Surgery – Diagnostic testing – Detecting nuclear – electromagnetic – or ultrasonic radiation
Reexamination Certificate
2001-03-29
2002-11-05
Vo, Hieu T. (Department: 3737)
Surgery
Diagnostic testing
Detecting nuclear, electromagnetic, or ultrasonic radiation
C324S309000
Reexamination Certificate
active
06477399
ABSTRACT:
BACKGROUND OF THE INVENTION
The field of the invention is nuclear magnetic resonance imaging methods and systems. More particularly, the invention relates to the production of brain function images.
Any nucleus which possesses a magnetic moment attempts to align itself with the direction of the magnetic field in which it is located. In doing so, however, the nucleus precesses around this direction at a characteristic angular frequency (Larmor frequency) which is dependent on the strength of the magnetic field and on the properties of the specific nuclear species (the magnetogyric constant gamma &ggr; of the nucleus). Nuclei which exhibit this phenomena are referred to herein as “spins”.
When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B
0
), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. A net magnetic moment M
z
is produced in the direction of the polarizing field, but the randomly oriented magnetic components in the perpendicular, or transverse, plane (x-y plane) cancel one another. If, however, the substance, or tissue, is subjected to a magnetic field (excitation field B
1
) which is in the x-y plane and which is near the Larmor frequency, the net aligned moment, M
z
, may be rotated, or “tipped” into the x-y plane to produce a net transverse magnetic moment M
t
, which is rotating, or spinning, in the x-y plane at the Larmor frequency. The practical value of this phenomenon resides in the signal which is emitted by the excited spins after the excitation signal B
1
is terminated. There are a wide variety of measurement sequences in which this nuclear magnetic resonance (“NMR”) phenomena is exploited.
When utilizing NMR to produce images, a technique is employed to obtain NMR signals from specific locations in the subject. Typically, the region which is to be imaged (region of interest) is scanned by a sequence of NMR measurement cycles which vary according to the particular localization method being used. The resulting set of received NMR signals are digitized and processed to reconstruct the image using one of many well known reconstruction techniques. To perform such a scan, it is, of course, necessary to elicit NMR signals from specific locations in the subject. This is accomplished by employing magnetic fields (G
x
, G
y
, and G
z
) which have the same direction as the polarizing field B
0
, but which have a gradient along the respective x, y and z axes. By controlling the strength of these gradients during each NMR cycle, the spatial distribution of spin excitation can be controlled and the location of the resulting NMR signals can be identified.
The imaging of brain functions with magnetic resonance imaging systems has been done using fast pulse sequences. As described by J. Frahm et al in “Dynamic MR Imaging of Human Brain Oxygenation During Rest and Photic Stimulation”,
JMRI
1992:2:501-505; K. Kwong et al in “Dynamic Magnetic Resonance Imaging of Human Brain Activity During Primary Sensory Stimulation”
Proc. Natl. Acad. Sci USA
Vol. 89, pp 5675-5679, June 1992 Neurobiology; and S. Ogawa et al, “Intrinsic Signal Changes Accompanying Sensory Stimulation: Functional Brain Mapping Using MRI”,
Proc. Natl Acad. Sci USA
Vol. 89, pp. 5951-5955, June 1992 Neurobiology, these prior methods use a difference technique in which a series of image data sets are acquired with an EPI pulse sequence while a particular function is being performed by the patient, and a baseline image data set is acquired with no patient activity. The baseline data set is subtracted from the series of data sets to produce difference images that reveal those parts of the brain that were active during the performance of the function. These difference images may be displayed in sequence to provide a cine display of the activity-induced brain functions. In this case the fMRI parameter which distinguishes active and inactive regions of the brain is signal amplitude difference.
The difference in NMR signal level produced By regions of the brain that are active and those that are inactive is very small. The difference is believed to result from the increase in oxygen supply to active portions of the brain which decreases the susceptibility differential between vessels and surrounding tissues. This allows an increase in the phase coherence of spins and a resulting increase in NMR signal level. However, this difference in signal level is only 2 to 4 percent (at 1.5 Tesla) and is masked by system noise, and artifacts caused by patient motion, brain pulsatility, blood flow and CSF flow.
An improved method for determining which regions of the brain are active is described in U.S. Pat. No. 5,603,322. Rather than relying on signal amplitude differences as a measure of activity, the disclosed method correlates the changes in the signal level over the duration of the study with the changes in the function being performed, or stimulation applied to the subject. The signal pattern of regions that are active in response to the function or stimulation correlates highly with the function or stimulation pattern and these regions are designated “active”. In this case the fMRI parameter which distinguishes active and inactive regions of the brain is a correlation number.
As one uses fMRI to answer more complex questions (for example, the difference in activation pattern between two or more tasks), the relative difference in signal intensity between the conditions becomes even less than 2 to 5 percent. In addition, it has been discovered that the fMRI signal from unactivated regions in the brain can vary by as much as 1 percent. Before any definitive conclusions can be made about the fMRI results in a study, therefore, the signal's reliability and variability both within a subject and between subjects must be determined.
To understand the statistical reliability of an estimate such as the correlation coefficient, suppose that the correlation coefficient value of 0.65 were obtained between a pixel time course (70 points) from the sensorimotor cortex and an idealized reference waveform representing the “on/off” cycle of bilateral finger tapping. If there were a 95% probability that the correlation coefficients would lie between 0.64 and 0.66, then the correlation coefficient of 0.65 could be considered to be very reliable. However, if the correlation probability distribution were evenly spread between −1 and 1, then the obtained correlation coefficient would not be reliable.
Hence, some measure is needed to assess the statistical accuracy and reliability of the correlation coefficient or of any other statistical parameter of interest used in fMRI.
Traditionally, the reliability of fMRI data has been obtained by using test-retest analysis. As its name suggests, in test-retest analysis, the same task is repeated several times using identical imaging parameters. The data obtained are then processed, and the reliability of the data sets is measured using a number of different techniques. Test-retest analysis assumes that the task activation paradigm can be repeated a number of times under identical conditions, without any learning or habituation by the subject to alter neuron firing. In each of the test-retest analysis, the experiment must be repeated several times (three or more) to obtain the reliability criteria.
Although this method might be effective in analyzing simple motor or visual tasks, for more complex tasks, this assumption will not be valid. Even for a simple finger-tapping experiment, not only must the imaging parameters be identical for each of the scans, but also the stimulus-related parameters, including the finger-tapping rate and the on/off cycle timing, must be the same. Any deviation from the specified finger-tapping rate or the on/off cycle in any of the scans would result in erroneous conclusions. With the increase in time of scanning, even motivated subjects are likely to move their heads by at least a few m
Biswal Bharat B.
Taylor Paul A.
Ulmer John L.
MCW Research Foundation, Inc.
Quarles & Brady LLP
Vo Hieu T.
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