Consider first the theory underlying sensitivity encoding, or SENSE, which can be extended to provide an understanding of local projection calibration as used in the present invention.
In MRI, data acquisition can be viewed as analyzing the spatially varying transverse magnetization, m(r), by projecting it onto a collection of encoding functions which take the form ej(k, r)=sj(r)g(k, r) where sj(r) is the sensitivity of receiver coil j and g(k, r)=exp(−i2πkTr) is the gradient encoding function for location k. When only one receiver coil is used, each encoding function can be uniquely identified by a specific location in k-space. When using multiple coils, the notion of location can be extended by defining an encoding location to be the pair of values, (j, k), consisting of an integer coil index and continuous-valued three-tuple k-space location k, that uniquely identifies an encoding function. The projection of m(r) onto an encoding function is accomplished by the integral
where dj(k) is the resulting datum acquired at encoding location (j, k).
Reconstruction can be viewed as an attempt to synthesize the magnetization image from the results of this analysis. Typically, a finite number of data points are acquired and a finite number of voxel values are reconstructed. The acquisition set, A, contains the encoding locations acquired in the scan,
A={(j,k)|dj(k) was acquired in the scan}, (2)
and the acquired data can be assembled in the vector dA. Since the encoding is linear, a linear reconstruction is appropriate, which can be written as
{circumflex over (m)}=RAdA, (3)
where the vector {circumflex over (m)} is the reconstruction estimate of the voxel values and RA is the contains reconstruction matrix, each row of which synthesizes an image voxel value from the acquired analysis data.
Pruessmann et al. showed that when the magnetization, m(r), can be approximated with a Dirac delta function at the center of each voxel, the reconstruction matrix, RA, that minimizes the noise in the reconstructed image can be expressed as
RA=EA\, (4)
where E\A=(EAHΨA−1EA)−1EAHΨA−1 is a pseudo-inverse of the encoding matrix EA. We will discuss EA in more detail shortly. ΨA, the sample noise matrix, is included to calculate the pseudo inverse that minimizes the noise in the reconstructed image. In general, this description assumes the Dirac delta voxel approximation, allowing functions of r to be written as vectors with a total number of elements of Nv, the total number of voxels. With this discretization of the magnetization, Eq. 1 can be written as
d
j(k)=ejT(k)m, (5)
where m is the Nv×1 magnetization vector, containing the magnetizations of the voxels, and ej(k) is the Nv×1 encoding vector for encoding location (j, k). Note that the encoding vector can still be broken up into the vector representation of the sensitivity and gradient encoding terms, i.e. ej(k)=sj·g(k), where sj is multiplied by g(k) using the Hadamard, or entry-wise, product.
Each row of the encoding matrix, EA, consists of an encoding vector in the acquisition set such that
dA=EAm. (6)
When the magnetization is fully encoded, the set of encoding vectors comprising EA span the space of the magnetization vector m. In this case, Eq. 4 can be used to find the reconstruction matrix and the image can be reconstructed using Eq. 3. Since the final objective of any reconstruction method is to reconstruct the image, this is usually how SENSE theory is employed.
Since the APPEAR method, as shown later, involves synthesis of unacquired data, we note that, alternatively, SENSE theory can be employed to synthesize, from the acquired data, any datum {circumflex over (d)}j(k) for an unacquired location k within the extent of acquired k-space. As SENSE theory assumes that the coil sensitivities are known (and hence the encoding functions are known), we can replace the magnetization vector in Eq. 5 with the SENSE estimate of the magnetization from Eq. 3:
where wj,j′,A(k,k′) is a complex scalar value formed by multiplying ejT(k)| by the column of RA corresponding to the encoding location (j′, k′). From Eq. 9 it is clear that the estimated datum, {circumflex over (d)}j(k), is simply a linear combination of the acquired data. The linear combination weights can be denoted by the vector wj,AT(k)=ejT(k)RA, allowing Eq. 9 to be rewritten as
{circumflex over (d)}j(k)=wj,AT(k)dA. (10)
While Eq. 10 is very useful for showing how an unacquired datum can be synthesized from the acquired data, it does not provide much intuition as to the quality of the synthesis. A better intuition can be obtained by expressing the synthesized datum in terms of an estimated encoding vector. This can be accomplished by substituting Eq. 6 into Eq. 10:
{circumflex over (d)}j(k)=wj,AT(k)EAm=êj,AT(k)m. (11)
{circumflex over (e)}j,AT(k)=wj,AT(k)EA. (12)
Since we use the idea of an estimated encoding vector extensively as we develop the theory for local projection calibration, it is useful to discuss it in further detail. While both the magnetization and the encoding vector are written as vectors to take advantage of matrix notation, both are best viewed as images. Acquiring a datum as in Eq. 5 can then be viewed as multiplying the magnetization image by an encoding vector image and summing all of the resultant voxel values. By expressing a synthesized datum in terms of an estimated encoding vector, as in Eq. 11, the synthesized datum can also be viewed as the result of summing the multiplication of two images (the magnetization image and the estimated encoding vector image). If the estimated encoding vector for an encoding location is not the same as the true encoding vector for that location, the synthesized datum can be in error. This can be made more concrete by defining the error in the estimated encoding vector as the vector
εj,AT(k)={circumflex over (e)}j,AT(k)−ejT(k). (13)
The error in the estimated encoding vector can also be viewed as an image and the error in the synthesized datum can then be calculated by multiplying the magnetization image by this error image and summing the voxel values. To ensure accurate synthesis, the error in an estimated encoding vector image should be zero, or sufficiently close to zero, at any voxel locations that contain magnetization. Note, however, that an estimated encoding vector and its associated error vector are dependent solely on the linear combination weights and the encoding vectors of the acquired data; they do not depend on the magnetization itself. Thus, the error in the estimated encoding vector, which can be viewed as an image, gives us the ability to see the quality of a set of linear combination weights independently from the magnetization being imaged.
When the magnetization is fully encoded, and the coil sensitivity functions known, the linear combination weights calculated by SENSE give a perfect estimate of the encoding vector. Since typically the magnetization is over-encoded, with more data acquired than voxels reconstructed, the SENSE weights are not the only weights that would give a perfect estimate of the encoding vector. However, when the acquired data contains noise, these SENSE weights do give the linear combination that minimizes the noise in the synthesized datum.
As can be seen from Eq. 10, when SENSE is used to synthesize data at unacquired k-space locations, it uses a linear combination, similar to GRAPPA, PARS and APPEAR. Since all of these methods can be expressed as procedures for synthesizing unacquired data using a linear combination, the differences in image quality between these methods can be attributed to different choices for linear combination weight values. When the coil sensitivities are known, the weights calculated by SENSE allow the encoding vectors to be perfectly estimated, resulting in removal of aliasing artifacts. However, when errors exist in the coil sensitivity estimates, these errors are propagated to the weights calculated by SENSE, leading to imperfect estimations of the encoding vectors and possible residual aliasing artifacts. In the next section we show how APPEAR uses local projection calibration to obtain linear combination weights without estimating the coil sensitivities, and show that these weights give estimated encoding vectors with very little error.
We now describe the APPEAR method for finding linear combination weights used to synthesize unacquired data. After using the APPEAR method to synthesize data across a full-FOV grid for each coil, separate coil images can be obtained by computing the Fourier transform of each grid. These coil images can then be combined to form a composite image; for this embodiment, the combination is done using sum-of-squares.
We divide the development of the APPEAR method into two sections. In the first section, we develop the local projection calibration technique and show that the technique is able to find high quality linear combination weights. While the local projection calibration technique does not assume that the coil sensitivity functions are known, it does make the assumption that within a central region of k-space, the data values are known or can be found for every encoding location (j, k). Since invariably only a finite amount of data is acquired, we must be able to synthesize appropriate values for unacquired locations within the central region.
In the second section, we develop an interpolation scheme that allows a data value at any encoding location within a sufficiently sampled central region of k-space to be synthesized from the acquired data. The APPEAR method uses the interpolation scheme developed in the second section to synthesize data at encoding locations within the central region as needed by the local projection calibration technique.
Local Projection Calibration: Despite Eq. 12, a small subset of the acquired encoding vectors (a few rows of EA) is sufficient for estimating an unacquired encoding vector, when the chosen subset of acquired encoding vectors are local to the k-space location of the encoding vector to be estimated. By limiting the number of acquired encoding vectors used in the estimate, local projection calibration can find high-accuracy weights using the sufficiently sampled central region of k-space. Self-calibrating PARS [5] also synthesizes an unacquired datum from acquired data in a limited local neighborhood in k-space, and uses a sufficiently sampled central region of k-space for calibration. However, the self-calibration technique [6] used by PARS, which uses the sufficiently sampled central region to estimate the coil sensitivities, is different from the local projection calibration technique (which does not estimate the coil sensitivities) and finds a different set of linear combination weights. In this section, we develop the local projection calibration technique, leaving a comparison between self-calibrating PARS and local projection calibration for the discussion section.
We start by introducing some notation that allows us to work with local acquired data. We then specify how the weights for the local neighborhood are calculated. Finally, we show that when the weights are calculated in this way, the errors in the estimated encoding vectors weighted by the magnetization are minimized.
Instead of using the entire acquisition set, A, we define the local set, L(k), as a set of acquired locations local to k-space location k:
(k)={(j,k+Δk)|(j,k+Δk)∈A,∥Δk∥<κ}. (14)
Essentially, L(k), as described in Table III and illustrated in
{circumflex over (d)}j(k)=wj,(k)T(k)d
(k) (15)
and the associated estimated encoding vector can be expressed by replacing the acquisition set, A, with the local set, L(k), in Eq. 12:
{circumflex over (e)}j,(k)T(k)=wj,
(k)T(k)E
(k). (16)
Each row of EL(k) contains an encoding vector at one of the locations in L(k). Note that EL(k) has far fewer rows than EA and the encoding vectors comprising EL(k) are not expected to span the space of the magnetization vector m. As such, êj,(k)T(k) will not be a perfect estimate of ejT(k). Rather, we desire the linear combination weights, wj,
(k)T(k) |, that minimize the magnitude of the error in the estimated encoding function:
For it to be possible to use Eq. 17 directly to find the optimal weights, the encoding vectors would need to be known, implying that the coil sensitivities would also need to be known. Local projection calibration does not use Eq. 17 directly. Instead, it provides a way to find near optimal local weights without needing to know the encoding vectors.
Local projection calibration takes advantage of the fact that linear combination weights which are found in one region of k-space can be applied in a completely different region. The reason such an approach works is because the element-by-element magnitude of the error in the estimated encoding vector, |εj,(k)(k)|, which we refer to as the magnitude image of the error in the estimated encoding vector, does not change when the encoding vector location and its neighborhood is shifted in k-space from position k to position k′. This can be shown by noting that shifting an encoding vector in k-space is equivalent to multiplying the vector by a gradient encoding term, allowing us to write ej(k′)=ej(k)·g(k′−k). Since εj,
(k)(k) is a linear combination of encoding vectors, the shifted error vector can be written as εj,
(k)(k)·g(k′−k)|. Finally, since
g*(k,r)g(k,r)=exp(i2πkTr)exp(−i2πkTr)=1
|εj,(k)(k)·g(k′−k)|=|εj,
(k)(k)|. (18)
Because shifting in k-space does not affect the magnitude of an image-space vector, the same weights that minimize the magnitude of the shifted error vector will minimize the magnitude of the original error vector. Thus, what matters in determining the weights is not where the encoding vectors are in k-space, but the pattern they form relative to the k-space location of the encoding vector being synthesized. To take advantage of the ability to freely shift in k-space, the pattern set is defined as
P(k,k′)={(j,k′+Δk)|(j,k+Δk)∈(k)}, (19)
which takes the pattern of acquired locations around location k and centers them around location k′. Illustrations of A, L(k) and P(k, k′) are given in
{circumflex over (e)}j,P(k,k′)T(k′)=wj,(k)T(k)EP(k,k′). (20)
Equation 20 expresses the estimate of the encoding function at location (j, k′) from encoding functions in the local neighborhood of k′ that are of the same pattern as the acquired encoding functions are about k. The error in the estimated encoding vector in Eq. 20 can be written as
Thus, using the pattern set, Eq. 18 can be written as
|εj,P(k,k′)(k′)|=|εj,(k)(k)|. (24)
Note that both Eq. 16 and Eq. 20 use the same set of weights, Wj,L(k)(k), to calculate the estimated encoding vector. When the same weights are used, Eq. 24 shows that the magnitude images of the error vectors for both encoding vectors are the same.
Without knowing the coil sensitivities, no encoding vectors are known, so we cannot find the linear combination weights using Eq. 17. However, we do have sufficiently sampled data within the low spatial frequency central region of k-space and this data can be used to generate the linear combination weights.
All of the data used to find the linear combination weights comes from the so-called central region of k-space. That is, local projection calibration assumes that there is a region, called the central region, where dj(k) is known, or can be found, for all encoding locations (j, k) as long as is within the central region. In addition to the central region, we define the central region as a region within the central region as shown in
Local projection calibration determines the local weights by minimizing
∥wj,(k)T(k)[dP(k,k′
where k′1 . . . k′N denote N k-space locations that fall on a full-FOV Cartesian grid in the central region and dP(k,k′) contains data at the locations in P(k,k′). We will develop more insight into the choice of k′1 . . . k′N shortly, as we analyze Eq. 25. The weights can be computed directly as
w
j,
(k)
T(k)=[dj(k′1) . . . dj(k′N)] [dP(k,k′
While Eq. 26 shows how the local projection calibration technique finds linear combination weights using data from the central region, it does not give much information on the properties of these weights. We now take advantage of the notation we have developed to demonstrate that the weights found by minimizing Eq. 25 will minimize the error in the estimated encoding vector weighted by the magnetization. That the minimization is performed on the magnetization-weighted error in the estimated encoding vector and not on the unweighted error in the estimated encoding vector is to some degree unavoidable. However, this weighting is not undesirable, the estimated encoding vector will tend to be more accurate where there are large amounts of magnetization. Errors in the estimated encoding vectors at locations where there are amounts of magnetization can result in large errors in the synthesized data values, so especially important that the estimated encoding vectors be accurate at these locations.
To demonstrate that such a minimization is, in fact, accomplished by local projection calibration, we start by looking at the data estimation error for a k-space location k′, within the central region and expanding each data value in terms of its encoding vector and the magnetization,
The expression gT(k′)[εj,P(k,0)(0)·m] can be interpreted in a very natural way. First of all, note that εj,P(k,0)(0) is a special case; since ej(0)=sj, the sensitivity of coil j, êj,P(k,0)(0) is an estimate of the sensitivity of coil j and εj,P(k,0)(0) is the error in that estimate. εj,P(k,0)(0)·m is then the sensitivity estimation error weighted by the magnetization. Finally, multiplication by gT(k′) computes the discrete space Fourier transform of the weighted sensitivity estimation error, evaluated at k′.
In Eq. 25, the magnitude is taken of a vector, each element of which contains the data estimation error, wj,(k)T(k)dP(k,k′)−dj(k′)|, for one of the k′ locations in k′1 . . . k′N. Expressing the data estimation error for each k-space location, k′1 . . . k′N, as a sample of the discrete space Fourier transform of the sensitivity estimation error weighted by the magnetization, as in Eq. 30, Eq. 25 can be written as the magnitude of the discrete space Fourier transform of the magnetization-weighted sensitivity estimation error, sampled at the low spatial frequency locations k′1 . . . k′N:
Since Eq. 25 is mathematically equivalent to Eq. 31, the weights that minimize Eq. 25 will also be the weights that minimize Eq. 31. Thus, the weights obtained using local projection calibration minimize the low frequency components of the magnetization-weighted sensitivity estimation error. Since the majority of the energy of the magnetization and sensitivity functions is contained in their low frequency components, local projection calibration will tend to find weights that successfully minimize the overall magnetization-weighted sensitivity estimation error.
By setting k=0 in Eq. 24, we see that the magnitude image of the sensitivity estimation error is equivalent to the magnitude image of the error in the estimated encoding vector used to synthesize the datum at location k:
|εj,P(k,0)(0)|=|εj,(k)(k)| (32)
Thus, the magnetization-weighted sensitivity estimation error is equal to the magnetization-weighted error in the estimated encoding vector,
∥εj,P(k,0)(0)·m∥=∥εj,(k)(k)·m∥ (33)
and minimizing the magnetization-weighted sensitivity estimation error is equivalent to minimizing the magnetization-weighted error in the estimated encoding vector.
Equation 31 also provides some insight into the choice of k′1 . . . k′N. By choosing k′1 . . . k′N to cover a full-FOV grid in the central region, we ensure that all of the low frequency components of the magnetization-weighted sensitivity estimation error are included in the minimization. Choosing the extent of the central region in k-space is still an inexact art, depending to some degree on the energy distribution in k-space of the magnetization and coil sensitivities. However, by choosing N to be much larger than the total number of elements in the local set, L(k), the error in the estimated encoding vector will be determined more by the ability of the local encoding vectors, EL(k), to synthesize the estimated encoding vector than by the effect the extent of the central region has on the calculation of the weight values.
In the above analysis of local projection calibration, it was shown that high quality local weights can be found without knowing the coil sensitivities, assuming all data values are known, or can be found, within the central region. While interpolation might be necessary when required points have not been acquired directly, when all of the acquired data falls on a uniform Cartesian grid, acquiring data at all grid locations in the central region is sufficient for local projection calibration. As shown in
Central Region Interpolation: When data is acquired using a k-space sampling pattern that does not fall on a uniform Cartesian grid, local projection calibration cannot be used directly since it requires data values from locations in the central region that have not been acquired, as shown in
Before engaging the details of central region interpolation, we summarize the overall APPEAR procedure. As illustrated in
w
j,
(k)
T(k)=[{tilde over (d)}j(k′1) . . . {tilde over (d)}j(k′N)[]{tilde over (d)}P(k,k′
which is identical to Eq. 26, except that the data values are synthesized. Using Eq. 34, APPEAR determines a separate set of weights for each grid point to be synthesized, repeating the procedure illustrated in
The success of the APPEAR method rests heavily on the ability to synthesize data values within the central region of a coil's k-space using only the data acquired on that receiver coil. Techniques, such as gridding, and BURS, for synthesizing data in new k-space locations from a set of full-FOV data are commonly used for reconstructing non-Cartesian k-space sampling patterns acquired using only one coil. However, these methods are designed to minimize errors in a particular FOV in image space, whereas the synthesis method used by APPEAR needs to minimize errors in the low spatial frequency region of k-space. This is a subtle but important difference. For example, when gridding is performed on an oversampled grid, errors can be pushed outside of the FOV of the object, separating the errors from the object. However, these errors cannot be separated from the data in such a clean fashion in k-space. We now develop the procedure that APPEAR uses to get high quality synthesized data suitable for use with the local projection calibration technique.
The single-coil data synthesis problem can be formulated in a similar way to the multi-coil formulation. However, in the single-coil case, the unknown coil sensitivity is grouped with the magnetization by letting ms denote the sensitivity-weighted magnetization, described in Table IV. Since only one coil is being considered at a time, the coil index j has been dropped. The analysis of the sensitivity-weighted magnetization is done by gradient encoding, such that d(k)=gT(k)ms. Similar to the multi-coil case, we can synthesize an encoding function from acquired encoding functions in the local k-space neighborhood, but in this case the encoding is solely accomplished by the gradients. The single coil version of Eq. 16 can be written as
{circumflex over (g)}C(k)T(k)=wj,C(k)T(k)GC(k), (35)
where C(k)is the k-space kernel set
C(k)={k+Δk|dj(k+Δk) was acquired, ∥Δk∥<k} (36)
and GC(k) is the gradient encoding matrix, each row of which contains a gradient encoding vector whose location is in the kernel set. The estimated gradient encoding vector is denoted by ĝC(k)(k) and the linear combination weights, wjC(k)(k), can be found as the weights that minimize the magnitude of the error in the estimated gradient encoding vector:
While such a procedure finds the optimal weights for estimating g(k) from its surrounding neighborhood, this estimate still is not of high enough accuracy when using a small number of points. To illustrate this, we use a one dimensional example, showing the accuracy that can be expected from this approach. Five different attempts are made to synthesize g(0), which equals unity, from four local gradient encoding vectors. In each of the five attempts, the four local gradient encoding vectors are shifted slightly in relation to g(0), as shown in
The accuracy of the data synthesis can be greatly increased using oversampling and apodization. While both of these concepts are used in gridding, they must be applied in a slightly different way in this case. Instead of depositing the data on an oversampling grid, in this case oversampling is accomplished by acquiring data in the central region more closely together (spaced for a larger FOV than the FOV of the object). Currently an oversampling ratio of 1.5 is used, which has been shown to give high accuracy in gridding. APPEAR takes advantage of apodization by finding the weights that synthesize an apodized version of the gradient encoding vector instead of the gradient encoding vector itself. By synthesizing all of the data in the central region using similarly apodized versions of the gradient encoding vectors, the resultant data, edj(k), is equivalent to data obtained by encoding an apodized version of the sensitivity weighted magnetization with the non-apodized gradient encoding vectors.
c) shows that using oversampling and apodization lead to a dramatic improvement in the fidelity of the synthesized data. In
In a case such as the example in
where ga(k)=a·g(k) for apodization vector a. This approach is attractive because it will find the optimal weights for any given kernel set, C(k), as long as it is sufficiently oversampled for the FOV.
When centralting to an apodized gradient encoding function, the synthesized data in the central region is equivalent to data that would be obtained with the same coil sensitivity but with a magnetization that is an apodized version of the actual magnetization. Thus, instead of the local projection calibration technique minimizing the encoding estimate error weighted by the magnetization, in the APPEAR case it minimizes the encoding estimate error weighted by an apodized version of the magnetization. Because of this, we must be careful not to allow too severe of an apodization, which can reduce the quality of the estimated encoding vector toward the edges of the FOV. In all experiments to date, the apodization function used (Fourier transform of a Kaiser-Bessel window) performs very well.
By slightly oversampling in the central region and synthesizing data that would be generated from an apodized version of the magnetization, instead of insisting on synthesis of data that would be acquired by the scanner, we are able to provide the local projection calibration technique with data values at any location in the central region, as required by the local projection calibration technique. In doing so, APPEAR gives a practical implementation of local projection calibration for arbitrary k-space trajectories.
Noise in the acquired data samples affects APPEAR in two ways. Firstly, noise in the central region can degrade the quality of the synthesis weights thereby increasing the error in the estimated encoding vector. Since errors in the estimated encoding vector can manifest themselves as structured image artifacts, it is important that the data acquired in the central region have a sufficiently high signal-to-noise ratio to ensure that the estimated encoding vector is accurate. Secondly, noise in the data samples leads to noise in the reconstructed image. Similar to other multi-coil reconstruction methods, the noise level varies from voxel to voxel. Since APPEAR is non-iterative, the noise level at each voxel can be calculated in a straightforward manner from the receiver noise matrix and the synthesis weights, as shown in the appendix.
Numerical simulations were performed in Matlab (MathWorks, Natick, Mass.) using a Shepp-Logan phantom and the same coil architecture used for the numerical simulations in [18]. The k-space trajectory used, shown in
Scanner experiments were performed on a 1.5T GE Excite scanner using two k-space trajectories: the trajectory used in the numerical simulations and a non-uniformly spaced trajectory. The non-uniformly spaced trajectory was identical to the uniformly spaced trajectory except that the 108 high spatial frequency phase-encodes were non-uniformly spaced, giving an acceleration ranging from 1 to 3.5, as seen in
The first experiment consisted of scanning an axial slice of a ball phantom using an 8-channel high-resolution knee coil (MRI Devices Corp., Waukesha, Wis.). In this experiment, the frequency encoding was done in the A/P direction. The second experiment consisted of scanning an axial slice of the brain of a healthy volunteer using an 8-channel high-resolution head coil (MRI Devices Corp., Waukesha Wis.). In this experiment, the frequency encoding was done in the R/L direction. A 5 mm slice was excited for both experiments. The ball was scanned with a TE/TR of 10/1000 and an 11 cm FOV. The brain was scanned with a TE/TR of 10/50 and a 22 cm FOV. The data was reconstructed using PARS, APPEAR and by separately gridding the data from each coil followed by a sum-of-squares combination.
The results for the numerical simulations are shown in (k)(k)|, directly for a set of linear combination weights. The error vector, ∈j,
(k)(k)|, can be viewed as an image, similar to the magnetization vector m. While the error vector images do not directly represent errors in the reconstructed images, they do indicate the accuracy with which unknown data values are synthesized. The error in a synthesized datum value can be found by multiplying the error vector image, corresponding to the encoding location of the synthesized datum, by the magnetization and summing the result. Thus, nonzero values ∈j,
(k)(k) | in where there is no magnetization are unimportant.
The results of the phantom experiment are shown in
As a method for reconstructing images from partially parallel encoded acquisitions, the APPEAR method performs well. In the experiments presented, APPEAR had significantly less artifact than the PARS method. The main difference between PARS and APPEAR is that APPEAR uses local projection calibration whereas PARS uses the self-calibration technique [6], in which data in the central region is used to estimate the coil sensitivities. Our results indicate that using the local projection calibration technique leads to less errors than using the self-calibration technique. To understand these results, it is instructive to look more closely at the self-calibration technique used by PARS. PARS finds the local weights that satisfy
which is similar to Eq. 17, except that EL(k) and ejT(k)| are replaced with EPARS,(k) and ePARSj T(k). For the PARS encoding functions, the coil sensitivities are replaced with low resolution images reconstructed by inverse Fourier transform from the central region data. These images can be written as [m(r)sj(r)]*psf(r), where psf(r) is the point-spread-function associated with the sampling pattern in the central region. The point-spread-function can be modified by applying different weightings to the k-space sampling locations. As described in [6], a Kaiser window weighting can be used to reduce the Gibbs ringing in the resultant image. Regardless of the weights chosen for the k-space sampling locations, convolution with psf(r) makes it difficult to extract sj(r). Self-calibration is built on the approximation that sj(r) is very low frequency and
[m(r)sj(r)]*psf(r)≈[m(r)*psf(r)]sj(r), (40)
which separates sj(r) from the other terms. Once sj(r) is separated in this way, coil-by-coil images can be reconstructed. The error in the approximation given in Eq. 40 tends to be largest near locations where m(r)sj(r) changes abruptly, such as at the edge of the object. These errors in turn lead to errors in the reconstruction matrix and estimated encoding functions. While local projection calibration limits itself to synthesizing estimated encoding functions from local neighborhoods, it is able to minimize Eq. 31 without approximation. Because of this, local projection calibration is not susceptible to errors due to the approximation in Eq. 40.
The autocalibration procedure used in Cartesian GRAPPA is a special case of local projection calibration, where the pattern sets needed to find the synthesis weights align perfectly with the acquired data in the central region. However, the extension of the autocalibration technique to radial and spiral trajectories, is not a case of local projection calibration. Autocalibration focuses on finding weights that optimize the central between raw unprocessed scanner-acquired data, but does not insist on the integrity of the pattern set. This is very different from local projection calibration which insists on the integrity of the pattern set, but accepts the use of synthesized (and apodized) data in finding the synthesis weights.
When used for non-Cartesian trajectories, autocalibration has the difficulty that it is hard to find acquired data in the necessary patterns in the central region. In dealing with this difficulty, techniques that use autocalibration are forced to make a number of compromises. They no longer central a pattern throughout the central region, often being limited to centralting in a small section of the central region. They disregard small differences between pattern sets. For example, two pattern sets that differ by a small rotation or dilation might be treated as the same pattern set. Finally, they might need to acquire extra calibration data and perform centrals in high spatial frequency locations that have a significantly lower signal level than the signal level in the central region. In
APPEAR provides a great deal of freedom in choosing the k-space trajectory used to acquire the data. While the two trajectories used in this paper have the same number of phase-encodes and hence same net acceleration, they have different properties. The variable acceleration trajectory has worse SNR compared to the uniform acceleration trajectory, but is more robust to errors in the estimated encoding vectors and less likely to produce images with visible aliasing artifacts.
The APPEAR method is unique in requiring an oversampled region in the center of k-space. For some trajectories, such as the radial trajectory, where oversampling is inherent, this might not be a disadvantage. However, for many trajectories, this will require special attention in the trajectory design.
In this paper, we have focused on describing the APPEAR method and providing some preliminary results. As the method matures, we are exploring the tradeoffs in the choice of parameters, such as the extent of the local neighborhood and central region. In addition, since APPEAR finds a unique set of weights in order to synthesize each unacquired datum, it is a computationally intensive method; we are currently exploring ways to efficiently implement the APPEAR method for reduced gradient encoding in two and three dimensions.
It should also be noted that the utility of the APPEAR method is to reduce the encoding time necessary to acquire an image and as such, should only be employed when the encoding time needs to be reduced. Many scans are not limited by encoding time, but by the time required to obtain sufficient SNR. In this case, the APPEAR method is of limited utility. Indeed, the first use of multiple receiving coils in MRI was to increase SNR, not to reduce encoding time.
Still, there are many cases when it is advantageous to reduce the encoding time. For these cases, the results presented here indicate that APPEAR will provide high quality reconstructions. APPEAR is simple to use, since no coil sensitivities are needed and APPEAR is non-iterative. This simplicity does not limit the flexibility of APPEAR, which is able to reconstruct arbitrary k-space trajectories.
In conclusion, the theory behind local projection calibration has been derived. This theory shows that local projection calibration will attain near optimal linear combination weights for data synthesis, even though the coil sensitivities are not known. This theory also shows that there is a fundamental difference between local projection calibration and techniques which estimate coil sensitivities from low spatial frequency data. The results of this paper indicate that local projection calibration produces reconstructions with less aliasing artifact than reconstructions which use estimated coil sensitivities from low spatial frequency data.
The APPEAR method has been introduced as a way to take advantage of local projection calibration with arbitrary k-space trajectories. APPEAR is a straightforward method that does not require iteration. Preliminary experiments confirm that APPEAR is able to produce high quality reconstructions.
Attached are tables which identify variables and the functions as used herein.
While the invention has been described with reference to specific embodiments, the description is illustrative of the invention and is not to be construed as limiting the invention. Various modifications and applications may occur to those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.