Electricity: measuring and testing – Particle precession resonance – Using a nuclear resonance spectrometer system
Reexamination Certificate
2000-12-11
2003-04-29
Lefkowitz, Edward (Department: 2862)
Electricity: measuring and testing
Particle precession resonance
Using a nuclear resonance spectrometer system
C324S307000, C324S318000, C324S322000, C382S260000
Reexamination Certificate
active
06556009
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates generally to Magnetic Resonance Imaging (MRI), and more particularly relates to acceleration of MRI using undersampled k-space data.
BACKGROUND
Magnetic Resonance Imaging is based on the absorption and emission of energy in the radio frequency range. To obtain the necessary MR images, a patient (or other target) is placed in a magnetic resonance scanner. The scanner provides a uniform magnetic field that causes individual magnetic moments of spins in the patient or target to align with the magnetic field. The scanner also includes multiple coils that apply a transverse magnetic field. RF pulses are applied to the coils that cause the aligned moments to be rotated or tipped. In response to the RF pulses, a signal is emitted by the excited spins that is detected by receiver coils.
The resulting data obtained by the receiver coils corresponds to the spatial frequency domain and is called k-space data. The k-space data includes multiple lines called phase encodes or echoes. Each line is digitized by collecting a number of samples (e.g., 128-256). A set of k-space data is acquired for each image frame, and each k-space data set is converted to an image by passing the data through a fast Fourier transform (FFT)
FIG. 1A
shows an example of a full k-space data set with all of the phase encodes (1, 2, 3 . . . N) acquired.
In several applications of MRI, a time series or sequence of images are obtained in order to resolve temporal variations experienced by the imaged object. For example, in cardiac imaging it is desirable to obtain a sequence of images to study the dynamic aspects of the heart. Ideally, when objects are imaged, all of the spatial information is obtained as in FIG.
1
A. Because this imaging process is slow, methods have been developed that use only part of the k-space data, while the remainder of the data is approximated by interpolation or other means. There are numerous types of such “undersampling” techniques that can be used that acquire only part of the k-space data. For example, the k-space data acquisition may obtain only every other line (e.g., even or odd lines), or fewer lines may be obtained near the boundaries of k-space, while additional lines are obtained around the center.
FIG. 1B
shows an example of an undersampled k-space data set with only the odd phase encodes acquired. Accelerated imaging refers to fewer samples of k-space are required to reconstruct an image with the same field-of-view and effective spatial resolution.
Typically, when undersampled k-space data is converted to image space the resulting images have aliasing defects called artifacts or ghost artifacts. There are several filtering techniques to reduce the affects of ghost artifacts. One such scheme is called UNFOLD (Madore et al.,
Unaliasing by Fourier
-
Encoding the Overlaps Using the Temporal Dimension
[UNFOLD],
Applied to Cardiac Imaging and fMRI
. Magn Reson Med. 1999 Nov; 42(5):813-28.) UNFOLD acquires half of the k-space lines to accelerate image acquisition. On the first data set of the sequence, UNFOLD acquires odd lines of k-space. On the next data set of the sequence, UNFOLD acquires the even lines of k-space. Such an acquisition process is called a time-varying, alternating acquisition. The aliasing defects resulting from the even and odd k-space acquisition are of opposite sign and are almost cancelled out when combined appropriately, depending on the degree and type of object motion.
Another technique for accelerating MR acquisition is called partially parallel acquisition. Methods in this category are SENSE (Pruessmann et al., SENSE:
Sensitivity Encoding for Fast MRL
. Magn Reson Med. 1999 Nov; 42(5): 952-962.) and SMASH (Sodickson D K, Manning W.
Simultaneous acquisition of spatial harmonics
(SMASH):
fast imaging with radiofrequency coil arrays
. Magn Reson Med 1997; 38:591-603). SENSE and SMASH use undersampled k-space data acquisition obtained from multiple coils in parallel. In these methods, the complex data from multiple coils are combined with complex weightings in such a way as to suppress undersampling artifacts in the final reconstructed image. This type of complex array combining is referred to as spatial filtering, and includes combining which is performed in the k-space domain (as in SMASH) or in the image domain (as in SENSE), as well as methods which are hybrids. Rather than alternating acquisition between even and odd lines as in UNFOLD, SENSE and SMASH use either even lines for all the data sets or odd lines for all the data sets. In either SENSE or SMASH, it is important to know the proper weightings or sensitivities of the coils. Coil sensitivities change based on proximity to the target. Additionally, if the target moves, the coil sensitivities vary. To obtain the coil sensitivities, a calibration run is typically performed prior to and/or after the imaging. It is assumed that the sensitivities of the coils remain static. However, in practice, if the patient moves during the course of the exam, the coil sensitivities change. Even slight motion, such as breathing, can cause sufficient motion to compromise the estimated coil sensitivities.
FIGS. 2A and 2B
show high-level diagrams of both the UNFOLD and SENSE filtering techniques.
FIG. 2A
shows that with the UNFOLD technique, alternating k-space data is converted to the image domain by a fast Fourier transform. After the conversion to the image domain, the images contain ghost artifacts and are only one-half field-of-view. The half field-of-view data is passed through the UNFOLD filter to obtain a full field-of-view image with the ghost artifacts suppressed. The k-space data may be supplied by multiple receiver coils as indicated by the multiple input lines to the fast Fourier transform. For each receiver coil, the UNFOLD filter produces corresponding output image data.
FIG. 2B
shows a similar technique using a SENSE filter. In this technique, even k-space data, acquired from multiple coils, is passed through a fast Fourier transform to obtain a half field-of-view image with ghost artifacts. The half field-of-view image is passed through the SENSE filter to obtain a full field-of-view image with the ghost artifacts suppressed.
In either imaging technique of
FIGS. 2A and 2B
, the filter converts a one-half field-of-view image to a full field-of-view image with ghost artifacts suppressed.
The above mentioned filtering techniques are effective, but the resulting images may still contain residual ghost artifacts that can obscure part of the image. It is desirable to accelerate image acquisition while further suppressing any residual artifacts.
SUMMARY OF THE INVENTION
The present invention relates to a method and apparatus for accelerating MR imaging. Undersampled k-space data is used to achieve an R-fold acceleration. Ghost artifacts that result from undersampling are suppressed by means of combined temporal and spatial filtering. Additionally, the spatial filter coefficients may be adaptively or dynamically computed while imaging so that patient movement or other variations in the sensor coil sensitivity profiles does not unduly affect the image reconstruction.
In one aspect, temporal and spatial filters are combined in series. In this context, spatial filtering refers to the weighted sum of multi-coil data (either in k-space or image space). Undersampled spatial spectral data from multiple coils is converted to image domain data using one of any variety of image reconstruction techniques, such as fast Fourier transforms. The data is then passed through a series combination of temporal and spatial filters. The image reconstruction, temporal filtering, and spatial filtering are linear operations. Consequently, these operations can be performed in virtually any order. Additionally, a wide variety of temporal and spatial filters can be used. UNFOLD and SENSE are examples of such temporal and spatial filters.
In another aspect, the spatial filter coefficients are adaptively or dynamically computed from time-varying, undersampled data
Kellman Peter
McVeigh Elliot
Klarquist & Sparkman, LLP
Lefkowitz Edward
The United States of America as represented by the Department of
Vargas Dixomara
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