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Accelerated parallel magnetic resonance imaging with multi-channel chaotic compressed sensing


The 2010 International Conference on Advanced Technologies for Communications

Accelerated Parallel Magnetic Resonance Imaging with Multi-Channel Chaotic Compressed Sensing
Tran Due Tant, Dinh Van Phongt, Truong Minh Chinh:l:, and Nguyen Linh-Trungt t University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam :I: College of Education, Hue University, Hue, Vietnam

Abstract- Fast acquisition in magnetic resonance imaging
(MRI) is considered in this paper. Often, fast acquisition is achieved using parallel imaging (pMRI) techniques. It has been shown recently that the combination of pMRI and compressed compressible signals from a small number of random measure? ments, can accelerate the speed of MRI acquisition because the number of measurements are much smaller than that by pMRI per se. Also recently in sensing

(CS),

which enables exact reconstruction of sparse or

chaotic measurements. This chaotic
combine chaotic

CS, chaos filters were designed to obtain CS approach potentially

offers simpler hardware implementation. In this paper, we

CS

and pMRI. However, instead of using

chaos filters, the measurements are obtained by chaotically undersampling the k-space. MRI image reconstruction is then performed by using nonlinear conjugate gradient optimization. For pMRI, we use the well-known approach

SENSE - sensitivity

encoding -, which requires an estimation of the sensitivity maps. The performance of the proposed method is analyzed using the point spread function, the transform point spread function, and the reconstruction error measure.

Index terms - fast acquisition, MRI, parallel imaging, SENSE,
compressed sensing, deterministic chaos.

I. IN TRODUCTION AND STATE-OF-THE-ARTS Magnetic Resonance Imaging (MRI), thanks to the phe? nomenon of magnetic resonance of tissue nuclei (e.g., the hydrogen nucleus H) present in the object (e.g., the brain) under imaging, has found various applications in the field of biology, engineering, and material science. In principle, by exciting the object with a time-varying excitation RF pulse, the resonance information of the nuclei can be picked up by a receiving RF coil. We take the simple case of acquisition of a full two-dimensional (2D) digital image of the object (e.g., a brain slice) to explain how the image acquisition is done. During a series of RF excitations each of which encodes the 2D location information of a particular point on the brain slice, the receiving coil receives an analog MRI time signal which contains the resonance information at all encoded locations. The encoded locations are represented in a space called k-space in which the changes of locations during the acquisition time often form a smooth trajectory. A digital MRI signal is then obtained by sampling the time and the k-space. The digital MRI image is then obtained (reconstructed) by applying a reconstruction algorithm on the digital signal to obtain the digital MRI image of the brain slice; for example, we apply the 2D Fourier transform on the digital MRI signal from the k-space to the pixel domain.

Fast image acquisition in MRI is important in order to enhance image contrast and resolution, to avoid physiological effects or scanning time on patients, to overcome physical constraints inherent within the MRI scanner, or to meet timing requirements when imaging dynamic structures or processes. Parallel MRI (pMRI) is an advanced fast imaging technique to reduce the number of samples using multiple coils to simultaneously collect data. Each coil acquires data corresponding to a portion of the imaging object. There exists some redundancy in the acquired data across all the coils. While the acquisition time is inversely proportional to the number of coils, this redundancy can be exploited to reconstruct the final object image. The reconstruction of the image can be done in the image domain, the k-space domain or the k-t-space domain. In the image domain approach, image reconstruction is done by solving a set of linear equations in the image domain. A common technique is SENSE (SENSitivity En? coding) [1] which uses the sensitivity profiles in order to reduce the acquisition time. SENSE-like methods include SPACE-RIP [2] and PILS [3]. SPACE-RIP allows to place arbitrarily RF receiver coils around the object and to use of any combination of k-space lines, while PILS utilizes localized sensitivities of each coil and the process to estimate the sensitivity profiles. The selection the k-space lines in SPACE-RIP can be made to ensure that the frequency? encoded direction is kept unchanged. This allows us to maintain high signal-to-noise ratio (SNR), minimize artifacts of SENSE-like methods, and reduce considerably the com? plexity [4]. The k-space domain approach uses partial data obtained in all the coils to synthesize the full k-space, hence reconstruct the MRI image. In this approach, the SMASH (SiMulta? neous Acquisition of Spatial Harmonics) method [5] uses the sensitivity profile of receiver coils as a complementary encoding function. It is limited to suitable combinations of coil arrangement, slice geometry, and the compressed factor. The GRAPPA (GeneRalized Autocalibrating Partially Parallel Acquisitions) method [6] uses spatial encoding with an RF coil, and a robust auto-calibration procedure to im? prove considerably the reconstruction results and reduce the computational complexity when compared to other SMASH? like methods. Methods in image and k-space domain approaches have limitations such as low SNR and aliasing artifacts for high

978-1-4244-8876-6/101$26.00 ?2010 IEEE

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compressed factors. In the k-t-space domain approach, the k-t SENSE method [7] exploits correlations in both k-space and time. The UNFOLD (UNaliasing by Fourier-encoding the Overlaps Using the temporal Dimension) method [8] encodes the sensitivity into pre-determined frequency bands. k-t SENSE method can be applied to arbitrary k-space trajectories, time-varying coil sensitivities, and various re? construction problems. In signal processing, a recent breakthrough called com? pressed sensing (CS) [9], [10] shows that sparse or com? pressible signals can be recovered from a much less number of samples than that using Nyquist sampling. Reconstruction can be achieved using nonlinear reconstruction algorithms. CS can be viewed as random undersampling. This method is important because many signals of interest, including natural images, diagnostic images, videos, speech and music, are sparse in some appropriate domains of signal representation. In a recent work [11], we designed chaos filters to obtain chaotic measurements in the framework of CS. This ap? proach, may be called chaotic CS, potentially offers simpler hardware implementation. Among various applications of CS, it has recently been shown to be successfully applied to MRI for fast acquisition by Lustig, Donoho & Pauly in [12]. In particular, while the acquisition of the analog MRI signal remains unchanged, the digital MRI signal is obtained by randomly undersampling the k-space. Inspired by this work, further development in the direction of using CS for MRI continues, such as the CG-SENSE [13] of Bilgin et al., k-t SPARSE [14], and k-t FOCUSS [15]. In this paper, we combine chaotic CS and pMRI. In this sense, we can call it multi-channel CS because, in the setting of pMRI, we simultaneously acquire multiple data using chaotic CS. However, instead of using chaos filters, the measurements are obtained by chaotically undersampling the k-space; this is inspired by [12]. The reconstruction is then performed by using nonlinear conjugate gradient optimization; motivated by [13]. For pMRI, we use the well-known approach SENSE- sensitivity encoding-, which requires an estimation of the sensitivity maps. II. BRIEF BACKGROUND
A.

- how to design the measurement system "\}I to obtain the measurement y, and (ii) reconstruction (decoding) - how to faithfully reconstruct x from y. We wish to have M as small as possible and the reconstruction algorithm as efficient as possible. If the sparsity information in x is still fully kept, though hidden, in y, exact reconstruction of s is feasible if we find a way to fully restore this sparsity from y. Thanks to the sparse structure of s, the exact reconstruction of the signal is made possible when a is constructed as an almost orthonor? mal system when restricted to sparse linear combinations and satisfies sufficient conditions called Restricted Isometry Properties (RIPs). A useful indicator for this property is the measure of inco? herence. ? is incoherent with "\}I in the sense that one cannot sparsify the other [16]. One way to ensure the incoherence is to have ? as a random matrix with Gaussian Li.d. elements. Under such a condition, s can be faithfully recovered from y when M is such that cKlog(N/K) < M < N, where c is some constant, using various sparse approximation techniques, for examples, h -optimization based Basis Pursuit (BP) [9] or Orthogonal Matching Pursuit (OMP) [17]. In a recent paper [11], we proposed to use a chaotic measurement matrix ?, which is deterministic, instead of random one. To construct the chaotic measurement matrix ?, generate sampled logistic sequence by a deterministic chaotic system, then create the matrix ? column by column with this sequence. Elements of the logistic sequence are generated by deterministic chaotic system which is so nonlinear, hence becomes random-like. After that, the reconstruction is also performed by the OMP technique. There, the simulated results indicated that the chaotic approach outperformed the random approach in terms of the probability of exact reconstruction. Moreover, using chaotic CS system also inherits a simpler hardware implementation compared to the random one. To generate a sequence of 'random' numbers, we can use a hardware random number generator (HRNG) or a pseudo-random number generator (PRNG). The HRNG is based on a physical phenomena such as electrical noise from a semiconductor diode or resistor or the decay of a radioactive material. Since the PRNG can generate 'random' numbers by feedback shift registers, it is more practical than HRNG. However, a long register is needed to generate a sequence of numbers that approximates the properties of random numbers. Therefore, a large memory and logic circuits are required.
B.

Chaotic compressed sensing N Let x E ]R be the signal of interest and suppose that we know x admits a sparse linear representation which reads N x ?s, where s E ]R is a K-sparse vector (Le., containing N N exactly K nonzero values) and ? E ]R x is called the sparsifying matrix. Suppose also that we measure/sense x M N by a linear system "\}I E ]R X , called the measurement matrix. Then, the measurements are given by y "\}Ix, with M y E ]R . Suppose we want to reconstruct x from y. This is equivalent to reconstructing s from y, since we can write y as, where a "\}I?. A problem of tremendous interest, called compressed sensing (CS), is when M is considerably less than N. The system "\}I or, equivalently a, becomes underdetermined. Thus, CS has two main tasks: (i) measurement (encoding)
= = = =

Parallel Imaging based on SENSE

I) Sensitivity encoding (SENSE): The number of exci? tations, Le. the number of horizontal lines in the k-space trajectory as shown in Fig. 1, determines the total acquisition time. In SENSE, the number of horizontal lines in the trajectory traced by each individual coil is reduced by the number of coils in use. Subsequently, the sensed size of the imaged area is also reduced. The spatial resolution is not changed but aliasing artifacts appear (Fig. 2).

147

ky

Last line

of each coil. These reference images must not contain aliasing artifact and noise. Smoothing and extrapolation of the coil sensitivity can be done to obtain an acceptable sensitivity map. To integrate compressed sensing into SENSE, we consider the k-space full-sampling by discretizing (1) as follows:

sl(kx,ky)
First line Fig. l. k-space of a brain MR image and a full linear sampling trajectory.

Nx-l Ny-l
=

(4) where Nx and Ny are the numbers of pixels along x and y axes of the image. It is obvious that s l(kx , ky) is viewed as the vector x within the compressed sensing setting. Consequently, we acquire a undersampled signal Sl (kx, ky) in the l-th channel by applying the chaotic measurement matrix ? to s l(kx, ky). Since, sl(kx, ky) is viewed as the vector y within the compressed sensing setting. 2) Conjugate Gradient SENSE (CG-SENSE): As by its original version, SENSE can work only when the k-space trajectory is Cartesian, as shown in Fig. 1, rather than other kinds of trajectories. An effective iterative method that can overcome this problem is the CG-SENSE. This method also requires the information of the sensitivity map. The number of k-space samples obtained by undersam? piing is much smaller than that by full-sampling. MRI reconstruction from the k-space samples is performed by Nonlinear Conjugate Gradient (NCG) [12]. The Tikhonov regularization can be given by:

nx=O ny=O

L L

Cl(nx,ny)m(nx,ny)e-j27f(nxkx+nyky)

1

(a)

(b)

Fig. 2. Illustration of (a) non-aliasing and (b) aliasing phenomena, using Nyquist sampling and downsamling [I].

SENSE works in the image domain by removing the aliasing effect caused by combining the individual images, called field-of-view (FOV) images, obtained by individual coils. The inversion of the aliasing transformation for each pixel is calculated individually. Consider the imaging of a slice of the object in the 2D plane {x, y}. Let m(x, y) be this image. Let L be the number of RF coils. Each coil would have individual values of image intensity. The k-space signal obtained from the l-th coil is given by:
s

argm n

?

{ llFum - yll;

+,\ Ilm112

subject to

IlFum - yl12 < E

}

(5)

where m is the image vector, y is the k-space measurement vector, Fu is the undersampled Fourier operator associated with the measurements, and ,\ is a data consistency tuning constant.
C.

l(kx, ky)

=

JJ CI(X, y)m(x, y)e-j27r(xkx+ykY)dxdy,
=

(1)

Multichannel compressed sensing using CG-SENSE

where kx and ky encode the information of location along the x and y directions of the image respectively, and Cl (x, y) is the sensitivity function of the l-th coil. k {kx, ky} lies in the k-space. Equation (1) shows that s l(kx, ky) is the Fourier transform of the sensitivity-weighted images CI(X, y)m(x, y). The im? age acquired by each individual coil, ml(x, y), can then be expressed as the ideal image modulated by the corresponding sensitivity function, by:

ml(x, y)

=

CI(X, y)m(x, y)

(2)

The chaotic measurement matrix is formed in MRI ac? quisition procedure. We generate the values of kx and ky by a logistic map process, and a couple of kx and ky will determine a coordinate in the k-space that will be acquired. However, the distribution of information in k-space concen? trates nearby the origin and decays when kx and ky increase. Fig. 1 shows that most encoded information is concentrated at the origin. Therefore, we convert the distribution of logistic map sequence to Gaussian distribution. The reconstruction is obtained by solving the constrained optimization problem:
argm n

Subsequently, each pixel of the full FOV image can be estimated as:

?

{ llFum - yll;

+,\ II'I1mI11

subject to

m(x, y)
=

=

CH(x, y)C-1(x, y)CH(x, y)m(x, y),

IlFum - yl12 < E

}

(6)

(3)

where C [Cb ... , CLl. In practice, a calibration procedure with the reference images is used to measure the sensitivity

Once the MRI data has been acquired, the reconstruction is performed by the NCG algorithm. Our scheme can be summarized in Algorithm 1.

148

Algorithm 1 Multi-channel Chaos-based CS for MRI acquisition
Step 1: Generate kx, ky that are Gaussian chaotic sequences.
The number of kx, ky based on pre-defined compression ratio

r=M/N.

Step 2: For each channel, determine coordinates in k-space based Step 3: For each channel, acquire digital data in k-space based Step 4: Estimate sensitivity maps using polynomial fitting.
on the mask and store them in a vector y. on k"" ky and store as a mask.

Step 5: Perform SENSE reconstruction using conjugated gradient
method.

III. RESULTS AND PERFORMANCE the simulation, the data source in use, obtained [18], is human MPRAGE data from 8-channel head coil. The data was acquired with the following param? TE = 3.45 ms, TR = 2350 ms, TI = 1100 ms, Flip angle = 7 deg., slice = 1, matrix = 128 x 128, slice thickness = 1.33 mm, FOV= 256 mm. To compare the efficiency of the design of chaotic mea? surements, we acquire the data for a series of compression ratios by measurements which are both chaotic and random. Then, we analyze the performance of these systems using the point spread function, the transform point spread function, and the reconstructed error. To measure the degree of incoherence of a sparsity system, [12] proposed to use the point spread function (PSF), defined as below: (7) is the i-th natural basis vector (having a value of where 1 at the ith location and zeros elsewhere). If the k-space is fully sampled, then PSF(i,j)ki'?j 0 in the image domain, meaning the system is incoherent. Fig. 3 shows the PSFs which correspond to k-space full-sampling, chaotic undersampling and random undersam? piing, respectively, with low and high compression ratios. With a low compression ratio, r 0.05, the interference between pixels is evident in the both cases of chaotic and random measurements. With a high compression ratio, r 0.1, the incoherence at the both cases of chaotic and random measurements is small enough. Consequently, the acquisition produced good reconstructed images. It also can be seen that chaotic k-space undersampling and random k-space undersampling have similar degree of incoherence. If the incoherence is analyzed in the transform domain of sparsity, such as the wavelet domain relevant to MRi images, the transform point spread function (TPSF) is used [12], as given by the following equation:
= = =

In from array eters:

Fig. 4 shows the wavelet TPSFs which correspond to the k-space full-sampling, chaotic undersampling and ran? dom undersampling, with low and high compression ratios. The sparsifying transform here is the I-level Daubechies-1 wavelet transform. With a low compression ratio, r 0.05, our analysis indicated that the interference is spread in all subbands. Whereas, the interference is quite small when a high compression ratio, r 0.1, was applied. Fig. 5 shows the reconstructed images for the k-space full-sampling, in comparison with chaotic and random un? dersampling for several different compression ratios. We can see that the image reconstructed from chaotic measurements is equivalent to random ones. Then, we determine, for each compression ratio, the error in the reconstructed image as compared to the original image. Suppose that is an N x M original image and is the reconstructed image. We define the error between them by:
= =

i

I

e

1
=

N

x

M

?= L IIij - iij I·
t=13=1

N M

(9)

Fig. 6 shows the results of this comparison. We can see that, for compression ratios that are larger than 0.1, the image reconstructed from chaotic measurements has smaller average error than the image reconstructed from random measurements. Our results confirm the success of replacing random measurements by chaotic measurements. IV. C ONCLUSION We have successfully combined chaos-based compressed sensing and the CG-SENSE technique in order to accelerate the speed of acquisition in parallel MRl imaging, hence improve the scanning time as well as reduce the hardware complexity by the deterministic approach, while ensuring the quality of reconstructed image. Both of these methods can exploit the information of the coil sensitivity map and the sparsity of the image. The simulation on the chosen MRl image shows that the system is potential for a practical implementation. Subsequent work could further combine multi-channel chaotic compressed sensing k-t space approach such as k-t SENSE in order to improve the quality of the reconstructed image.
ACKNOW LEDGEMENT

ei

This work is supported by the QG-1O.40 project of Viet? nam National University, Hanoi.
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TPSF(i, j)

=

ej

'ItF: Fu 'It *

ej lih

(8)

It can be used to measure how a single transform coeffi? cient affect to other transform coefficients of the measured object. For incoherence, we want to have TPSF( i, j) as small as possible.

149

(a)

(b)

(c)

(d)

(e)
r =

Fig. 3. PSFs in the image domain for full-sampling (a), chaotic undersampling (b) and random undersampling (c) with the compression ratio (b), chaotic undersampling (d) and random undersampling (e) with r = 0.1

0.05

(a)

(b)

(c)

(d)

(e)
r =

Fig. 4. TPSFs in the wavelet domain for full-sampling (a), chaotic undersampling (b) and random undersampling (c) with the compression ratio (b), chaotic undersampling (d) and random undersampling (e) with r = 0.1

0.05

(a)

(b)

(c)

(d)

(e)
r =

Fig. 5. Reconstructed images for different compression ratios: full-sampling (a), chaotic undersampling (b) and random undersampling (c) with (b), chaotic undersampling (d) and random undersampling (e) with r = 0.2

0.17

150

0.6 0.55 0.5 0.45 ? 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0 0.05 0.1 0.15

_random

, , ,0 , chaos
'

en

?

0.2

0.25

Ratio
Fig. 6. Performance comparison between chaotic and random approach.

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