U.S. patent number 5,427,101 [Application Number 08/286,049] was granted by the patent office on 1995-06-27 for diminishing variance process for real-time reduction of motion artifacts in mri.
This patent grant is currently assigned to Board of Trustees of the Leland Stanford Junior University. Invention is credited to Craig H. Meyer, Dwight G. Nishimura, Todd S. Sachs.
United States Patent |
5,427,101 |
Sachs , et al. |
June 27, 1995 |
Diminishing variance process for real-time reduction of motion
artifacts in MRI
Abstract
A method whereby motion can be detected in real time during the
acquisition of MRI data. This enables the implementation of several
algorithms to reduce or eliminate this motion from an image as it
is being acquired. The method is an extension of the
acceptance/rejection method algorithm called the diminishing
variance algorithm (DVA). With this method, a complete set of
preliminary data is acquired along with information about the
relative motion position of each frame of data. After all the
preliminary data is acquired, the position information is used to
determine which lines are most corrupted by motion. Frames of data
are then reacquired, starting with the most corrupted frame. The
position information is continually updated in an iterative
process, therefore each subsequent reacquisition is always done on
the worst frame of data. The algorithm has been implemented on
several different types of sequences, and preliminary in vivo
studies indicate that motion artifacts are dramatically
reduced.
Inventors: |
Sachs; Todd S. (Beachwood,
OH), Meyer; Craig H. (Palo Alto, CA), Nishimura; Dwight
G. (Palo Alto, CA) |
Assignee: |
Board of Trustees of the Leland
Stanford Junior University (Palo Alto, CA)
|
Family
ID: |
23096840 |
Appl.
No.: |
08/286,049 |
Filed: |
August 4, 1994 |
Current U.S.
Class: |
600/410; 324/309;
600/534 |
Current CPC
Class: |
G01R
33/56509 (20130101); G01R 33/5676 (20130101); G01R
33/5608 (20130101) |
Current International
Class: |
G01R
33/54 (20060101); G01R 33/567 (20060101); G01R
33/56 (20060101); A61B 005/055 () |
Field of
Search: |
;128/653.1,653.2,716-721
;324/309 ;364/413.13,413.15 ;378/901,62 |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Sachs et al., Real-Time Motion Detection in MRI, Society of
Magnetic Resonance in Medicine Twelfth Annual Scientific Meeting,
1993, p. 202. .
Axel et al., Respiratory Effects In Two-Dimensional Fourier
Transform MR Imaging, Radiology, vol. 160, Sep. 1986, pp. 795-801.
.
Ehman et al., Influence of Physiologic Motion on the Appearance of
Tissue in MR Images, Radiology, vol. 159, Jun. 1986, pp. 777-782.
.
Wood et al., Suppression of Respiratory Motion Artifacts in
Magnetic Resonance Imaging, Medical Physics, vol. 13, No. 6,
Nov./Dec. 1986, pp. 794-805. .
Glover et al., Projection Reconstruction Techniques for Reduction
of Motion Effects in MRI, Magnetic Res. in Imaging, vol. 28, 1992,
pp. 275-289. .
Schultz et al., The Effect of Motin on Two-Dimensional Fourier
Transformation Magnetic Resonance Images, Radiology, vol. 152, Jul.
1984, pp. 117-121. .
Sachs et al., Real-Time Reduction of Motion Artifacts in Spiral MRI
by Using Navigators, FIrst Meeting of the Society of Magnetic
Resonance, 1994, p. 61. .
Ehman et al., Adaptive Technique for High-Definition MR Imaging of
Moving Structures, Radiology, vol. 173, pp. 255-263. .
Korin et al., Compensation for Effects of Linear Motion in MR
Imaging, Magnetic Resonance in Medicine, vol. 12, 1989, pp.
99-113..
|
Primary Examiner: Pfaffle; Krista M.
Attorney, Agent or Firm: Townsend and Townsend Khourie and
Crew
Government Interests
This invention was made with Government support under contract NIH
HL 39297 awarded by the National Institutes of Health. The
Government has certain rights in this invention.
Claims
What is claimed is:
1. A method for reducing motion artifacts in magnetic resonance
images comprising the steps of
a) acquiring magnetic resonance image signals for multiple image
frames,
b) acquiring relative position data for each image frame,
c) developing a histogram of positional shifts in frames based on
said relative position data, and
d) reacquiring magnetic resonance signals for selected image frames
to reduce positional variance in said histogram.
2. The method of claim 1 and further including the steps of
e) updating said histogram of positional shifts using relative
position data for reacquiring signals for said selected image
frames, and
f) reacquiring magnetic resonance signals for selected image frames
to reduce positional variance in the updated histogram.
3. The method as defined by claim 2 wherein steps e) and f) are
repeated iteratively until an acceptable range of motion in all
frames is realized.
4. The method as defined by claim 2 wherein steps e) and f) are
repeated iteratively until a scan time limit is reached.
5. The method as defined by claim 1 wherein relative position data
in step b) is provided by a navigator in each frame.
6. The method as defined by claim 1 wherein step c) includes
obtaining a cross correlation between a reference projection and a
test projection associated with each frame of data as a measure of
motion shift.
7. The method as defined by claim 1 wherein each frame is an
interleaved spiral acquisition.
8. The method as in claim 7 wherein all frames are interleaved
spiral acquisitions.
9. The method as defined by claim 1 wherein step b) determines
positional variance based on a centering algorithm.
10. The method as defined by claim 9 wherein said centering
algorithm uses a value selected from the group consisting of mean,
median, and mode.
11. The method as defined by claim 1 and further including after
step b) the step of weighting each image frame, and step c)
includes developing a histogram of weighted positional shifts.
12. The method as defined by claim 11 wherein a weighting factor is
based on image frame position in k-space with frames near the
center of k-space having greater weight.
13. The method as defined by claim 1 wherein each frame is a 2DFT
acquisition.
14. Apparatus for providing magnetic resonance imaging signals with
reduced motion artifacts comprising
a) means for acquiring magnetic resonance image signals for
multiple image frames,
b) means for acquiring relative position data for each image
frame,
c) means for developing a histogram of positional shifts in frames
based on said relative position data,
d) means for reacquiring magnetic resonance signals for selected
image frames to reduce positional variance in said histogram,
and
e) means for using said magnetic resonance signals including
reacquired magnetic resonance signals to produce an image.
Description
BACKGROUND OF THE INVENTION
This invention relates generally to magnetic resonance imaging
(MRI), and more particularly the invention relates to real-time
reduction of motion artifacts in MRI.
Magnetic resonance imaging apparatus is widely used in medical
diagnosis applications. In very general terms, nuclear magnetic
moments are excited at specific spin precession frequencies which
are proportional to the local magnetic field. The radio-frequency
signals resulting from the precession of the spins are received
using pick-up coils. By manipulating the magnetic fields, an array
signal is provided representing different regions of the volume.
These can be combined to produce a volumetric image of the nuclear
spin density of the body.
Referring to the drawing, FIG. 1A is a perspective view partially
in section illustrating coil apparatus in a MR imaging system, and
FIGS. 1B-1D illustrate field gradients which can be produced in the
apparatus of FIG. 1A. This apparatus is discussed by Hinshaw and
Lent, An Introduction to NMR Imaging: From the Block Equation to
the Imaging Equation, PROCEEDINGS OF THE IEEE, Vol 71, No. 3, March
1983, pp. 338-350. Briefly, the uniform static field B.sub.0 is
generated by the magnet comprising the coil pair 10. A gradient
field G.sub.x is generated by a complex gradient coil set which can
be wound on the cylinder 12. An RF field B.sub.1 is generated by a
saddle coil 14. A patient undergoing imaging would be positioned
along the Z axis within the saddle coil 14.
In FIG. 1B an X gradient field is shown which is parallel to the
static field B.sub.0 and varies linearly with distance along the X
axis but does not vary with distance along the Y or Z axes. FIGS.
1C and 1D are similar representations of the Y gradient and Z
gradient fields, respectively.
FIG. 2 is a functional block diagram of the imaging apparatus as
disclosed in NMR-A Perspective on Imaging, General Electric
Company, 1982. A computer 20 is programmed to control the operation
of the NMR apparatus and process FID signals detected therefrom.
The gradient field is energized by a gradient amplifier 22, and the
RF coils for impressing an RF magnetic moment at the Larmor
frequency is controlled by the transmitter 24 and the RF coils 26.
After the selected nuclei have been flipped, the RF coils 26 are
employed to detect the FID signal which is passed to the receiver
28 and thence through digitizer 30 for processing by computer
20.
The strong static magnetic field is employed to line up atoms whose
nuclei have an odd number of protons and/or neutrons, that is, have
spin angular momentum and a magnetic dipole moment. A second RF
magnetic field, applied as a single pulse transverse to the first,
is then used to pump energy into these nuclei, flipping them over,
for example to 90.degree. or 180.degree.. After excitation, the
nuclei gradually return to alignment with the static field and give
up the energy in the form of weak but detectable free induction
decay (FID). These DFID signals are used by a computer to produce
images.
Motion continues to be a major stumbling block in imaging thoracic
and abdominal structures. Be it bulk translations and rotations due
to respiratory motion, partial field of view translations and
rotations due to cardiac motion, or unpredictable spurious patient
motion, the effect on the resultant image often completely obscures
the fine detail that was originally sought.
The present invention is directed to detecting the presence of
motion in real-time during a scan; that is, each frame of data is
put through a test for motion directly after it is acquired, and
before the next frame of data is acquired. With this scheme, the
implementation of several different real-time processing algorithms
is possible. An acceptance/rejection algorithm is one such method
that has previously been discussed, and the invention presents a
new algorithm which makes more efficient use of time, data, and a
priori information.
SUMMARY OF THE INVENTION
Briefly, the invention utilizes a diminishing variance algorithm
(DVA) to operate on a preliminary set of data frames. The first
pass through the scan is identical to what it would have been
without any real-time detection, with the exception that
information about each frame's relative position is computed and
kept track of during the scan. After the initial set of data has
been acquired, it is possible in real-time to reacquire certain
data lines that are deemed positionally worse than the others
through a motion test.
The process in accordance with the invention is accomplished
without the use of breath-holds and is designed to be dependent as
little as possible on patient cooperation. Complicated instructions
and/or physical challenges presented to an unwilling or unable
patient are minimized as much as possible. It is also completely
general, and can be implemented on any type of sequence. It has
been implemented on several different types of sequences, and has
yielded very promising results.
The invention and objects and features thereof will be more readily
apparent from the following description and appended claims when
taken with the drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
FIGS. 1A-1D illustrate arrangement of conventional MRI apparatus
and magnetic fields generated therein.
FIG. 2 is a functional block diagram of MRI imaging apparatus.
FIG. 3 is a flow diagram illustrating one embodiment of the
invention.
FIGS. 4A, 4B are pictures of T1-weighted axial abdominal scans with
no real-time processing and with processing in accordance with the
invention.
DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS
Referring now to FIG. 3, a flow diagram of the diminishing variance
process is illustrated. Initially, a complete set of data frames,
as required to construct an image, is acquired with each frame
having a mechanism for indicating a relative position of the frame.
A histogram of positional shifts in the frames is then developed,
and the positionally worst data frames due to motion are then
reacquired. The histogram is updated, and the positionally worst
data frames in the updated histogram are reacquired. This process
is continued until an image which is motion free is realized. As
used herein, a data frame is a portion of the data, typically a
single line in k-space for 2DFT imaging or a simple spiral
interleaf for interleaved spiral imagery.
The invention has been implemented using navigator echoes, as
disclosed in Ehman et al., Adaptive Technique for High-definition
MR Imaging of Moving Structures, RADIOLOGY 173:255-263 (1989), and
Korin et al., Compensation for Effects of Linear Motion in MR
Imaging, MAGNETIC RESONANCE IN MEDICINE 12:99-113 (1989). A
navigator is acquired at the end of each data acquisition along
each of the axes for which a motion test is desired. The scheme is
based on the assumption that the motion is slow enough with respect
to one acquisition that it will be reflected in both the data and
the navigator. Alternatively, other sources of reference signals
can be used, including motion sensors on the body being imaged.
Since the Fourier transform of a navigator echo is the resultant
projection of the imaging field of view (FOV) through the axis of
navigator, it has all the information necessary to resolve motion
in that direction, as discussed in Ehman et al. and Korin et al.,
supra. Currently, a cross correlation between a reference
projection and a test projection associated with a frame of data
resolves the motion. The resultant index of the peak of this cross
correlation is used as a shift in determining if the current frame
of data is corrupted by motion.
During the first pass through the data frames, the sequence runs as
it would have with no real time processing. However, the shifts for
each frame are still computed and kept track of in a histogram. At
the end of this first pass, a complete data set has been acquired
along with an accurate measure of the relative motion of each frame
of data. It is now possible to reacquire data frames employing a
reacquisition strategy that makes use of the motion information.
Analysis of the histogram should provide a region of points which
are relatively stationary with respect to other points. These
points can be found using the mean, median, mode, or some more
detailed centering algorithm. However, keeping in mind the
application of tracking motion in the chest and abdomen, the mode
is currently used. This is because the respiratory cycle has a
somewhat one-sided distribution over time, with the highest region
of time located at the end point of expiration. The mode of the
shifts in the histogram then provides a reasonable estimate of this
point in the respiratory cycle.
After this mode is found, between the acquisition of the last frame
of data in the preliminary pass and the first reacquisition, the
distance of each data frame from the mode is computed. The frame of
data that is furthest from the mode in absolute pixel distance is
the next one to be reacquired. After this has been reacquired, the
histogram is updated, and the process is repeated. In this way,
each subsequent reacquisition always occurs on the worst frame of
data. Since more time during the respiratory cycle is spent in the
relaxed position of expiration than in any other part, the
reacquisitions of frames will have a greater chance of falling
closer to the mode than they previously were, especially since the
reacquisitions will always be occurring on the worst current data
frame. In this way, the variance among the sample points on the
histogram is constantly decreasing, leading to the diminishing
variance.
It is important to remember that by the end of the first pass
through the scan, a complete image has been obtained. Each
subsequent acquisition serves only to make the resultant image more
motion free. The scan can be stopped at any time after the first
pass, and an image can be reconstructed. The longer the scan runs
during the reacquisition stage, the better the image will be in
terms of motion artifacts. However, a point is eventually reached
where the image is as motion free as the resolution of the cross
correlation can possibly detect. At this point, extra scan time
will not change the image appreciably. This provides the first
stopping criterion for the reacquisition of data. If, after a
reacquisition, all of the data frame shifts fall into an acceptable
range of motion as determined by the application and the resolution
of the navigators, then reacquisition is complete,and the data set
is as motion free as the navigators can detect. Alternatively, if
during reacquisition the "worst" data frame does not improve after
several reacquisitions, then the scan can stop since subsequent
reacquisitions will not improve the image significantly. Another
stopping criterion is the total time limit of the scan. This again
depends on user input and the particular application, but it is
clear that some maximum total scan time should be set so that the
scan cannot possibly run forever. When using the
acceptance/rejection algorithm, successful scans usually fell in
the range of 2.5 to 4 times the scan time of a normal scan,
indicating that the stationary expired position represents about a
quarter to a third of the respiratory cycle. For this reason, a
reasonable maximum allowable scan time for the diminishing variance
algorithm is three to four times the normal scan time of the
sequence. If this maximum is reached before the first stopping
criterion, the scan will stop at this point. The resultant image is
as motion free as could be acquired given the time constraint.
Preliminary experiments have shown that with a maximum allowable
time of three times the normal scan time, the images demonstrate
dramatic reduction in motion artifacts even when they reach the
maximum scan time before stopping via the first criterion. Images
demonstrating this will be presented below.
FIG. 4A shows the results of using an axial T1-weighted
gradient-echo spiral sequence on the abdomen without any real-time
processing. The scan was done on a normal volunteer who was given
no special instructions. No breath-holds or countdowns were given.
Notice the motion artifacts present in the image. FIG. 4B shows the
same abdominal study scanned with the same sequence. However, this
time, the real-time diminishing variance algorithm was employed.
Again, no special instructions were given to the volunteer, who was
breathing normally. The scan reached the time limit of three times
a normal scan, i.e., three times the time it took to complete FIG.
4A. Notice the dramatic reduction of motion artifacts, even though
the scan did not reach the motion free state in the allotted time.
The stripe across the left side of the two images shows where the
navigator was applied, which detected motion in the
inferior/superior direction, corresponding to the motion of the
diaphragm. For consistency, the navigator was played out in image
4A as well, so that the two sequences would be identical. Both
figures are 40 interleave, gradient-echo spiral acquisitions with
TR=400 ms, FOV=32 cm, 5 mm slice thickness, and a 15 cm navigator
applied along k.sub.z. Scan time for FIG. 4A is 16 seconds.
Scanning in FIG. 4B reached time limit of 48 seconds before
stopping.
The images demonstrate the feasibility and potential clinical
effectiveness of using a real-time motion detection scheme for
images of moving structures. The extension of the invention from
the acceptance/rejection algorithm to diminishing variance allows
for a more efficient reacquisition strategy that has a definitive
maximum scan time. Preliminary results on volunteers have shown
dramatic reduction of breathing artifacts in abdominal and thoracic
imaging.
During the reacquisition of magnetic resonance signals, lines can
be selected not only based on relative motion but also, for
example, on relative position in k-space. Data frames near the
center of k-space contain considerably more energy than data frames
far away from the center. While all of the data frames contribute
to the overall image quality, the frames with more energy (near the
center of k-space) should be given preference in reacquisition over
frames with less energy. Thus a weighting factor is preferably
applied to each scan data frame to indicate the relative importance
of the data frame to image quality. Accordingly, both positional
information (worst frame in a motion sense) as well as the relative
importance of the frame (proximity to the center of k-space) must
be considered. Spiral scans cover all of k-space starting at the
origin, and all spiral scans have equal importance. However, in
echo planar imaging, or 2DFT imaging, the proximity of scan frames
to the center of k-space is important.
Basically, position information alone need not be the only
determinant of which data frames to reacquire. Other criterion can
be used in assigning weighting factors. The criterion can be
proximity to the center portion of k-space, as noted above, but
this need not be the sole criterion.
The invention possesses some inherent advantages over
post-processing correction. It is a general technique, and should
work equally well with any type of sequence. The method is robust
with respect to different types of motion and makes no mathematical
model of the underlying motion kinematics in a scan. Unlike
correction through post-processing, the process functions
identically on all types of detectable motion. As long as a motion
is detectable, it is treated in the same way as any other type of
motion. The method can be combined with a sequence that uses
cardiac synchronization as with an electrocardiograph signal to
cope with both cardiac and respiratory-dependent motion.
In addition, the SNR of the scan is not limited to what can be
acquired in a breath-held. In fact, the scan is in no way limited
by the patent's ability to hold his or her breath. This is
especially advantageous for uncooperative patients who have poor
breath holding ability. The real-time algorithm should also help
with the problem of slice registration. Since the scan is not
restricted to a breath-hold, data from all slices, or even a 3D
data set, can be acquired form one reference position. This rids
the resultant data set of slice registration problems in much the
same way as it rids it of motion artifacts. In fact, slice
registration problems arise from through-plane motion, and are
simply motion artifacts in that dimension. This has been a problem
in scans using successive breath-holds with no feedback.
While the invention has been described with references to specific
embodiments, the description is illustrative of the invention and
is not to be construed as limiting the invention. Various
applications and modifications might occur to those skilled in the
art without departing form the true spirit and scope of the
invention as defined by the appended claims.
* * * * *