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Green’s function estimation in speckle using the decomposition of the time reversal operator: Application to aberration correction in medical imaging
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10.1121/1.2816562
/content/asa/journal/jasa/123/2/10.1121/1.2816562
http://aip.metastore.ingenta.com/content/asa/journal/jasa/123/2/10.1121/1.2816562
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Figures

Image of FIG. 1.
FIG. 1.

(Color online) Acquisition of the transfer matrix in the FDORT method. The focused transmit insonify the medium. The signal received by array element is time gated to select the signal from depth to , and its Fourier coefficient at frequence gives the matrix coefficient . In speckle is taken to be about the pulse length.

Image of FIG. 2.
FIG. 2.

(a) A transmit focusing at a point and the corresponding received wave front. In speckle, ideally, to estimate the Green function at point , we would like to fire several times the same transmit focusing at , but with a different speckle distribution each time, to provide a good averaging of the randomness. (b) Instead, we use a neighbor beam focusing at close to (the distance between and is exaggerated here). (c) By properly shifting the wave front of the received signal, it looks like the whole medium is virtually translated and ’, the new position of , corresponds to . Thus a new realization of the signal coming from is obtained, with a different scatterer distribution. By virtually translating the phantom by a different distance, several realizations are obtained. This is valid only if the aberration seen by is the same as the aberration seen by .

Image of FIG. 3.
FIG. 3.

(Color online) Theoretical amplitude of the spatial correlation matrix (a) and projection on the antidiagonal (b) in homogeneous media as predicted by the VanCittert–Zernike theorem. The triangular shape that arises from the Fourier transform of is easily identifiable. sixtyfour of the 128 elements were used in transmission, which is why the triangle base is 64 elements wide. (c), (d) Experimental amplitude of the FDORT matrix, for a focus. The differences observed for the edge elements are mainly due to the transducers directivity, which was not taken into account in the VanCittert–Zernike prediction.

Image of FIG. 4.
FIG. 4.

In speckle, the standard deviation of the estimation is inversely proportional to the square root of the number of realizations used in the estimation. However, the number of lateral realizations is limited by the size of the isoplanatic patch, which is the size of the region where all the points see the same aberration, and thus where the Green functions are similar enough to be averaged. Therefore, to increase the number of realizations, additional windows can be selected using the depth dimension.

Image of FIG. 5.
FIG. 5.

(Color online) Standard deviation of the phase in function of the lateral size of the speckle region. In the first numerical experiment (solid blue plot), the transmits are very close to each other while in a second one (red cross), the transmits used in the estimation were separated by the beam width . Taking more transmits than required by the condition to have independent realizations does not improve the estimation.

Image of FIG. 6.
FIG. 6.

The phase of the estimated Green functions is unwrapped and the geometrical delay removed to show an estimate of the aberrator delay profile. The estimate (dash line) is compared with the true profile (solid line), after the first iteration (a) and after five iterations (b). The estimate is not very good at the first iteration but converges after a few iterations.

Image of FIG. 7.
FIG. 7.

(Color online) Simulated transmit fields at in presence of the near field phase aberrator for a different number of iterations of the algorithm. (a) Initial transmit: it is based on the Green function in homogeneous medium, and therefore the focusing is very poor. It is the equivalent virtual object for the first FDORT iteration (b) The first eigenvector obtained in the first iteration is back-propagated. It focuses mainly on the brightest spot of the first transmit, but with significant interferences. The focusing criterion is only 0.3. It is used to correct the transmit for the second iteration of FDORT. (c) First eigenvector from the second iteration. The focusing has improved. It is used to correct the transmit for the third iteration. (d) Fifth iteration: the first eigenvector now yields a very good focusing. It is an accurate estimate of the Green function in presence of the phase aberrator. is now equal to 0.7.

Image of FIG. 8.
FIG. 8.

Evolution of the normalized first eigenvalue in speckle with a simulated near-field phase screen. The normalized first eigenvalue is equal to the focusing criterion . At the first iteration, is about 0.3, which is the sign of a bad focusing related to the phase screen. After a few iterations, the algorithm converges and passes 0.7, which means that the focusing is excellent. The corresponding transmit fields are displayed in Fig. 7.

Image of FIG. 9.
FIG. 9.

(Color online) Simulated transmit fields at in the presence of the far field phase aberrator (drawn on each transmit in white) located at depth, for a different number of iteration of the algorithm. (a) Initial transmit: it is based on the Green function in homogeneous medium, and therefore the focusing is very poor. It is the equivalent virtual object for the first FDORT iteration (b) Field obtained after back-propagation of the first eigenvector obtained in the first iteration. It focuses mainly on the brightest spot of the first transmit, but with interferences. is 0.35. (c) First eigenvector after the fourth iteration. is 0.7. (d) Evolution of the normalized eigenvalue, or factor. Even if the screen is not close to the array, the convergence is reached quickly.

Image of FIG. 10.
FIG. 10.

(Color online) Image of the phantom with a rubber aberrator before (left) and after correction (right) using the FDORT method to improve the focusing in transmit and receive.

Image of FIG. 11.
FIG. 11.

(Color online) Setup for the simulation: the goal is to estimate the Green’s function of point in speckle (dash blue wave front) using focused transmit, in the presence of a strong interferer signal (solid red wave front). The signal from point travels through a heterogeneity, but the interferer does not. In order to estimate the parameters of the heterogeneity it is necessary to isolate the wave front of from the interferers. In the simulation, the interferer is 1000 times stronger than the signal.

Image of FIG. 12.
FIG. 12.

(Color online) (Top) Phase for the first and second eigenvector of FDORT, corresponding to the setup of Fig. 11. The first eigenvector (left) corresponds clearly to the interference, while the second corresponds to the speckle, and carries information on the heterogeneity. For clarity, a geometrical delay has been removed from the second eigenvector phase. (Bottom) Fields after numerical backpropagation of the first (left) and second eigenvectors. This confirms that the first eigenvector corresponds to the interference, while the second eigenvector corresponds to the desired speckle signal. The eigenvector corresponding to the speckle is a very good estimate of the Green’s function and leads to a good focusing through the heterogeneity despite the presence of a strong interfering signal.

Image of FIG. 13.
FIG. 13.

(Color online) Phase estimate obtained with the 1-lag cross-correlation method. The estimate is completely dominated by the interference, and it is not possible to extract any information about the heterogeneity.

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/content/asa/journal/jasa/123/2/10.1121/1.2816562
2008-02-01
2014-04-18
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Green’s function estimation in speckle using the decomposition of the time reversal operator: Application to aberration correction in medical imaging
http://aip.metastore.ingenta.com/content/asa/journal/jasa/123/2/10.1121/1.2816562
10.1121/1.2816562
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