U.S. patent application number 10/143671 was filed with the patent office on 2003-11-13 for susceptibility weighted imaging.
Invention is credited to Haacke, E. Mark.
Application Number | 20030212322 10/143671 |
Document ID | / |
Family ID | 29400189 |
Filed Date | 2003-11-13 |
United States Patent
Application |
20030212322 |
Kind Code |
A1 |
Haacke, E. Mark |
November 13, 2003 |
SUSCEPTIBILITY WEIGHTED IMAGING
Abstract
A method for magnetic resonance imaging is described.
Inventors: |
Haacke, E. Mark; (Detroit,
MI) |
Correspondence
Address: |
FISH & RICHARDSON PC
225 FRANKLIN ST
BOSTON
MA
02110
US
|
Family ID: |
29400189 |
Appl. No.: |
10/143671 |
Filed: |
May 10, 2002 |
Current U.S.
Class: |
600/410 ;
382/131; 600/419 |
Current CPC
Class: |
G01R 33/5635 20130101;
A61B 5/02007 20130101; G01R 33/563 20130101; G01R 33/5607 20130101;
A61B 5/055 20130101; G01R 33/56 20130101; G01R 33/5608
20130101 |
Class at
Publication: |
600/410 ;
382/131; 600/419 |
International
Class: |
A61B 005/055 |
Claims
What is claimed is:
1. A method of MR imaging, comprising: obtaining a magnitude image;
obtaining a phase image; computing a phase image mask using the
phase image; applying the phase image mask to the magnitude image a
number q times; and selecting q by computing CNR as a function of
q.
2. The method of claim 1 wherein CNR is computed as a function of
SNR and .DELTA..phi..
3. The method of claim 2 wherein
CNR(q)=SNR*(1-(1-.vertline..DELTA..phi..v-
ertline./.pi.).sup.q)/sqrt(1+q.sup.2/.pi..sup.2).
4. The method of claim 1, 2, or 3 wherein selecting q further
comprises using a function dependent on the size of a feature of
interest.
5. The method of claim 4 wherein selecting q such that sqrt(A)
CNR(q) is greater than a value in the range from about 3 to about 5
where A is the area of the feature of interest measured in square
pixels.
6. The method of claim 1 comprising: selecting a filter for
reducing nonlocal contributions to the phase image; computing a
local phase image by filtering the first phase image with said
filter to reduce nonlocal contributions on said image; and wherein
computing a phase image mask further comprises using the local
phase image.
7. The method of claim 2 wherein .DELTA..phi. is the phase
difference between water and fat.
8. The method of claim 2 wherein .DELTA..phi. is the phase
difference between tissues with different amounts of iron.
9. The method of claim 1 or 6 comprising obtaining said magnitude
and phase image by selecting a first echo time that results in
partial volume cancellation of features of interest.
10. The method of claim 9 comprising obtaining images of veins.
11. The method of claim 9 comprising obtaining images of
microhemorrage.
12. The method of claim 9 comprising: obtaining a second phase
image by selecting a second echo time that results in partial
volume cancellation of features of interest; computing a corrected
phase image by using the first and second phase images; and wherein
computing the phase image mask further comprises using the
corrected phase image.
13. The method of claim 1 comprising selecting an acquisition
resolution such that the resolution is higher than the size of a
feature of interest; and acquiring magnitude and phase data at the
acquisition resolution.
14. The method of claim 13 wherein obtaining a magnitude and
obtaining a phase image comprises reconstructing a magnitude and a
phase image at a lower resolution than the resolution of the
acquired data.
15. The method of claim 14 wherein reconstructing a magnitude and a
phase image comprises using the magnitude data and the phase
data.
16. The method of claim 14 comprising selecting a filter for
reducing nonlocal contributions to the phase data; computing local
phase data by filtering the phase data with said filter to reduce
nonlocal contributions on the phase data; and wherein
reconstructing the magnitude and the phase image comprises using
the magnitude data and the local phase data.
17. A method of MR imaging comprising: computing CNR as a function
of q, SNR, and .DELTA..phi.; and selecting q, SNR and .DELTA..phi.
to produce a desired CNR.
18. The method of claim 17 comprising selecting an echo time to
produce the selected SNR or .DELTA..phi..
19. The method of claim 17 comprising selecting a set of echo times
to produce the selected SNR or .DELTA..phi..
20. The method of claim 17, 18, or 19 wherein
CNR(q)=SNR*(1-(1-.vertline..-
DELTA..phi..vertline./.pi.).sup.q)/sqrt(1+q.sup.2/.pi..sup.2).
21. The method of claim 17 wherein the desired CNR is defined such
that sqrt(A) CNR(q) is greater than a value in the range from about
3 to about 5 where A is the area of the feature of interest
measured in square pixels.
22. The method of claim 17 wherein CNR is dependent on the number
of data acquisitions; and selecting a number of data acquisitions
to produce the desired CNR.
23. The method of claim 22 comprising for a given total data
acquisition time, selecting a number of data acquisitions.
24. The method of claim 22 wherein CNR is computed according to
CNR(q)=SNR*sqrt(1/.alpha.)*exp((1-.alpha.)TE/T.sub.2*)*(1-(1-.vertline..D-
ELTA..phi..vertline./.pi.).sup.q)/sqrt(1+q.sup.2/.pi..sup.2)wherein
.alpha.=.DELTA..phi./.pi..
25. The method of claim 17 wherein SNR and .DELTA..phi. depend on
partial volume cancellations.
26. A method of MR imaging, comprising: obtaining a first phase
image by selecting a first echo time; obtaining a second phase
image by selecting a second echo time; obtaining a predicted phase
image by extrapolating the first phase image to the second echo
time; and computing a corrected phase image by computing the
difference between the predicted phase image and second phase
image.
27. The method of claim 26 comprising selecting a filter for
reducing nonlocal contributions to the corrected phase image; and
computing a local phase image by filtering the first phase image
with said filter to reduce nonlocal contributions on said corrected
phase image.
28. A method of MR imaging, comprising: obtaining a magnitude
image; obtaining a phase image; computing a phase image mask using
the phase image; applying the phase image mask to the magnitude
image a number q times; selecting an acquisition resolution such
that the resolution is higher than the size of a feature of
interest; and wherein obtaining a magnitude and obtaining a phase
image comprises reconstructing a magnitude and a phase image at a
lower resolution than the resolution of the acquired data.
29. A method of MR imaging, comprising: obtaining a phase image;
and applying minimum intensity projection to the phase image.
Description
TECHNICAL FIELD
[0001] This invention relates to magnetic resonance imaging.
BACKGROUND
[0002] Magnetic resonance (MR) imaging is a useful noninvasive
method for imaging the internal components of a wide array of
objects. Its noninvasive imaging of tissue in living subjects,
especially humans, is highly valued in the medical field.
[0003] In its most basic form, MR imaging measures nuclear spin
density throughout a sample. In this case, the image intensity is
proportional to the number of observed nuclear spins. Practically,
either the spin density, T.sub.1 or T.sub.2 of .sup.1H nuclei is
measured. Although such images provide valuable information about
an object, these parameters alone may not provide adequate image
contrast. Many dissimilar materials have very similar spin
densities, T.sub.1 or T.sub.2 and, therefore, such materials are
indistinguishable or in other words they lack contrast.
[0004] A technique for enhancing contrast is described in "Artery
and Vein Separation Using Susceptibility-Dependent Phase in
Contrast-Enhanced MRA", Wang et al., Journal of Magnetic Resonance
Imaging, 12:661-670 (2000), the entire contents of which is
incorporated herein by reference. In this technique, a magnitude
and phase image are obtained using a gradient echo sequence. The
magnitude image is operated upon using a mask computed from the
phase image.
SUMMARY
[0005] In one aspect the invention features a method of MR imaging,
including: obtaining a magnitude image, obtaining a phase image,
computing a phase image mask using the phase image, applying the
phase image mask to the magnitude image a number q times, and
selecting q by computing CNR as a function of q.
[0006] In another aspect, the invention features a method of MR
imaging including: computing CNR as a function of q, SNR, and
.DELTA..phi., and selecting q, SNR and .DELTA..phi.to produce a
desired CNR.
[0007] In another aspect the invention features a method of MR
imaging, including: obtaining a phase image and applying minimum
intensity projection to the phase image.
[0008] In another aspect, the invention features a method of MR
imaging, including: obtaining a first phase image by selecting a
first echo time, obtaining a second phase image by selecting a
second echo time, obtaining a predicted phase image by
extrapolating the first phase image to the second echo time, and
computing a corrected phase image by computing the difference
between the predicted phase image and second phase image.
[0009] In another aspect the invention features a method of MR
imaging including: obtaining a magnitude image, obtaining a phase
image, computing a phase image mask using the phase image, applying
the phase image mask to the magnitude image a number q times,
selecting an acquisition resolution such that the resolution is
higher than the size of a feature of interest, and wherein
obtaining a magnitude and obtaining a phase image comprises
reconstructing a magnitude and a phase image at a lower resolution
than the resolution of the acquired data.
[0010] Embodiments of the method may include one or more of the
following features.
[0011] The method may compute CNR as a function of SNR and
.DELTA..phi.. The method may compute CNR using
CNR(q)=SNR*(1-(1-.vertline..DELTA..phi..-
vertline./.pi.).sup.q)/sqrt(1+q.sub.2/.pi..sup.2). .DELTA..phi. may
be the phase difference between water and fat. .DELTA..phi. may be
the phase difference between tissues with different amounts of
iron.
[0012] The method may select q by using a function dependent on the
size of a feature of interest. The method may select q such that
sqrt(A) CNR(q) is greater than a value in the range from about 3 to
about 5 where A is the area of the feature of interest measured in
square pixels.
[0013] The method may include selecting a filter for reducing
nonlocal contributions to the phase image, computing a local phase
image by filtering the first phase image with the filter to reduce
nonlocal contributions on the image, and wherein computing a phase
image mask further includes using the local phase image.
[0014] Obtaining the magnitude and phase image may include
selecting a first echo time that results in partial volume
cancellation of features of interest. The method may include
obtaining images of veins. The method may include obtaining images
of microhemorrage.
[0015] The method may include obtaining a second phase image by
selecting a second echo time that results in partial volume
cancellation of features of interest, computing a corrected phase
image by using the first and second phase images, and the computing
the phase image mask includes using the corrected phase image.
[0016] The method may include selecting an acquisition resolution
such that the resolution is higher than the size of a feature of
interest, and acquiring magnitude and phase data at the acquisition
resolution. Obtaining a magnitude and obtaining a phase image may
include reconstructing a magnitude and a phase image at a lower
resolution than the resolution of the acquired data. Reconstructing
a magnitude and a phase image may include using the magnitude data
and the phase data.
[0017] The method may include selecting a filter for reducing
nonlocal contributions to the phase data, computing local phase
data by filtering the phase data with said filter to reduce
nonlocal contributions on the phase data, and reconstructing the
magnitude and the phase image inlcudes using the magnitude data and
the local phase data.
[0018] The method may include selecting an echo time to produce the
selected SNR or .DELTA..phi.. The method may include selecting a
set of echo times to produce the selected SNR or .DELTA..phi.. The
method may compute CNR as
CNR(q)=SNR*(1-(1-.vertline..DELTA..phi..vertline./.pi.).su-
p.q)/sqrt(1+q.sub.2/.pi..sup.2). The desired CNR may be defined
such that sqrt(A) CNR(q) is greater than a value in the range from
about 3 to about 5 where A is the area of the feature of interest
measured in square pixels. CNR may be dependent on the number of
data acquisitions and the method may include selecting a number of
data acquisitions to produce the desired CNR.
[0019] The method may include for a given total data acquisition
time, selecting a number of data acquisitions. The method may
compute CNR according to
CNR(q)=SNR*sqrt(1/.alpha.)*exp((1-.alpha.)TE/T.sub.2*)*(1-(1-
-.vertline..DELTA..phi..vertline./.pi.).sup.q)/sqrt(1+q.sup.2/.pi..sup.2)
wherein .alpha.=.DELTA..phi./.pi.. SNR and .DELTA..phi. may depend
on partial volume cancellations.
[0020] The method may include selecting a filter for reducing
nonlocal contributions to the corrected phase image, and computing
a local phase image by filtering the first phase image with said
filter to reduce nonlocal contributions on said corrected phase
image.
[0021] Embodiments may include one or more of the following
advantages. An advantage of embodiments is improved contrast in
magnetic resonance imaging. Using this method an imaging experiment
can be optimized by determining the contrast-to-noise ratios (CNR)
of a final image as a function of the signal to noise ratio of the
magnitude image (SNR), the phase difference between objects of
interest (.DELTA..phi.), and the number of times (q) a phase mask
is applied to the magnitude image. This permits selection of an
optimal value of q to maximize CNR for given experimental
conditions. For example, a given phase difference between features
of interest may vary between different experiments due to hardware
limitations, relaxation times, or susceptibility of the features of
interest, yet the analysis determines a value of q that optimizes
CNR in a final susceptibility weighted image in each of these
cases. Alternatively, or in addition, given a target CNR the
analysis provides experimental and processing parameters including
echo time and multiplier q that most likely will result in a final
image with the target CNR.
[0022] The entire contents of U.S. patent application Ser. No.
09/098,651, filed Jun. 17, 1998 and entitled "Application-specific
optimization of echo time in MR pulse sequences for investigating
materials with susceptibilities different from that of the
background in which they are embedded" is incorporated herein by
reference.
[0023] The details of one or more embodiments of the invention are
set forth in the accompanying drawings and the description below.
Other features, objects, and advantages of the invention will be
apparent from the description and drawings, and from the
claims.
DESCRIPTION OF DRAWINGS
[0024] FIG. 1 is a diagram that outlines a method for MR imaging
that utilizes phase information.
[0025] FIG. 2a is a plot of a theoretically predicted relation
between CNR and the multiplication exponent q.
[0026] FIG. 2b is a plot of a theoretically predicted relation
between CNR and the multiplication exponent q for a constant time
experiment with signal averaging.
[0027] FIG. 3 is a diagram that outlines a method for MR imaging
for visualizing features that are smaller than the resolution of
the acquired MR data.
[0028] FIG. 4 is a diagram that outlines a method for MR imaging
for visualizing features with a high resolution acquisition and
simultaneously taking the same data and reconstructing images at
lower resolution than the resolution of the acquired data.
[0029] Like reference symbols in the various drawings indicate like
elements.
DETAILED DESCRIPTION
[0030] FIG. 1 outlines an MR imaging method 100 that includes using
phase information directly as an image or using phase information
to enhance contrast in magnitude images. In step 110, the echo time
of a gradient echo imaging pulse sequence, TE, is chosen such that
the phase contrast between features of interest is sufficiently
above noise levels. A MR imaging system collects the data using the
imaging pulse sequence with the echo time TE. The acquired data is
then processed in step 120 to create a phase image. A filter is
applied to the phase image in step 130 to remove or reduce unwanted
phase effects. The resulting phase image can itself be used for
visualizing structures. For example, phase images of the brain
often may themselves be useful. The original acquired data is
processed again in step 140 to create a magnitude image. From this
filtered image, a mask is created in step 150 which transforms the
phase image into a set of values ranging between zero and unity and
a multiplication exponent q is selected. The mask is then applied
to the magnitude image q number of times in step 160 to enhance the
contrast in the magnitude image. The number of times, q, is
obtained by determining the CNR as a function of q. Each step will
be described in more detail.
[0031] Step 110: An understanding of the sources of phase variation
is useful in determining the optimum echo time. The phase of MR
data from a gradient echo pulse sequence is given by
.phi.=-.gamma..DELTA.B.sub.ZTE [1]
[0032] where .gamma. is the gyromagnetic ratio, .DELTA.B.sub.Z is
the variation in magnetic field from one tissue to the next, and TE
is the echo time. For example, if two tissues of interest have a
phase difference .DELTA..phi. caused by their susceptibility
difference, then under ideal conditions (i.e., ignoring T.sub.2
effects) the optimal choice of TE is such that .DELTA..phi.=.pi. or
.DELTA..phi.=-.pi.. A more realistic method includes the signal
decay due to T.sub.2 effects. An example of such a method chooses
the optimal TE by considering contrast-to-noise (CNR) between two
tissues with a phase difference of .DELTA..phi.. The CNR is given
by
CNR(.DELTA..phi.)=S*.DELTA..phi./.sigma. [2]
[0033] where .sigma. is the noise in the magnitude image and S is
the signal in the magnitude image. S is proportional to
exp(-TE/T.sub.2) and .DELTA..phi. is proportional to TE. The
optimum echo time, TE.sub.opt, is the echo time that maximizes the
function TE exp(-TE/T.sub.2). By setting the derivative of this
function to zero and solving for TE, it is found that the optimum
echo time TE.sub.opt=T.sub.2. This assumes that T.sub.2 is the same
for each of the two adjacent tissues. If the T.sub.2 of the tissues
differ, then the optimal choice of echo time lies somewhere between
the two values and can be calculated by deriving a similar CNR
function. However, this optimal echo time may be longer than
necessary. If the TE is chosen so that the CNR is larger than 4
then the phase difference between the two tissues will be visible.
The user can choose to go to the longer TE if better image quality
is desired.
[0034] If there are many tissues present in the sample, TE may be
chosen to allow all the data to be analyzed simultaneously. The
user might, for example, choose TE.sub.opt equal to the shortest
optimal TE and as long as this value yields a CNR greater than 4
for the other longer T.sub.2 tissues then reasonable contrast is
attained. Alternatively, the data can be collected as a series of
images during a multi-echo pulse sequence and the images then
processed yielding phase information at various echo times for each
tissue.
[0035] Once TE is chosen, the MR data set is acquired. A suitable
system for MR imaging is the Siemens Symphony (Siemens Corporation,
Iselin, N.J.) with a field strength of 1.5T. As systems change in
design or field strength, the steps described herein can be
appropriately modified to accommodate such changes. A suitable
pulse sequence is a 3D gradient echo pulse sequence.
[0036] Step 120: The acquired data are complex Fourier transformed
to create a real (R(r)) and imaginary (I(r)) pair of images. These
images are converted into a phase image .phi.(r) as follows:
.phi.(r)=arc tan(I(r)/R(r)). [3]
[0037] This step computes the phase image.
[0038] Step 130: As described in Eq. (1), the phase depends on
.DELTA.B.sub.z(r). The field variation, .DELTA.B.sub.z(r), can be
written as the sum of two components 1 B z ( r ) = B z 0 ( r ) + B
z M ( r ) . [ 4 ]
[0039] The first term, .DELTA.B.sub.z.sup.0(r), is representative
of the inhomogeneity in the applied field although it can contain
other effects such as off centered sampling of the data. Even with
modem high homogeneity superconducting magnets and high order
shimming, the applied main field varies throughout the sample
volume. On a typical MR imaging system this field inhomogeneity,
.DELTA.B.sub.z.sup.0(r) is typically about 0.5 ppm across a
patient's head, for example. The second term, B.sub.z.sup.M(r),
includes all the contributions from fields that are induced by the
applied field. These include chemical shift and susceptibility
effects.
[0040] Combining these two terms into Eq. [1], the phase of the MR
signal is given by 2 = - TE [ B z 0 ( r ) + B z M ( r ) ] . [ 5
]
[0041] Thus the phase depends on both the field inhomogeneity and
the magnetic field B.sub.z.sup.M(r) at that point. The magnetic
field at a point r due to the magnetizations depends on the
susceptibility, .chi..sub.M(r'), at every point r' in the sample.
The filtering of step 120, reduces the contributions of the field
inhomogeneities and the contributions from nonlocal susceptibility
effects.
[0042] Generally, these undesired phase effects have low spatial
frequency and are filtered out by applying a high pass spatial
filter to the phase image .phi.(r). An appropriate filter size is
chosen based on the given body part and magnet inhomogeneities. The
filter reduces these unwanted field effects to zero or a
satisfactory level. The high pass filter is then applied to create
what is effectively a local phase image, .phi..sub.L(r). The filter
not only removes the phase contributions from the field
inhomogeneity .DELTA.B.sub.z.sup.0(r) but also reduces most of the
nonlocal contributions to the phase image, including those coming
from the nonlocal sources of susceptibility.
[0043] For example, the sinuses in the brain can act like a local
dipole. Far from this source the field varies slowly as 3 1 r 3
.
[0044] By applying a high pass filter that removes low spatial
frequency variation, most of the slowly varying phase variations
(such as the long distance effects of the example dipole field) are
filtered out leaving the more local effects. A method to produce
such high pass filtering uses first a low pass filter of the phase
image. The low pass filtered image is complex divided into the
original image thus producing a high pass filtered image. The size
of the filter used depends on the echo time and the local fields
present. For an average 1.5T system, and with an echo time of 40
ms, a filter size of 64.times.64 or 96.times.96 may be used to
create the low pass filtered image. This image is then complex
divided into the original phase image to create an effective high
pass filtered image that is the local phase image .phi..sub.L (r).
Experimentally, for a field-of-view of 256 mm, a resolution of 0.5
to 1.0 mm in each of two dimensions and a filter size of
96.times.96, all visible low frequency effects are gone but 80% of
the local contrast remains intact for small objects around 5 mm in
size. A higher contrast remains for the 64.times.64 filter, but
there are more edge artifacts from the air/tissue interfaces that
otherwise do not appear with a 96.times.96 filter.
[0045] Furthermore, because the phase is directly proportional to
the echo time as shown in Eq. (1), when the echo time is reduced
(increased) the filter size can be reduced (increased). For example
using the above example, if instead a TE of 5 ms were used, then
the filter size could be reduced to 8.times.8 or 16.times.16 and
still effectively remove background field effects from air/tissue
interfaces.
[0046] In addition, the choice of filter also depends on the
susceptibility of the source. For example, if a filter size of
96.times.96 is chosen for TE=40 ms, and the images are acceptable
to the clinician, and there is a fresh clot present in the image,
then for data acquired at TE=5 ms, a smaller filter may be used. On
the other hand, if an old clot with 8 times higher susceptibility
due to the presence of a large concentration of hemosiderin is
present, then an image acquired with a TE=5 ms would show the same
signal changes in the magnitude and phase images as the longer echo
TE=40 ms scan and, therefore, to filter out the low spatial
frequency effect would require the same filter size as for the
TE=40ms case or 96.times.96. Larger (smaller) magnetic field
variations would require a larger (smaller) filter size.
[0047] After this filter is applied, a new source of image contrast
is provided: a filtered, local phase image. The phase image can be
displayed and analyzed directly if desired. Alternatively, or in
addition, contrast in the magnitude image is created by using the
local phase image to create a phase mask and then multiply it
against the magnitude image to create a susceptibility weighted
image as described further below.
[0048] Step 140: The original acquired data are complex Fourier
transformed to create a real (R(r)) and imaginary (I(r)) pair of
images. These image are converted into a magnitude image,
.rho..sub.m(r), as follows:
.rho..sub.m(r)=sqrt(R(r).sup.2+I(r).sup.2). [8]
[0049] Step 150: The local phase image is used to compute a mask,
.phi..sub.MASK(r). The mask takes on values between zero and unity
for each pixel location. The filtered phase image contains
susceptibility information (i.e., variations in phase) but these
changes in phase need to be converted into a set of numbers that
will enhance the contrast in the magnitude images. For example, a
suitable mask is computed from the local phase image by,
if .phi..sub.L(r)<.phi..sub.0 then .phi..sub.MASK(r)=1.
if .phi..sub.L(r)<.phi..sub.0 then
.phi..sub.MASK(r)=1+(.phi..sub.L(r)-- .phi..sub.0)/.pi. [6]
[0050] where .phi..sub.0 is a phase selected on the basis of for
example echo time, susceptibility or the size of features of
interest. In other embodiments, the mask could be defined as
if .vertline..phi..sub.L(r).vertline.=0 then
.phi..sub.MASK(r)=1.
if .vertline..phi..sub.L(r).vertline.>0 then
.phi..sub.MASK(r)=1-.vertl- ine..phi..sub.L(r).vertline./.pi.
[7]
[0051] In yet other embodiments, the algorithm that produces the
mask can be based either on theoretical grounds or on empirical
grounds. For example, the predicted susceptibility effects can be
used to calculate a mask or the mask may be simply found by
repeated observation of certain known tissues while empirically
modifying the mask with an eye towards optimizing contrast. The
choice of phase mask can be made to enhance one tissue type or
another. In one implementation, it can be used to separate two or
more tissue types into different images where tissue A may be
suppressed in one image and tissue B in another so as to give the
appearance of an image dominated by tissue B in the former case and
tissue A in the latter.
[0052] Step 160: The above phase mask, .phi..sub.MASK, is then
applied to the magnitude image by multiplying them together to form
a susceptibility weighted image:
.rho..sub.SWI(r)=.phi..sub.MASK(r)*.rho..sub.m(r) [9]
[0053] A more general application of this concept is to perform the
multiplication q times in the form:
.rho..sub.SWI(r)=(.phi..sub.MASK(r)).sup.q*.rho..sub.m(r) [10]
[0054] The choice of the mask multiplier, q, will depend, as is
discussed below, on the signal-to-noise (SNR), the object size, the
number of objects trying to be simultaneously seen and the value of
the phase difference between features of interest, .DELTA..phi.,
and requirements on the total time allocated for data acquisition.
The choice of q can be derived from the contrast-to-noise (CNR) as
follows for large values of SNR:
CNR(q)=SNR*(1-(1-.vertline..DELTA..phi..vertline./.pi.).sup.q)/sqrt(1+q.su-
p.2/.pi..sup.2) [11]
[0055] .DELTA..phi. is the phase difference, q is the number of
multiplications, S is the signal in the magnitude image, and
.sigma. is the noise in the magnitude image. Hence, SNR=S/.sigma.
is the local signal-to-noise between the two tissues (assuming they
both have the same magnitudes in this example; Eq. (11) can be
modified to accommodate different signal intensities in each
tissue). The numerator of Eq. (11) relates to the signal difference
as a function of q in the susceptibility weighted image between two
tissues with phase difference .DELTA..phi. (note Eq. (7)). The
denominator is the standard deviation of the noise in the
susceptibility weighted image as a function of q. The denominator
is determined by adding the noise in quadrature for the two terms.
The variance (the square of the error term) then is just given by
.sigma..sup.2(1+q.sup.2/.pi..sup.2) where the factor of q comes
from the fact that we multiplied the mask q times (and hence
variance increases by q.sup.2) and the factor of 1/.pi..sup.2 comes
from the fact that we normalized the mask with 1/.pi.. The CNR as
given in Eq. (11) therefore represents the signal difference
(contrast) divided by the standard deviation of the noise.
[0056] Referring to FIG. 2a, the relationship between
contrast-to-noise and the multiplication exponent q computed
according to Eq. (11) is shown. The relation is plotted for a
number of different values of phase difference. The relation shown
in FIG. 2a allows for the determination of the optimum q based on
the phase difference and SNR. Conversely, given a target CNR, the
relation allows for the determination of the combination of phase
difference, SNR and q that may result in a final image with that
target CNR.
[0057] Another example of such CNR optimization relates to constant
time experiments. Given a constant amount of time for data
collection, CNR can be optimized with respect to the competing
factors of phase difference versus signal averaging a number of
data acquisitions. A shorter echo time TE leads to a larger number
of repetitions of the scanning parameters so that the total time
stays the same but the combined SNR goes up by a factor of
sqrt(1/.alpha.). Here, .alpha. is the fractional reduction in the
repeat time of the experiment from an experiment with a phase
difference of .pi.. That is, .alpha.=.DELTA..phi./.pi.. A reduced
TE changes the SNR per acquisition because the signal varies as
exp(-TE/T.sub.2*). For a TE reduction factor of .alpha., the
combined signal increase is given by exp((1-.alpha.)TE/T.sub.2*).
This approximation does not include effects due to increases in the
gradients needed to accommodate the reduced TE time. If TE becomes
too short then the term sqrt(1/.alpha.) must be removed. Under
these conditions, the analysis that lead to Eq. (11) is modified
and produces:
CNR(q)=SNR*sqrt(1/.alpha.)*exp((1-.alpha.)/TE/T.sub.2*)*(1-(1-.vertline..D-
ELTA..phi..vertline./.pi.).sup.q)/sqrt(1+q.sup.2/.pi..sup.2)
[12]
[0058] Referring to FIG. 2b, TE=T.sub.2* is taken as the optimal
choice for TE. Under the assumption of a constant time experiment,
FIG. 2b reveals a TE chosen so that .DELTA..phi.=.pi. does not
yield the highest CNR and that a better choice would be an echo
time of 0.3 times this TE value using q=3. Even the case with 0.1
times this echo time yields a better CNR using q=5. Such a
relationship is helpful in the design of the experiment. The SNR
may drop at longer TE because of the T2* of the tissue and in such
cases, by choosing a shorter TE, it is still possible to produce
images with high CNR by taking advantage of signal averaging (i.e.,
taking the same acquisition time as with the longer TE scan) and
the appropriate value of q.
[0059] Further considerations can be made if the object size is
included. For example, if the size of the object is included, say
as the radius, r (where r is measured in pixels), of a circular
object, then a reasonable choice for q satisfies the relation
4<r sqrt(.pi.)CNR(q). [13]
[0060] The factor 4 in Eq. (13) can be in the range from about 3 to
about 5, depending on the expertise of the clinician interpreting
the images. The more general expression replaces the term r
sqrt(.pi.) with the term sqrt(A) where A is the area of the object
being evaluated in square pixels.
[0061] High resolution phase images will often allow for the
separation of healthy tissue from diseased tissue because they
often have different susceptibilities. For example, consider the
case of atherosclerosis, where the blood vessel wall is affected.
The diseased tissue may include: fatty plaque, fibrous plaque,
hemorrhage, calcium deposits and vaso vasorum uptake of contrast
agent.
[0062] Particular application of this method includes the
following:
[0063] 1) Increasing Contrast Between Water and Fat:
[0064] In order to simulate the effects of a shorter echo time than
can be collected with a given piece of equipment, the complex image
from a sequence with an echo time of TE1 can be taken and divided
into that of an image with an echo time of TE2 to get an equivalent
echo time of TE2-TE1. For example, with TE2=10 ms and TE1=8 ms, the
equivalent echo time is 2 ms. Collecting the data this way or with
equipment that can actually acquire data with a TE=2 ms, the
resulting phase images can be used to suppress fat in the brain or
coronary arteries. Using the former approach, data are acquired
with a resolution of 1.times.1.times.1 mm.sup.3 using a 3D gradient
echo sequence. This value for TE is chosen to obtain fat about .pi.
radians out of phase with water. The resulting phase images are
well smoothed and clearly discriminate water and fat without the
needed for any anti-aliasing programs. On the basis of Eq. (12) and
since the phase theoretically lies between about 0.8.pi. to
0.9.pi., the optimal multiplication factor q should be 2. The fat
signal in a T1 weighted sequence starts out being much brighter
than the surrounding tissue. Therefore, the fat signal is reduced
but not eliminated by the application of the mask once (i.e., q=1)
and contrast with other brain tissues is poor. Applying the filter
twice (i.e., q=2) improves CNR (actual results may be even better
than predicted in Eq.(11) because of the increased signal of the
fat relative to other tissue). Using a value of q=2 better
suppresses the fat. This gives good contrast in the T.sub.1
weighted images or in suppressing fat signals in angiographic
images of the body.
[0065] 2) Increasing Contrast Between Tissues With Different
Amounts of Iron.
[0066] The basal ganglia tend to gather iron over time. For
example, the globus pallidus has larger iron content than
neighboring tissues such as the caudate nucleus or putamen. For a
TE of 80 ms, the phase of iron in brain tissue tends to be around
.pi./6. This would suggest that q should be 6 or greater to obtain
the best contrast in the susceptibility weighted image. However,
even using a q=4 still gives good contrast so the method allows
reasonable flexibility if there are other tissues or contrasts that
one might wish to keep and yet still enhance the iron contrast as
well. This method will enhance the boundaries between these tissues
and the visibility of the structures. Further, if there is a
disease process that increases the iron content of the tissue, this
difference will be manifest as a darkening of the tissue with the
increased iron content allowing the physician to better diagnose
the condition. If the desire is to quantitate the amount of brain
iron or just to see the boundaries between other tissues, the
filtered phase image itself can also be used. At a TE of 80 ms, a
heavier filter such as 96.times.96 or 128.times.128 should be used
to remove background inhomogeneities. If the regions of interest
are centrally located in the brain far from air tissue boundaries,
then a filter of 32.times.32 can be used on each image. The filter
can also be applied in three dimensions as a 32.times.32.times.32
filter.
[0067] FIG. 3 outlines an MR imaging method 200. Typically, method
200 is used when features of interest are smaller than the
resolution of the acquired data. Features that are physically
smaller than the voxel size of the acquired data can lead to
partial volume cancellations by which image contrast is increased.
In addition to partial volume cancellations, such small features
also impart information in the phase of the MR signal. Thus method
200 optimizes the parameters such as echo time, filter size, mask
exponent, in order to optimize the final image contrast.
[0068] An MR imaging system collects the data (Step 210). Similar
pulse sequences are used as for method 100. The echo time is chosen
based on the optimal cancellation effect. This occurs when the
phase difference is .pi. radians. However, it may not always be
possible to collect data with the echo time associated with this
value if this echo time is so long that little signal is left. In
that case, a shorter echo time can be chosen and the number of
multiplications is increased to accommodate the smaller
cancellation and smaller phase effects. Even if the desired echo
time is reached, a further enhancement of the contrast can be
achieved using the mask multiplication described in method 100.
These two features allow for a balance between the benefits of the
contrast achieved from the phase image and the contrast achieved
from any partial signal cancellations. Examples discussed below
include separating arteries from veins and imaging small
hemorrhages. The MR system applies standard data processing
techniques to compute a phase image, .phi.(r), across the sample
(Step 220).
[0069] The partial volume effects are optionally further
highlighted by collecting data at two echo times TE1 and TE2 (Step
230). The phase from echo one at TE1 is multiplied by TE2/TE1 to
create a predicted phase .phi..sub.Pred(r) at TE2. This phase is
then subtracted from the phase .phi..sub.Exp(r) of the second echo
at TE2 to give a corrected phase image .phi..sub.CORR(r):
.phi..sub.CORR(r)=.phi..sub.Exp(r)-.phi..sub.Pred(r) (14)
[0070] This operation gives zero phase in all areas of the image
except where partial volume effects occur. In areas where partial
volume effects occur, the phase behavior is non-linear. For
example, for a vessel parallel to the field in a voxel with 50%
signal coming from gray matter and 50% from the blood at an echo
time of 25 ms at 1.5T, the phase of the resulting vector is
45.degree. (this is because the phase of the blood is 90.degree.
but the phase of the gray matter is zero). At TE=50 ms, the blood
signal will dominate and the phase will be 180.degree.. If the
blood has less than 50% contribution then the phase will be zero.
In either case, when TE is doubled from 25 to 50 ms the phase does
not double (hence, it is non-linear in its behavior). On the other
hand, the material that sees a background field inhomogeneity has a
linear phase behavior and all of these terms vanish. If this step
is applied, the resulting phase image .phi..sub.CORR(r) replaces
the original phase image in the following process.
[0071] An appropriate filter size is computed and a data processing
algorithm applies this filter to the phase image (or the phase
corrected image) producing a local phase image, .phi..sub.L(r)
(Step 240). The filter removes the phase contributions from the
field inhomogeneity .DELTA.B.sub.z.sup.0(r) and furthermore the
filter reduces most of the nonlocal contributions to the phase
image. These can include eddy current phase effects as well as
shifts caused by a poorly centered echo.
[0072] The raw data is used to compute a magnitude image
.rho..sub.m(r). (Step 250)
[0073] Using the filtered local phase image, a mask,
.phi..sub.MASK(r) is calculated and the mask exponent q is computed
in a similar fashion as described in methods 100 (Step 260).
[0074] The mask is applied to the magnitude image to form a final
susceptibility weighted image (Step 270).
.rho..sub.SWI=(.phi..sub.MASK).sup.q* .rho..sub.m(r) [15]
[0075] Applications of this method include:
[0076] 1) Imaging Veins.
[0077] Veins have a change in susceptibility compared to arteries
and surrounding tissue. When veins are smaller than a voxel, and
echo time increases toward .pi. radians, the signal from the veins
begins to cancel that from the surrounding tissue. This makes them
visible (they appear dark) compared to arteries which often appear
isointense or bright in the images. Small veins (less than a pixel
in size) parallel to the field will be best seen with echo times of
40 to 50 ms. Small veins perpendicular to the main field will be
ideally enhanced at 1.5 T with an echo time of about 80 to 100 ms.
For vessels that are between 0.2 to 0.5 mm in diameter, for
example, an in-plane resolution of 0.5 mm.times.0.5 mm to 1.0
mm.times.1.0 mm would be a good choice. For 3 T, the echo time
would be reduced to about 40 to about 50 ms. For smaller vessels a
higher resolution should be used. Diseased tissue can extract more
oxygen from the blood making the level of deoxyhemoglobin in the
veins increase and hence its susceptibility increase. This would
then make the perpendicular vessels visible at the shorter echo
times from about 40 to 50 ms and would be an indicator of a problem
in the tissue drained by those veins that otherwise would normally
not have been seen until the later echo times. On the other hand,
since the brain contains vessels of varying size and orientation,
it is safe to apply a large q on the order of 4 to 6 to show good
contrast from vessels with smaller phase changes or partial volume
effects which also reduce the local phase. One can also process a
series of images to view images with different values of q and look
for different structures of interest from image to image.
[0078] 2) Imaging Microhemmorrhage.
[0079] When a clot or hemorrhage occurs in the tissue, the
susceptibility goes up dramatically, perhaps by as much as a factor
of 4 to 10 or more. This means that the cancellation effects can be
seen at much shorter echo times and for much smaller sized objects.
For example, a sphere of volume 1/4 that of a pixel would be seen
best at an echo time of 40 to 80 ms if it had the same
susceptibility of normal veins. However, if its susceptibility goes
up by a factor of 4 the echo time could be reduced by a factor of
4. Alternatively, if the echo time remains large, then the volume
of the object required to cause significant signal loss will also
go down by a factor of roughly 4. The multiplication factor q will
be chosen based on the resulting phase in the region of interest.
Often small hemorrhages are visible with this method that may not
otherwise be visible with conventional technology.
[0080] FIG. 4 outlines an MR imaging method 300 that uses phase
information typically induced by susceptibility effects to varying
degrees and also uses image reconstruction at a variety of
resolutions less than or equal to the acquisition resolution. Data
are collected at one or more echo times to enhance features of
interest.
[0081] An optimum value or set of values for TE is chosen (Step
310). An MR imaging system collects the data for a specified TE
using a single echo scan or a range of TE values using a multi-echo
scan at an acquisition resolution chosen to be higher than the
resolution of some of the features of interest (Step 320). Examples
which are discussed below are imaging small vessels, imaging
hemorrhage or local mineralization. The MR system applies standard
data processing techniques to compute a phase image, .phi.(r),
across the sample (Step 330). An appropriate filter size is
computed and a data processing algorithm applies this filter to the
phase image producing a local phase image, .phi..sub.L (r) (Step
340). The filter removes the phase contributions from the field
inhomogeneity .DELTA.B.sub.z.sup.0(r) and furthermore the filter
reduces most of the nonlocal contributions to the phase image. The
filter size is chosen based on the echo time and the extent and
spatial variations in the background field. The slower the spatial
variation, the smaller the filter size required to correct the
data.
[0082] The MR system applies standard data processing techniques to
compute a magnitude image, .rho..sub.m(r), at the acquisition
resolution (Step 350). The information in the local phase image and
the magnitude image are combined to form a complex image,
.rho.(r)=.rho..sub.m(r)*exp(i- .phi..sub.L(r)), and a new phase and
magnitude image are reconstructed at a lower resolution than the
acquisition resolution (Step 360). In other embodiments, the
complex image can be formed from the original magnitude and the
original phase images by .rho.(r)=.rho..sub.m(r)*exp(i .phi.(r)).
The choice will depend on which of the two images yield the best
contrast-to-noise. For example, in some cases, by constructing the
complex image using the local phase image the effects of background
fields are removed while the effects of the local sources of signal
change from blood vessels or hemorrhage are not altered. The
particular lower resolution chosen would correspond to a resolution
comparable to some feature of interest. Typically, this
reconstruction includes vector summing the magnitude and phase of
each voxel at the acquisition resolution until the lower resolution
voxel size is attained. This reduction from a high resolution to a
low resolution image is effected by filtering the complex data. For
example, to collapse the image from a 0.5 mm.times.0.5 mm image to
a 1.0 mm.times.1.0 mm image can be done in several ways. One way
uses a sliding window filter defined by the weighting coefficients
w(i)=0.5 for i taking on the values 1 and 2 in the formula:
.rho..sub.new(m)=w(1).rho.(m)+w(2).rho.(m+1) for all values of m
from 1 to n (where there are n pixels in the image). As discussed
above, .rho.(m) and .rho.(m+1) are complex valued. When m=n,
.rho.(n+1) is set to zero and w(1) is set to unity. This is a
sliding filter. Only the odd points in m may be taken hence
reducing the matrix size to n/2 points to save space. Variants of
this filter such as w(1)=0.25, w(2)=0.5, w(3)=0.25 can be used with
the modified formula .rho..sub.new(m)=w(1)
.rho.(m)+w(2).rho.(m+1)+w(3).rho.(m+2) for all values of m from 1
to n (where there are n pixels in the image). When m=n-1,
.rho.(n+1) is set to zero and w(1) and w(2) are set to 0.5 When
m=n, .rho.(n+1) and p(n+2) are set to zero and w(1) is set to
unity. This one-dimensional filter can be applied also as a 2D
filter or 3D filter by simply applying the filter in both or all
three directions. All such filtered images can then be viewed
separately as if they had been acquired with the lower resolution.
The advantage of viewing the data this way is that each scale that
the image is viewed at will reveal unique features related to the
scale (or size) of the pixels. Normal venous blood vessels will be
best revealed when the pixel size is roughly 4 times smaller than
the resolution (or voxel size). Small hemorrhages may be best seen
when the voxel volume is ten times larger than the hemorrhages
(depending on the age of the hemorrhage).
[0083] A mask .phi..sub.MASK(r) is computed and a mask exponent q
is computed (Step 370). The phase mask can be applied to the
reconstructed magnitude image, .rho..sub.m.sub..sub.--.sub.new(r)
to form a final susceptibility weighted image (Step 380) as
described in method 100 or 200.
.rho..sub.SWI(r)=(.phi..sub.MASK(r)).sup.q*.rho..sub.m.sub..sub.--.sub.new-
(r) [16]
[0084] This can be done using either the high resolution original
phase images or the lower resolution images. The advantage of the
latter is there will be an improvement in signal-to-noise in the
phase images (but a loss of resolution and hence a loss of phase
information). On the other hand, the original phase will have a
better defined set of edges and better defined phase and may in
some circumstances yield better phase masked information in the
images. Applications of such a method include:
[0085] 1) Imaging Veins.
[0086] Veins have a change in susceptibility compared to arteries
and surrounding tissue. When veins are smaller than a voxel, and
echo time increases toward .pi. radians, the signal from the veins
begins to cancel that from the surrounding tissue. This makes them
uniquely visible (they appear dark) compared to arteries which
often appear isointense or bright in the images. Small veins (less
than a pixel in size) parallel to the field will be best seen with
echo times producing phase differences of c or approximately 40 ms
to 50 ms. Small veins perpendicular to the main field will be
ideally enhanced at 1.5T with an echo time of 80 to 100 ms. For
vessels that are between 0.2 to 0.5 mm in diameter, for example, an
in-plane resolution of 0.5 mm.times.0.5 mm to 1.0 mm.times.1.0 mm
would be a good choice. However, veins can be as small as 50
microns in size. By imaging at as high a resolution as possible,
for example at 250 microns in humans or 100 microns say in small
animals such as rats or mice, and then reconstructing a series of
images with lower resolutions such as 500 microns, 1 mm and 2 mm,
many different features can be revealed in the different images.
This is akin to using a microscope to focus in on a structure of
interest. Everything else may become blurry for a given focus, but
what is blurry in one image will be sharper in a different
resolution image. The optimal q value will depend on how the phase
changes for the smaller voxel size. Generally, to obtain the best
CNR, as the resolution decreases (the voxel size increases) the q
value will need to be increased. However, the larger CNR of the
lower resolution images will partly offset the need to use a larger
q value. If the need is to see contrast typically then as long as
CNR is greater than 4 that q value will suffice.
[0087] 2) Imaging Microhemorrhage.
[0088] When a clot or hemorrhage occurs in the tissue, the
susceptibility goes up dramatically, perhaps by as much as a factor
of 4 to 10 or more. This means that the cancellation effects can be
seen at much shorter echo times and for much smaller sized objects.
For example, a sphere of volume 1/4 that of a pixel would be seen
best at an echo time of 40 to 80 ms if it had the same
susceptibility of normal veins. However, if its susceptibility goes
up by a factor of 4, the echo time could be reduced by a factor of
4. Alternatively, if the echo time remains large, then the volume
of the object required to cause significant signal loss will also
go down by a factor of 2 to 4. Often many small hemorrhages are
visible with this method that may otherwise not be visible using
conventional imaging methods.
[0089] Again, clots of different sizes can be enhanced in terms of
their visualization by varying the resolution in the image as
described above. Hemorrhages can occur at all levels from a few
dozen microns to on the order of a millimeter. By imaging at as
high a resolution as possible, for example at 250 microns in humans
or 100 microns say in small animals such as rats or mice, and then
reconstructing a series of images with lower resolutions such as
500 microns, 1 mm and 2 mm, many different features can be revealed
in the different images. Again the optimal q value will depend on
the available CNR, phase and imaging time available. Hemorrhages on
the order of a cubic mm may be best viewed with a resolution of 2
mm while a small hemorrhage on the order of 250 microns may be best
viewed with a resolution of 0.5 mm.
[0090] In other embodiments, the above methods can be applied to
produce a series of images. From such a series, a minimum intensity
projection may be used to form a new composite image. Once a series
of images has been created either with or without phase masking and
with or without reconstructing with different resolutions, a
minimum intensity projection may be used to best visualize a
connected series of vessels or other structures or just as a means
of viewing all the information in a given area relative to the
anatomy better. This method takes a series of images and searches
along a set of rays in a given direction and chooses the smallest
value along that ray to write out to a new projected image. This
can be applied to the original magnitude images or the phase
processed images. It can be applied to any image at any echo time.
It can also be applied to the phase images themselves.
[0091] The above described methods are another means by which to
visualize what structures are being enhanced by the phase masking
process. For example, the contiguity of vessels may be seen in
these images or the location of hemorrhages relative to the basal
ganglia or the venous vasculature may be seen in this way.
[0092] A number of embodiments of the invention have been
described. Nevertheless, it will be understood that various
modifications may be made without departing from the spirit and
scope of the invention. Accordingly, other embodiments are within
the scope of the following claims.
* * * * *