Patent Application
20090093709
The present disclosure is directed to noise reduction in magnetic resonance (MR) imaging, and more particularly to reduction of acceleration-related noise in parallel MR.
Magnetic resonance imaging (MRI) is a technique used frequently in medical settings to produce images of the inside of the human body. MRI is based on detecting nuclear magnetic resonance (NMR) signals, which are electromagnetic waves emitted by atomic nuclei in response to state changes resulting from applied electromagnetic fields. In particular, magnetic resonance techniques involve detecting NMR signals produced upon the re-alignment or relaxation of the nuclear spins of atoms in the tissue of the human body. MR techniques may be used to image and study the properties of tissue in a variety of regions of the human body, for example, for detection and/or diagnosis of tissue anomalies, study of blood flow, etc. The applied electromagnetic fields and detection of the resulting MR signals may be performed using a transmit and receiver radio frequency (RF) coil.
In the past, MRI data was obtained using a single transmit/receiver coil. As a result of having only a single MR detector, only one line of data could be acquired at a time. Recently, techniques for using multiple transmit/receiver coils have been developed that facilitate acquiring multiple lines of data simultaneously. MR techniques using multiple transmit and/or receive coils to image a subject is referred to herein as parallel MR. Parallel MR takes advantage of the spatial information available using an appropriately arranged array of RF coils. In parallel MR, some number of the RF coils in an array may be independently excited (e.g., RF power may be transmitted over independent channels to multiple respective RF coils) and/or independently measured (e.g., measurements may be obtained/received from multiple RF coils over respective independent channels).
Parallel MR has circumvented previous limits on speed and efficiency, effecting a reduction in image acquisition times due, in part, to the fact that multiple lines of data may be obtained simultaneously. As a result, parallel MR has become an integral technology in modern MRI clinical and research scanners. However, an important trade-off exists in conventional parallel MR. Specifically, the acceleration of data acquisition in parallel MR results in a corresponding decrease in signal-to-noise ratio (SNR). In general, n receiver coils may be capable of achieving an n-fold reduction in scan time. The amplification of noise in parallel image reconstruction, however, also increases with the reduction in scan time. The term “acceleration-related noise” refers herein to noise associated with accelerated data acquisition and reconstruction in parallel MR (also referred to as geometry factor, or g-factor, noise in the literature).
Acceleration-related noise results, in part, from overlap or crosstalk between the various coils in a multiple coil array. That is, acceleration-related noise is produced when the coil array does not provide sufficiently independent data. Conventional attempts to reduce acceleration-related noise have generally been unsatisfactory for clinical parallel MRI. For example, one approach to reducing acceleration-related noise includes approximating the acceleration-related noise using an exhaustive search method, and subtracting the approximated noise from the reconstructed pixel values (Larkman et al., “Beyond the g-factor limit in sensitivity encoding using joint histogram entropy,” Magnetic Resonance in Medicine, 55(1): 153-160, 2005). However, such a search may take hours to perform (e.g., up to 18 hours for an exhaustive search), which substantially negates the benefits of using parallel MR in the first place (e.g., relatively long computation times counteract the reduction in scan time achievable using parallel MR).
Some embodiments include a method for noise reduction in parallel MR imaging using a plurality of RF coils, the method comprising, performing a first MR scan of a target to obtain first MR data, performing a second MR scan of the target to obtain second MR data, the second MR data obtained from operating the plurality of coils substantially in parallel, computing a noise estimate associated with the second MR data based at least in part on the first and second MR data, and obtaining a noise-reduced image based at least in part on the second MR data and the noise estimate.
Some embodiments include a computer storage device encoded with a program for execution on at least one processor, the program when executed performs a method for noise reduction in parallel MR imaging, the method comprising acts of receiving first MR data, receiving second MR data obtained from operating a plurality of RF coils substantially in parallel, computing a noise estimate associated with the second MR data based at least in part on the first and second MR data, and computing a noise-reduced image based at least in part on the second MR data and the noise estimate.
Some embodiments include a system for performing parallel MR imaging comprising a scanning apparatus comprising a plurality of RF coils, the scanning apparatus configured to perform accelerated image acquisition by operating the plurality of coils substantially in parallel to obtain parallel MR data, and a signal processing device programmatically coupled with the scanning apparatus to obtain the parallel MR data from the scanning apparatus, the signal processing device configured to reduce acceleration-related noise in the parallel MR data by receiving reference MR data, computing a noise estimate associated with the parallel MR data based at least in part on the parallel MR data and the reference MR data, and generating at least one noise-reduced MR image based at least in part on the accelerated MR data and the noise estimate.
As discussed above, conventional methods of reducing the effects of acceleration-related noise may be unsatisfactory in that they counteract the benefits of performing parallel MR. That is, the acquisition speed-ups may be nullified by the computational expense incurred in approximating the acceleration-related noise. Applicant has appreciated that a computationally inexpensive algorithm (e.g., an algorithm that can be performed in real-time during a scan) for reducing acceleration-related noise in parallel MR would facilitate the adoption of noise reduction algorithms in clinical settings by reducing the wait time for a clinician to have the data and begin making a diagnosis, reducing the time required for patients to be in the MR scanner (during which time a patient may move appreciably, and/or may become claustrophobic).
Various aspects of the invention derive from Applicant's recognition that a relatively low resolution reference image may be used to approximate a noiseless image of a target being scanned at accelerated acquisition rates. This approximation of a noiseless image may then be used to estimate acceleration-related noise without having to perform an exhaustive and/or computationally expensive search. The estimated acceleration-related noise may then be subtracted from the reconstructed pixel values to reduce the effects of acceleration-related noise. According to some embodiments, the noise reduction process described above may be performed in real-time or substantially in real-time. According to some embodiments, the noise reduction process described above may be performed on the order of one to ten seconds.
Concepts related to methods and apparatus according to the invention, and various embodiments thereof, are described below in greater detail. It should be appreciated that various aspects of the invention described herein may be implemented in any of numerous ways, and examples of specific implementations are provided herein for illustrative purposes only. For instance, while the exemplary embodiments presented below are based on Cartesian-type sampling, concepts and techniques of the invention may be used in conjunction with other sampling trajectories, such as radial and spiral trajectories.
Furthermore, aspects of the invention may be used in conjunction with both Fourier-based and non-Fourier-based methods for parallel MR. In a Fourier-based method, raw NMR data are stored in a data matrix called the k-space matrix, from which an image of the scanned target is generated by Fourier transformation. Essentially, Fourier transformation converts frequency information associated with NMR emissions detected by the RF coils to spatial information representing the distribution of nuclear spins in the scanned target. One class of Fourier based methods includes applying a reconstruction algorithm after Fourier transformation has been applied to the raw NMR data. Sensitivity encoding (SENSE) is an example of reconstruction in the image space.
According to the SENSE parallel imaging method and its variants, an aliased image (that is, a K-fold reduced image, where K is the acceleration/reduction factor) is obtained by Fourier transformation on the k-space. Each pixel in this aliased image is “unfolded” into K pixels that are equidistantly distributed in the desired full image. The process of unfolding involves a so-called sensitivity matrix (also know as a reconstruction matrix), which captures spatial sensitivities of the RF coils according to the particular geometric configuration at which scanning is performed. A sensitivity matrix may be calculated theoretically based on coil geometry, or derived by a calibration protocol carried out on a phantom image or on a pre-scan at the time of in vivo imaging. Methods for building a sensitivity matrix are well known in the art of parallel MR (for instance, in Pruessmann et al., “SENSE: Sensitivity Encoding for Fast MRI,” Magnetic Resonance in Medicine, 42(5): 952-962, 1999).
The SENSE reconstruction computation may be summarized as:
|X=C−1|A
(1)
where |X is a complex vector representing the pixel intensities of the unfolded image, C is the sensitivity matrix, and |A
is a complex vector representing the pixel intensities of the folded image.
As discussed above, accelerated data acquisition and the corresponding image reconstruction process in parallel MR leads to acceleration-related noise in the reconstructed image. Thus, the vector |A may be expressed in terms of a noiseless image with the addition of acceleration-related noise:
|A=|A′
+|dA
(2),
where |A′ represents true pixel intensities in the folded image and |dA
represents the noise contribution. Similarly, the vector |X
may be expressed in terms of a noiseless image with the addition of acceleration-related noise:
|X=|X′
+|dX
(3),
where |X′ represents true pixel intensities in the unfolded image and |dX
represents the noise contribution. The noise contribution |dX
may correspond to undesirable artifact in the unfolded image, which may negatively impact the applicability of parallel MR in clinical settings. There is a need to identify or approximate the noise contribution |dX
and to remove |dX
from |X
, so as to obtain a good approximation of the noiseless image |X′
.
Substituting equations 2 and 3 into equation 1 yields:
|X+|dX
=C−1(|A′
+|dA
) (4).
Applicant has recognized that, due to the linear nature of the matrix operations involved, equation 4 can be rewritten as:
|dX=C−1|dA
(5).
In other words, |dX, which represents noise in the unfolded image, can be viewed as the result of unfolding the noise contribution |dA
in the folded image.
Recall that the sensitivity matrix C is based on the configuration and geometry of the particular coil array from which the NMR data is acquired. Using singular value decomposition (SVD), the sensitivity matrix C may be factorized into principal components, yielding,
C=USV
+ (6).
Accordingly, the inverse of the sensitivity matrix may be written as,
C
−1
=VS
−1
U
+=Σk|vksk−1
uk| (7),
where k runs from 1 to the acceleration factor K (for example, K is the number of RF coils in the coil array), |vk is the k-th column vector of the matrix V,
uk| is the k-th row vector of the matrix U+, sk is the k-th diagonal element of the diagonal matrix S. Performing appropriate substitutions in equations 3, 5 and 7 yields,
|X′=|X
−Σk|vk
sk1
uk|dA
(8).
Equation 8 expresses the noiseless image |X in terms of the decomposed sensitivity matrix and the noise contribution |dA
in the folded image. Equation 8 can be rewritten as,
|X′=|X
−Σk|vk
sk−1δAk (9),
where δAk is an unknown scalar value defined by the inner product of uk| with the unknown noise contribution vector |dA
.
It has been appreciated that at high accelerations (i.e., high values of K), the sensitivity matrix C may become ill-conditioned. As a result, the inverse of the sensitivity matrix (i.e., C−1) is dominated by the first few singular values and the associated singular vectors. For instance, at high acceleration, an ill-conditioned sensitivity matrix may be dominated by the most significant singular vector (i.e., the dominant eigenvector). As a result, the sensitivity matrix may be adequately described using the first singular value and the associated singular vector. Using this insight, equation 9 may be rewritten as,
|X′≈|X
−|v1
s1−1δA1 (10).
Note that equation 10 includes two unknowns, the noiseless image |X′ and the scalar value δA1, and therefore cannot be solved directly. It has been proposed that a search may be conducted to determine the scalar value δA1. More specifically, a search algorithm may exhaustively test every value in a search domain and return a value for δA1 that minimizes a selected metric, for instance, the joint entropy between the reconstructed image and a reference image (Larkman et al., “Beyond the g-factor limit in sensitivity encoding using joint histogram entropy,” Magnetic Resonance in Medicine, 55(1): 153-160, 2005). However, as discussed above, such searches are computationally expensive and tend to nullify any reductions in scan time achieved by using parallel MR.
Applicant has appreciated that, instead of performing a computationally expensive search, a low resolution reference image may be suitable as an approximation of the noiseless image |X′ for purposes of computing δA1. Examples of potentially suitable reference images include low resolution images that are routinely obtained in MRI to calibrate the scanner and/or to tune the RF coils. Thus estimated, δA1 may then be substituted into equation 10 to compute an approximation of the noiseless image |X
.
More specifically, using equations 3 and 10, the image noise vector |dx can be expressed as,
|dX≈|v1
s1−1δA1 (11).
Separately, a relatively low resolution reference image |Xref may “stand-in” for the noiseless image |X
in equation 3, yielding,
|dX≈|X
−|Xref
(12).
Combining equations 11 and 12 yields,
|X−|Xref
≈|v1
s1−1δA1 (13).
Equation 13 has only one unknown, namely δA1, therefore a solution may be found algebraically. In some embodiments, equation 13 is solved using the Moore-Penrose least squares solution, thereby obtaining an estimate for δA1. For example, the single dominant value δA1 may be determined by,
δA1≈s1(v1|v1
)−1
v1|(|X
−|Xref
) (14).
The determined value for δA1 may then be substituted back into equation 10 to compute an approximation of the noiseless image |X′. As a result, an approximation for a noiseless image based on data obtained using parallel MR techniques (e.g., at a K-fold speed-up) may be directly computed without performing extensive or exhaustive searches. The term “directly computed” refers herein to determining the object of the computation using analytic methods that compute a single result for the object being computed. Solving for a variable in an expression wherein the resulting value of the variable is solved for once is an example of directly computing the value or result. Thus results obtained using iterative optimization schemes and other searching techniques that iteratively compute an estimate for the object being computed to find a result that minimizes or maximizes some metric or expression are not “directly computed” as used herein.
From a computational standpoint, searches may be more expensive relative to solving algebraic equations using, for example, least squares techniques. Thus, the speed benefits of parallel MR may be exploited without nullifying the acceleration with costly noise compensation algorithms. It should be appreciated that the sensitivity matrix C may be approximated using multiple singular values and the associated singular vectors, rather than just the first singular value and the associated singular vector. For instance, equation 9 may be rewritten alternatively as,
|X≈|X
−|v1
s1−1δA1−|v2
s2−1δA2−|v3
s3−1δA3 (15).
The procedure described above for estimating δS1 can be generalized to a procedure for simultaneously estimating multiple scalar values, such as δA1, δA2, and δA3 in equation 15 above. This may be achieved by replacing |v in the above procedure with a matrix containing a larger subset of the columns of V. In the case of equation 15, a matrix containing the first three columns of V may be used.
Accordingly, based on the Applicant's insights described above, a relatively low resolution reference image may be used to approximate acceleration-related noise, which can in turn be used to improve the quality of an image obtained using any one of various parallel MR techniques.
In act 110, MR data is acquired at relatively low resolution. The MR data may be low resolution relative to the resolution of the MR data obtained to form the intended image of the target, for example, to form a clinical or diagnostic image of the target. Relatively low resolution MR data is routinely obtained during MRI procedures to calibrate the MR scanner (e.g., to calibrate the transmit/receive coils or otherwise tune the components of the scanner). This relatively low resolution MR data may then be reconstructed to form a reference image (act 120). As discussed above, reference images are often obtained for other purposes (e.g., calibration and/or tuning) and therefore may not increase the overall scan time of a typical MRI procedure, which often must be re-calibrated and/or retuned for each object that is scanned (e.g., because each object such as an individual human patient operates as a different load on the coil array).
In act 130, accelerated acquisition MR data is obtained from a coil array having multiple RF coils. For example, the coil array may include K coils and the MR data may be obtained at an K-fold reduction in acquisition time with respect to single coil acquisition. However, any amount of acceleration over single coil acquisition may be achieved, as the aspects of the invention are not limited in this respect. In general, the accelerated acquisition MR data is at a resolution appropriate for clinical use, such as for use in the detection of anomalous tissue, diagnostics, FMRI analysis, etc., although this is not a limitation on the aspects of the invention. The accelerated acquisition MR data may then be reconstructed into a target image (act 140). For example, the accelerated acquisition MR data may be reconstructed using the SENSE technique, or other appropriate parallel MR reconstruction techniques.
As discussed above, MR images reconstructed from accelerated acquisition MR data may be corrupted, at least somewhat, by acceleration-related noise. That is, the acceleration-related noise in parallel MR data may be reconstructed into image artifacts. In act 150, the acceleration-related noise is estimated based, at least in part, on the reference image and the target image. In some embodiments, the target image is reconstructed using a SENSE technique for parallel MR. In particular, the target image may be reconstructed using a sensitivity matrix associated with the coil array. This sensitivity matrix may be employed to facilitate estimating acceleration-related noise.
For example, the sensitivity matrix may be decomposed using one or more SVD techniques. The decomposed sensitivity matrix may then be used in connection with the target image and the reference image to estimate the acceleration-related noise, for example, as described in the derivation of equations provided in the foregoing. However, the reference image may be used in other ways in connection with the target image to estimate acceleration-related noise, as the aspects of the invention are not limited in this respect. In particular, Applicant's insight that the reference image may be used as an approximation of an image free of the effects of acceleration-related noise may be used in other ways to estimate the acceleration-related noise of the acquisition process.
In act 160, the estimated acceleration-related noise is used to correct the target image. For example, the estimated acceleration-related noise may be used to approximate artifacts in the target image resulting from the acceleration-related noise. There are numerous ways in which the acceleration-related noise may be used to approximate the resulting image artifacts. In some embodiments, the estimated acceleration-related noise may be used as an initial guess in a search for an optimally estimated acceleration-related noise. This estimated noise may then be subtracted from the target image.
In some embodiments, the computation cost of performing acts 150 and 160 may be insignificant relative to the acquisition time. That is, the speed-up in scan time achieved using parallel MR may not be substantially affected by the noise reduction computations. As such, noise introduced as a result of accelerated scan times using parallel MR may be, to some extent, compensated for without adding significant computation time that tends to negate parallel MR speed-ups using conventional techniques. In some embodiments, acts 150 and 160 may be performed substantially in real-time with little or no appreciable impact on the total time for image acquisition (e.g., noise compensation may be performed in a matter of seconds). However, noise compensation computations may be performed in any time frame, as the aspects of the invention are not limited in this respect.
It should be appreciated that the concepts and techniques described above may be used in conjunction with other methods for parallel MR, such as those that perform reconstructions in the k-space and those based on non-Fourier spatial distributions.
Furthermore, the various aspects of the invention described in the exemplary embodiments may be used alone or in any combination, and are not limited to the combinations explicitly described herein. Various techniques described herein may be used in connection with any MRI scanner and device that performs parallel MR acquisition, as the aspects of the invention are not limited for use with a particular MR device or apparatus.
Several experiments, both in vivo and on phantom objects, have been performed to study the effectiveness of noise reduction according to some embodiments of the invention. Hereinafter, various embodiments of the invention are referred to generically as Least Squares Noise Reduction (LSNR) algorithms/methods/techniques. It should be understood that the use of the generic term “LSNR” does not limit aspects of the invention to the details provided in the following description. It should also be understood that the invention is not limited to the particular software (e.g., software packages for data processing) and hardware (e.g. scanning equipments and computers) used in the experiments described below.
Various acceleration schemes are explored in these experiments and the performances are investigated for high numbers of RF coil elements. For the in vivo experiments, low resolution images obtained during scanning are used as reference images in the LSNR algorithms. These studies verify that the LSNR method does not replicate the reference image. The studies also verify that small lesions which are conspicuous in a high resolution image but not discernible in a lower resolution image are preserved after the application of an LSNR algorithm in which the lower resolution image is used as a reference image.
In some experiments, in vivo images were obtained by scanning brain tissues in human volunteers. Images were acquired on a 1.5-T GE Excite HD-x scanner (GE Healthcare, Waukesha, Wis.). Two-dimensional (2-D) axial images of the brain were acquired using a head array coil with 8 elements circumferentially distributed. Brain images were acquired with T1-weighted (T1-w) contrast by a spin echo sequence with a single signal average and with T2-weighted (T2-w) contrast by a fast spin echo sequence with an echo train length of 8 and with 2 signal averages. Acquisition parameters include: 22-cm field of view (FOV), 5-mm slice thickness, 256×256 image matrix, ±15.63-kHz acquisition bandwidth, repetition/echo time (TR/TE) for T1-w of 500/20 ms and for T2-w of 2500/85 ms. Images were acquired twice to allow a reference image to be obtained with independent noise content.
In some embodiments, low-resolution reference images were obtained from the center of the acquired k-space data and filtered with a Kaiser-Bessel window (β=2). Coil sensitivities for parallel imaging were obtained from the center of the image k-space in the manner of self-calibrated parallel imaging with the center of k-space filtered with the same Kaiser-Bessel window. Images were acquired fully sampled and later decimated with various acceleration factors. Brain images were maximally undersampled, with acceleration factor K equal to the number of coils n. Both undersampling in one direction and in two dimensions were investigated. In some experiments, the frequency encoded direction is undersampled after acquisition mimicking the case of a section from a volumetric image acquisition, for example, undersampling with K=Ky×Kz equal to 4×2 and 2×4. Discrete lesions were simulated by setting the pixel intensities of 4 small regions to unity in a single 2-D axial SENSE reconstructed image. In addition, some experiments explored the performance of the LSNR method with sub-maximal acceleration (K<n) with K=4. In some experiments, similar methods were performed for phantom studies, where a body array with 32 RF coil elements distributed as 4×4 anterior and posterior grids was used on a manufacturer's standard physiological phantom.
Images were reconstructed using the SENSE algorithm as described herein. Regularly undersampled data were reconstructed into aliased (i.e., folded) images by fast Fourier transform (FFT). Unfolding was then be achieved using the inverse of the sensitivity matrix found by SVD for groups of aliased pixels, as shown in equation 9. SENSE image reconstruction may be implemented both with and without coil sensitivity images mask, which may be constructed, for example, by determining the set of pixels that are less than 5% of the maximum reference image pixel intensity and by setting corresponding indices of the coil sensitivities to zero. G-Factor noise amplification may be reduced by eliminating aliased pixel positions from the reconstruction which are known to contribute no signal intensity from the scanned object.
In some embodiments, an LSNR algorithm was implemented on a pixel-by-pixel basis. The sensitivity matrix formed from the known coil sensitivities may be decomposed via SVD. The principal singular value and vector may then be used to find δA1 with the vector of unfolded image pixels and the corresponding vector of pixels in the reference image. Multiple singular vectors could be used by finding noise-reduction vectors for the singular vectors corresponding to the multiple scaling parameters δAk, as indicated in equation 9.
For the original SENSE images, g-factor may be calculated according to well-known methods. For the LSNR method, noise reduction was quantified using a pseudo multiple replica method, in which many image replicas (e.g., 128) are reconstructed, each with artificial noise added to the acquired k-space. Image artifact power (AP) may also be calculated to quantitatively assess image quality. Artifact power is defined based on the differences of squared pixel values between the image of interest and an unaccelerated gold standard. More precisely,
AP is a measure of the amount of noise and artifact in an image, and it does not distinguish between these two types of image corruptions.
Associated with noise reduction is a potential increase in residual artifact. To ensure that artifact is not introduced without significant reduction in image noise, the artifact power for the LSNR image is calculated for maximal and submaximal accelerated in vivo reconstructions where a limit is placed on the condition number of the sensitivity matrix. Only groups of pixels for which the condition number of the sensitivity matrix is above a given threshold are operated on using the LSNR algorithm. Thus, by setting the threshold to a very large value, no pixels undergo noise reduction and the conventional SENSE image is produced; conversely, by setting the threshold to zero, all image pixels undergo noise reduction.
A major source of residual artifact in brain images is the pericalvarial tissue (PCT) which produces characteristic edge artifacts across the image. A scheme may be implemented to eliminate these artifacts from LSNR images by segmenting and removing the PCT from the SENSE reconstructed image before the LSNR algorithm is applied. This may be achieved by: automatically identifying the brain tissue without the PCT on the masked SENSE images, performing a LSNR algorithm on the remaining image, and then reintroducing the PCT to this reconstructed image.
Specifically, after performing the SENSE reconstruction with the masking technique described above, the ‘imextendedmin’ function in the Matlab Image Processing toolbox can be used to perform an extended minima transform on the masked SENSE image using a connectivity of 8 points to create a binary image differentiating the image (PCT and brain tissue) from non-tissue (image null space). The ‘bwlabel’ function with a pixel connectivity of 4 may then be applied to the binary image in order to classify the image structures based on their spatial orientation and connectivity. This function exploits the fact that the PCT in the image is surrounded on its exterior and interior by an area of low intensity that has greater than 4 point connectivity. The ‘bwlabel’ function results in an image where each connected part of the image is assigned a common integer value. By excluding the image null space outside of the image of interest, the brain tissue without the PCT may then be isolated before performing the LSNR algorithm on it, resulting in an LSNR image with only the brain tissue. Finally, using simple image addition, the PCT from the masked SENSE image may be reintroduced to this brain tissue LSNR image, resulting in a LSNR hybrid image. Using this method, the extent of PCT-related edge artifact in LSNR images can be determined.
Comparison of the appearances of noise-reduced images may be performed between images from the LSNR approach and from the joint entropy (JE) minimization search to assess the effectiveness of techniques disclosed herein and conventional exhaustive search techniques. JE reconstructions may be performed from coil sensitivity masked SENSE reconstructed images, with the same low-resolution reference image used in LSNR reconstructions. It should be noted that the JE method only converges to meaningful minima if the input SENSE and reference images are masked by coil sensitivity.
In comparison experiments, image reconstruction and processing was performed on a standard PC laptop computer with a 1.7 GHz processor and 2 GB RAM using the Matlab programming environment. In the noise reduction experiments, the LSNR algorithm took approximately 30 seconds to execute. By contrast, the corresponding JE minimization procedures took approximately 95 hours to complete. Thus, compared to JE minimization, LSNR is approximately four orders of magnitude faster in producing noise-reduced images.
To demonstrate the results quantitatively, AP and g-factor values are calculated for several experiments and presented in the table in
LSNR techniques were also studied in experiments involving submaximally accelerated reconstructions.
A similar result is demonstrated in
AP and g-factor values associated with
In
The above-described embodiments of the invention can be implemented in any of numerous ways. For example, the embodiments may be implemented using hardware, software or a combination thereof. When implemented in software, the software code can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers. It should be appreciated that any component or collection of components that perform the functions described above can be generically considered as one or more controllers that control the above-discussed functions. The one or more controllers can be implemented in numerous ways, such as with dedicated hardware, or with general purpose hardware (e.g., one or more processors) that is programmed using microcode or software to perform the functions recited above.
It should be appreciated that the various methods outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and/or conventional programming or scripting tools, and also may be compiled as executable machine language code. In this respect, it should be appreciated that one embodiment of the invention is directed to a computer-readable medium or multiple computer-readable media (e.g., a computer memory, one or more floppy disks, compact disks, optical disks, magnetic tapes, etc.) encoded with one or more programs that, when executed, on one or more computers or other processors, perform methods that implement the various embodiments of the invention discussed above. The computer-readable medium or media can be transportable, such that the program or programs stored thereon can be loaded onto one or more different computers or other processors to implement various aspects of the invention as discussed above.
It should be understood that the term “program” is used herein in a generic sense to refer to any type of computer code or set of instructions that can be employed to program a computer or other processor to implement various aspects of the invention as discussed above. Additionally, it should be appreciated that according to one aspect of this embodiment, one or more computer programs that, when executed, perform methods of the invention need not reside on a single computer or processor, but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the invention.
Various aspects of the invention may be used alone, in combination, or in a variety of arrangements not specifically discussed in the embodiments described in the foregoing, and the aspects of the invention described herein are not limited in their application to the details and arrangements of components set forth in the foregoing description or illustrated in the drawings. The aspects of the invention are capable of other embodiments and of being practiced or of being carried out in various ways. Various aspects of the invention may be implemented in connection with any type MR imaging equipment of any configuration. No limitations are placed on scanner implementation. Accordingly, the foregoing description and drawings are by way of example only.
Having thus described several aspects of at least one embodiment of this invention, it is to be appreciated various alterations, modifications, and improvements will readily occur to those skilled in the art. Such alterations, modifications, and improvements are intended to be part of this disclosure, and are intended to be within the spirit and scope of the invention. Accordingly, the foregoing description and drawings are by way of example only.
Use of ordinal terms such as “first”, “second”, “third”, etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed, but are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term) to distinguish the claim elements.
Also, the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” or “having,” “containing”, “involving”, and variations thereof herein, is meant to encompass the items listed thereafter and equivalents thereof as well as additional items.
This application claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Application 60/930,774, filed May 18, 2007, entitled “NOISE REDUCTION SYSTEM AND METHODS FOR MAGNETIC RESONANCE IMAGING,” which is herein incorporated by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 60930774 | May 2007 | US |
