagnoses of neural anomalies. Alternatively, system 24 may assist the physician in making a diagnosis, or assessing the need for, or likely success of, surgery. In one embodiment, system 24 may be employed simply to confirm or question the physician's diagnoses.
Another approach that may be employed by system 24, is based upon the apparent increase in T2 exhibited by injured nerve. More particularly, an initial "long T2 " analysis or diffusion weighted image can be performed to image all neural structures. Then T2 can be extended to roughly 100 milliseconds to image only those nerves that are injured.
In addition to analyzing the output of neurography system 10, the diagnostic system 24 may also provide feedback to system 10 to control the pulse sequences used and the type of information produced. For example, where sites of nerve compression, section, laceration, or fibrosis are imaged, the alteration in endoneurial fluid flow and in axoplasmic flow are readily detected by monitoring the increase in signal intensity when T2 -based, or other, neurographic sequences are used.
Yet another important application of surgical system 28 is in the use of CNS neurograms to guide stereotactic surgery in the brain. Currently, tissue structures visible by virtue of their T1 or T2 MRI are used to guide stereotactic surgery. In contrast, CNS neurograms provide information concerning the connections or relation of specific tracts of interest, which may travel in or among other tracts from which they cannot be differentiated by means of conventional tissue-based images.
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