Electricity: measuring and testing – Particle precession resonance – Using a nuclear resonance spectrometer system
Reexamination Certificate
1999-12-27
2001-03-06
Oda, Christine (Department: 2862)
Electricity: measuring and testing
Particle precession resonance
Using a nuclear resonance spectrometer system
C324S307000
Reexamination Certificate
active
06198283
ABSTRACT:
BACKGROUND OF THE INVENTION
The present invention relates generally to the field of medical diagnostic systems, such as imaging systems. More particularly, the invention relates to a system and technique for phase sensitive MRI reconstruction using partial k-space data to minimize data acquisition time (TE) while preserving phase information and reducing edge blurring in the reconstructed image.
In magnetic resonance (MR) imaging, the scan time can be reduced by using a partial NEX, or alternatively, the echo time can be reduced by using a fractional echo. This moves the time to the echo peak closer to the start of the read-out gradient waveform than in a full echo. However, in all partial echo or half-Fourier reconstruction strategies, all phase information is lost. The present invention is a method and system for using the homodyne reconstruction algorithm to generate a complex-valued image from which phase information can be extracted.
In general, the synthesis of the missing k-space data assumes that the MR data is Hermitian for a real-valued image. That is:
F(−k
x
)=F*(k
x
) [11]
where the * denotes a complex conjugate. If the k-space is divided into 4 quadrants, the data for at least two of the four quadrants is needed in order to generate an image. Therefore, either a partial echo (partially filled k
x
) or partial NEX (partially filled k
y
) can be used, but not both.
The following background is a review of the prior art homodyne method. If ƒ(x) is the real-valued image and &phgr;(x) is the spatially varying phase in the image, the expression for the complex valued image can be written as:
I(x)=ƒ(x)exp(j&phgr;(x))=ƒ
L
(x)exp(j&phgr;
L
(x))+ƒ
H
(x) exp(j&phgr;
H
(x)). [2]
This expression is a linear combination of the Fourier transforms of the low-pass and high-pass filtered k-space data, respectively. In homodyne reconstruction, the phase is assumed to be slowly varying and that &phgr;
L
(x)≈&phgr;
H
(x). Therefore, if only one-half of the high-pass filtered data is available, this is equivalent to multiplying the high-pass filtered data by a Heaviside function such that the resulting image is given by:
I
H
(
x
)
=
f
L
(
x
)
exp
(
jφ
L
(
x
)
)
+
f
H
(
x
)
exp
(
jφ
L
(
x
)
)
⊗
1
2
(
δ
(
x
)
+
1
jπ
x
)
,
[
3
]
where {circle around (x)} denotes a convolution. Since the convolution term decays with 1/x and that the phase is slowly varying, Eqn. [3] can be rewritten as:
I
H
(
x
)
≈
(
f
L
(
x
)
1
2
f
H
(
x
)
-
j
2
f
H
(
x
)
⊗
1
π
x
)
exp
(
jφ
L
(
x
)
)
.
[
4
]
If the available high frequency data is weighted by 2, Eqn. [4] can be written as:
I
H
(
x
)
=
(
f
L
(
x
)
+
f
H
(
x
)
-
j
f
H
(
x
)
⊗
1
π
x
)
exp
(
jφ
L
(
x
)
)
.
[
5
]
If the spatially varying phase term is divided out, the image is then the real-valued part of I
H
(x) exp(−j&phgr;
L
(x)), i.e.:
ƒ
L
(x)+ƒ
H
(x)=ƒ(x)=Re(I
H
(x)exp(−j&phgr;
L
(x))), [6]
where the spatially varying phase is estimated from the phase of the Fourier transform of the low-pass filtered data. It is noted that in Eqn. [6], all phase information has now been lost. Note that Eqn. [6] could easily be written as ƒ(x)=Re(I
H
(x))e
−j&phgr;
L
(x)
where the phase in the image is the low spatial frequency phase. However, this phase is only an estimate and is of little use. Hence, this technique is not suitable for phase contrast reconstruction. Furthermore, the loss of phase information requires that the Fourier transform in the y direction be performed first, before the homodyne reconstruction is applied to the data in the x direction.
It would therefore be desirable to have a system and method capable of preserving magnitude and phase information in a partially acquired k-space data set that allows reduced data acquisition times and significantly improves edge blurring in the reconstructed MR image.
Solutions to the problems described above have not heretofore included significant remote capabilities. In particular, communication networks, such as, the Internet or private networks, have not been used to provide remote services to such medical diagnostic systems. The advantages of remote services, such as, remote monitoring, remote system control, immediate file access from remote locations, remote file storage and archiving, remote resource pooling, remote recording, remote diagnostics, and remote high speed computations have not heretofore been employed to solve the problems discussed above.
Thus, there is a need for a medical diagnostic system which provides for the advantages of remote services and addresses the problems discussed above. In particular, there is a need for providing the ability to remotely view images by an off-site expert. Further, there is a need for the ability to perform upgrades of correction factors and algorithms from a remote facility. Even further, there is a need to apply the correction factor during reconstruction, also via a remote facility.
SUMMARY OF THE INVENTION
One embodiment of the invention relates to a system to correct edge blurring in an image reconstructed with partial k-space data including a magnetic resonance imaging system, a computer, and a network. The magnetic resonance imaging system has a plurality of gradient coils positioned about a bore of a magnet to impress a polarizing magnetic field and an RF transceiver system and an RF modulator controlled by a pulse control module to transmit RF signals to an RF coil assembly to acquire MR image. The computer is programmed to: acquire a partial k-space data set having both magnitude and phase information; filter the partial k-space data set through high and low-pass filters and a linear combination of both the high and low-pass filters; Fourier transform the filtered data set; estimate a blurring correction factor representative of a convolution error term from a portion of the filtered data set; and apply the blurring correction factor to the filtered data set to remove the convolution error term and reconstruct an MRI preserving both magnitude and phase information while minimizing edge blurring in the reconstructed MRI. The network is coupled to any one of the magnetic resonance imaging system and the computer. The network provides communication with a remote facility for remote services.
Another embodiment of the invention relates to in a magnetic resonance imaging system, a method of phase sensitive magnetic resonance image (MRI) reconstruction using partial k-space data. The method includes acquiring a partial k-space data set having both imaginary and real components containing both magnitude and phase information; communicating at least the partial k-space data set to a remote facility to provide remote services; filtering the partial k-space data set through high and low-pass filters and a linear construction of both; Fourier transforming the filtered data set; estimating a blurring correction factor representative of a convolution error term from a portion of the filtered data set; applying the blurring correction factor to the filtered data set to remove the convolution error term; and reconstructing an MRI having both magnitude and phase information thereby minimizing edge blurring in the reconstructed MRI.
Another embodiment of the invention relates to a system for minimizing edge blurring in a reconstructed magnetic resonance image (MRI) using partial k-space data. The system includes means for acquiring partial k-space data containing both magnitude and phase components; means for communicating the partial k-space data to a remote facility to provide remote services; means for partially calculating a homodyne recon
Foo Thomas K. F.
Polzin Jason A.
Cabou Christian G.
Cook & Franke S.C.
Fetzner Tiffany A.
GE Medical Systems Global Technology LLC
Oda Christine
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